ORIGINAL_ARTICLE
A flexible cell scheduling problem with automated guided vehicles and robots under energy-conscious policy
A flexible cell scheduling problem (CSP) under time-of-use (TOU) electricity tariffs are developed in this study. To apply a kind of energy-conscious policy, over cost of on-peak period electricity consumption, limitations on total energy consumption by all facilities, set up time available on each cell, part defect (pert) percentage and the total number of automated guided vehicles (AGV) are considered. Additionally, an ant colony optimization (ACO) algorithm is employed to find a near-optimum solution of proposed mixed integer linear programming (MILP) model with the objective of minimizing the total cost of CSP model. Since no benchmark is available in the literature, a lower bound is implemented as well to validate the result achieved. Moreover, to improve the quality of the results obtained by meta-heuristic algorithms, two hybrid algorithms (HGA and HACO) was proposed to solve the model. For parameter tuning of algorithms, Taguchi experimental design method is applied. Then, numerical examples are presented to prove the application of the proposed methodology. Our results compared with the lower bound and as a result it confirmed that HACO was capable to find better and nearer optimal solutions.
http://scientiairanica.sharif.edu/article_4399_1221f23e396b45db6c5700120db4f480.pdf
2018-02-01
339
358
10.24200/sci.2017.4399
Cell-scheduling
Automated guided vehicles (AGV)
Robots
Energy-conscious policy
Ant colony optimization (ACO)
genetic algorithm (GA)
Taguchi experimental design method
Mohammad
Hemmati Far
m.hemmatifar@gmail.com
1
Department of Industrial Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
Hassan
Haleh
hhaleh@gut.ac.ir
2
Department of Industrial & Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran
AUTHOR
Abbas
Saghaei
a.saghaei@srbiau.ac.ir
3
Department of Industrial Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran
AUTHOR
References
1
1. Wemmerlov, U. and Hyer, N.L. Cellular manufacturing in the U.S. industry: a survey of users", International Journal of Production Research, 27(9),
2
pp. 1511-1530 (1989).
3
2. Suer, G.A. An algorithm to nd the number of parallel stations for optimal cell scheduling", Computers &
4
Industrial Engineering, 23(1-4), pp. 81-84 (1992).
5
3. Selim, H.M., Askin, R.G., and Vakharia, A.J. Cell
6
formation in group technology: Review, evaluation, and directions for future research", Computers &
7
Industrial Engineering, 34(1), pp. 3-20 (1998).
8
4. Yu, J.J., Sun, S.D., Si, S.B., Yang, H.G., and Wu, X.L.
9
A study on the aviation manufacture cell scheduling
10
based on adaptive ant colony algorithm", Materials
11
Science Forum, 532-533, pp. 1060-1063 (2007).
12
5. Saravanan, M. and Haq, A.N. A scatter search
13
method to minimize make-span of cell scheduling
14
problem", International Journal of Agile Systems and
15
Management, 3(1-2), pp. 18-36 (2008).
16
6. Li, D.N., Wang, Y., and Xiao, G.X. Dynamic parts
17
scheduling in multiple job shop cells considering intercell
18
exible routes", Computers & Operations
19
Research, 40(5), pp. 2007-2023 (2013).
20
7. Tang, J.F., Wang, X., Kaku, I., and Yung, K.L.
21
Optimization of parts scheduling in multiple cells considering
22
intercell move using scatter search approach",
23
Journal of Intelligent Manufacturing, 21(4), pp. 525-
24
537 (2010).
25
8. Schaller, J. A comparison of heuristics for family
26
and job scheduling in a
27
ow-line manufacturing cell",
28
International Journal of Production Research, 38(2),
29
pp. 287-308 (2000).
30
9. Hendizadeh, H., Faramarzi, H., Mansouri, S.A.,
31
Gupta, J.N.D., and Elmekkawy, T.Y. Meta-heuristics
32
for scheduling a
33
ow shop manufacturing cell with
34
sequence dependent family setup times", International
35
Journal of Production Economics, 111(2), pp. 593-605
36
10. Lin, S.W., Ying, K.C., Lu, C.C., and Gupta, J.N.D.
37
Applying multi-start simulated annealing to schedule
38
ow line manufacturing cell with sequence dependent
39
family setup times", International Journal of Production
40
Economics, 130(2), pp. 246-254 (2011).
41
11. Nagarjuna, N., Mahesh, O., and Rajagopal, K. A
42
heuristic based on multi-stage programming approach
43
for machine loading problem in a
44
exible manufacturing
45
system", Robotics and Computer Integrated
46
Manufacturing, 22, pp. 342-352 (2006).
47
12. Saravanan, M. and Noorul Haq, A.N. Evaluation of
48
scatter-search approach for scheduling optimization of
49
exible manufacturing systems", International Journal
50
of Advanced Manufacturing Technology, 38(5), pp.
51
978-986 (2008).
52
13. Blazevicz, J., Eiselt, H.A., Finke, G., Laporte, G., and
53
Weglarz, J. Scheduling tasks and vehicles in a
54
manufacturing system", International Journal of
55
Flexible Manufacturing System, 4(1), pp. 5-16 (1991).
56
14. Tanchoco, J.M.A. and Sinriech, D. OSL-optimal
57
single-loop guide paths for AGVS", International Journal
58
of Production Research, 30(3), pp. 665-681 (1992).
59
15. Sinriech, D. and Tanchoco, J.M.A. The centroid
60
projection method for locating pick-up and delivery
61
stations in single-loop AGV systems", Journal of
62
Manufacturing Systems, 11(4), pp. 297-307 (1992).
63
16. Lengerke, O., Campos, A.M.V., Dutra, M.S., and
64
Pinto, F.D.N.C. Trajectories and simulation model
65
of AGVs with trailers", ABCM Symposium Series in
66
Mechatronics, 4, pp. 509-518 (2010).
67
17. The Cadmus Group, "Regional lectricity emission
68
factors nal report" (1998).
69
18. Ulusoy, G., Sivrikaya-Serifoglus, F., and Bilge, U.
70
A genetic algorithm approach to the simultaneous
71
scheduling of machines and automated guided vehicles",
72
Computers & Operations Research, 24(4), pp.
73
335-351 (1997).
74
19. Ali A. Pouyan, Heydar Toossian Shandiz, Soheil
75
Arastehfar Synthesis a Petri net based control model
76
for a FMS cell", Computers in Industry, 62, pp. 501-
77
508 (2011).
78
20. Pach, C., Bekrar, A., Zbib, N., Sallez, Y. and Trentesaux,
79
D. An eective potential eld approach to FMS
80
holonic heterarchical control", Control Engineering
81
Practice, 20, pp. 1293-1309 (2012).
82
21. Leit~ao, P. and Restivo, F. ADACOR: A holonic architecture
83
for agile and adaptive manufacturing control",
84
Computers in Industry, 57(2), pp. 121-130 (2006).
85
22. Abazari, A.M., Solimanpur, M., and Sattari, H. Optimum
86
loading of machines in a
87
exible manufacturing
88
356 M. Hemmati Far et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 339{358
89
system using a mixed-integer linear mathematical programming
90
model and genetic algorithm", Computers
91
& Industrial Engineering, 62, pp. 469-478 (2012).
92
23. Pach, C., Berger, T., Bonte, T., and Trentesaux, D.
93
ORCA-FMS: a dynamic architecture for the optimized
94
and reactive control of
95
exible manufacturing
96
scheduling", Computers in Industry, 65(4), pp. 706-
97
720 (2014).
98
24. Balaji, A.N. and Porselvi, S. Articial immune system
99
algorithm and simulated annealing algorithm for
100
scheduling batches of parts based on job availability
101
model in a multi-cell
102
exible manufacturing system",
103
Procedia Engineering, 97, pp. 1524-1533 (2014).
104
25. Erdin, E.M. and Atmaca, A. Implementation of an
105
overall design of a
106
exible manufacturing system",
107
Procedia Technology, 19, pp. 185-192 (2015).
108
26. He, Y., Stecke, K., and Smith, M. Robot and machine
109
scheduling with state-dependent part input sequencing
110
exible manufacturing systems", International
111
Journal of Production Research (2016).
112
DOI: 10.1080/00207543.2016.1161252
113
27. Solimanpur, M., Vrat, P., and Shankar, R. A heuristic
114
to minimize make-span of cell scheduling problem",
115
International Journal of Production Economics, 88(3),
116
pp. 231-241 (2004).
117
28. Logendran, R., Mai, L., and Talkington, D. Combined
118
heuristics for bi-level group scheduling problems",
119
International Journal of Production Economics, 38,
120
pp. 133-145 (1995).
121
29. Venkataramanaiah, S. Scheduling in cellular manufacturing
122
systems: a heuristic approach", International
123
Journal of Production Research, 46(2), pp. 429-449
124
30. Tavakkoli-Moghaddam, R., Javadian, N., Khorrami,
125
A., and Gholipour-Kanani, Y. Design of a scatter
126
search method for a novel multi-criteria group scheduling
127
problem in a cellular manufacturing system",
128
Expert Systems with Applications, 37, pp. 2661-2669
129
31. Lin, S.W., Ying, K.C., and Lee, Z.J. Meta-heuristics
130
for scheduling a non- permutation
131
ow line manufacturing
132
cell with sequence dependent family setup
133
times", Computers and Operations Research, 36, pp.
134
1110-1121 (2009).
135
32. Shirazi, B., Fazlollahtabar, H., and Mahdavi, I. A six
136
sigma based multi-objective optimization for machine
137
grouping control in
138
exible cellular manufacturing
139
systems with guide-path
140
exibility", Advances in Engineering
141
Software, 41, pp. 865-873 (2010).
142
33. Saadettin Erhan Kesen, Sanchoy K. Das, Zulal Gungor
143
A genetic algorithm based heuristic for scheduling of
144
virtual manufacturing cells (VMCs)", Computers &
145
Operations Research, 37, pp. 1148-1156 (2010).
146
34. Lin, S.W., Ying, K.C., Lu, C.C., and Gupta, J.N.D.
147
Applying multi-start simulated annealing to schedule
148
ow line manufacturing cell with sequence dependent
149
family setup times", International Journal of Production
150
Economics, 130(2), pp. 246-254 (2011).
151
35. Kia, R., Baboli, A., Javadian, N., Tavakkoli-
152
Moghaddam, R., Kazemi, M., and Khorrami, J.
153
Solving a group layout design model of a dynamic
154
cellular manufacturing system with alternative process
155
routings, lot splitting and
156
exible reconguration by
157
simulated annealing", Computers & Operations Research,
158
39, pp. 2642-2658 (2012).
159
36. Batur, G.D., Karasan, O.E., and Akturk, M.S. Multiple
160
part-type scheduling in
161
exible robotic cells", International
162
Journal of Production Economics, 135(2),
163
pp. 726-740 (2012).
164
37. Izui, K., Murakumo, Y., Suemitsu, I., Nishiwaki, S.,
165
Noda, A., and Nagatani, T. Multiobjective layout optimization
166
of robotic cellular manufacturing Systems",
167
Computers & Industrial Engineering, 64, pp. 537-544
168
38. Boutsinas, B. Machine-part cell formation using biclustering",
169
European Journal of Operational Research,
170
230, pp. 563-572 (2013).
171
39. Fazlollahtabar, H. and Jalali Naini, S.G. Adapted
172
Markovian model to control reliability assessment in
173
multiple AGV manufacturing system", Journal of
174
Scientia Iranica, 20(6), pp. 2224-2237 (2013).
175
40. Yan, C.Z.J.T.C. Job-shop cell-scheduling problem
176
with inter-cell moves and automated guided vehicles",
177
Journal of Intelligent Manufacturing, 26, pp. 845-859
178
41. Forghani, K. and Mohammadi, M. A genetic algorithm
179
for solving integrated cell formation and layout
180
problem considering alternative routings and machine
181
capacities", Journal of Scientia Iranica, 21(6), pp.
182
2326-2346 (2014).
183
42. Zhang, H., Zhao, F., Fang, K., and Sutherland, J.
184
Energy-conscious
185
ow shop scheduling under time-ofuse
186
electricity taris", CIRP Annals - Manufacturing
187
Technology, 63, pp. 37-40 (2014).
188
43. Li, Y., Li, X., and Gupta, J. Solving the multiobjective
189
ow line manufacturing cell scheduling problem
190
by hybrid harmony search", Expert Systems with
191
Applications, 42, pp. 1409-1417 (2015).
192
44. Zohrevand, A.M., Raei, H., and Zohrevand, A.H.
193
Multi-objective dynamic cell formation problem: A
194
stochastic programming approach", Journal of Computers
195
& Industrial Engineering, 98, pp. 323-332
196
45. Majumder, A. and Laha, D. A new cuckoo search
197
algorithm for 2-machine robotic cell scheduling problem
198
with sequence-dependent setup times", Journal of
199
Swarm and Evolutionary Computation, 28, pp. 131-
200
143 (2016).
201
46. Gultekin, H., Akturk, M.S., and Karasan, O.E. Bicriteria
202
robotic operation allocation in a
203
exible manufacturing
204
cell", Computers & Operations Research, 37,
205
pp. 779-789 (2010).
206
47. Tuysuz, F. and Kahraman, C. Modeling a
207
manufacturing cell using stochastic Petri nets with
208
fuzzy parameters", Expert Systems with Applications,
209
37, pp. 3910-3920 (2010).
210
M. Hemmati Far et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 339{358 357
211
48. Naderi, B. and Azab, A. Modeling and scheduling
212
exible manufacturing cell with parallel processing
213
capability", CIRP Journal of Manufacturing Science
214
and Technology, 11, pp. 18-27 (2015).
215
49. Yang, Y., Chen, Y., and Long, C. Flexible robotic
216
manufacturing cell scheduling problem with multiple
217
robots", International Journal of Production Research,
218
pp. 1-14 (2016)
219
50. Logendran, R., Carson, S., and Hanson, E. Group
220
scheduling in
221
ow shops", International Journal
222
of Production Economics, 96, pp. 143-155 (2004).
223
51. Logendran, R., de Szoeke, P., and Barnard, F.
224
Sequence-dependent group scheduling problems in
225
ow shops", International Journal of Production
226
Economics, 102, pp. 66-86 (2005).
227
52. Salmasi, N., Logendran, R., and Skandari, M. Total
228
ow time minimization in a
229
ow shop sequencedependent
230
group scheduling problem", Computers and
231
Operations Research, 37, pp. 199-212 (2010).
232
53. Bruzzone, A.A.G., Anghinol, D., Paolucci, M., and
233
Tonelli, F. Energy-aware scheduling for improving
234
manufacturing process sustainability: A mathematical
235
ow shops", CIRP Annals - Manufacturing
236
Technology, 61, pp. 459-462 (2012).
237
54. Mozdgir, A., Fatemi Ghomi, S.M.T., Jolai, F., and
238
Navaei, J. Three meta-heuristics to solve the nowait
239
two-stage assembly
240
ow-shop scheduling problem",
241
Journal of Scientia Iranica, 20(6), pp. 2275-2283
242
55. Jolai, F. and Abedinnia, H. Consideration of transportation
243
lags in a two-machine Flow shop scheduling
244
problem", Journal of Scientia Iranica, 20(6), pp. 2215-
245
2223 (2013).
246
56. Jolai, F., Tavakkoli-Moghaddam, R., Rabiee, M., and
247
Gheisariha, E. An enhanced invasive weed optimization
248
for make-span minimization in a
249
shop scheduling problem", Journal of Scientia Iranica,
250
21(3), pp. 1007-1020 (2014).
251
57. Seidgar, H., Zandieh, M., and Mahdavi, I. Biobjective
252
optimization for integrating production and
253
preventive maintenance scheduling in two-stage assembly
254
ow shop problem", Journal of Industrial and
255
Production Engineering, 33(6), pp. 404-425 (2016).
256
58. Fang, K., Uhan, N., Zhao, F., and Sutherland, J.
257
A new approach to scheduling in manufacturing for
258
power consumption and carbon footprint reduction",
259
Journal of Manufacturing Systems, 30, pp. 234-240
260
59. Dai, M., Tang, D., Giret, A., Salido, M., and Li,
261
W.D. Energy-ecient scheduling for a
262
shop using an improved genetic-simulated annealing
263
algorithm", Robotics and Computer-Integrated Manufacturing,
264
29, pp. 418-429 (2013).
265
60. Zhang, R., Chang, P.C., and Wu, C. A hybrid
266
genetic algorithm for the job shop scheduling problem
267
with practical considerations for manufacturing costs:
268
Investigations motivated by vehicle production", International
269
Journal of Production Economics, 145(1),
270
pp. 38-52 (2013).
271
61. Nourali, S. and Imanipour, N. A particle swarm
272
optimization-based algorithm for
273
exible assembly job
274
shop scheduling problem with sequence dependent
275
setup times", Journal of Scientia Iranica, 21(3), pp.
276
1021-1033 (2014).
277
62. Yazdani, M., Zandieh, M., Tavakkoli-Moghaddam, R.,
278
and Jolai, F. Two meta-heuristic algorithms for the
279
dual-resource constrained
280
exible job-shop scheduling
281
problem", Journal of Scientia Iranica, 22(3), pp. 1242-
282
1257 (2015).
283
63. Zhang, J., Yang, J., and Zhou, Y. Robust scheduling
284
for multi-objective
285
exible job shop Problems with
286
exible workdays", Journal of Engineering Optimization,
287
48(11), pp. 1973-1989 (2016).
288
64. Hamta, N., Fatemi Ghomi, S.M.T., Tavakkoli-
289
Moghaddam, R., and Jolai, F. A hybrid metaheuristic
290
for balancing and scheduling assembly lines
291
with sequence-independent setup times by considering
292
deterioration tasks and learning eect", Journal of
293
Scientia Iranica, 21(3), pp. 963-979 (2014).
294
65. Khalili, S., Mohammadzade, H., and Fallahnezhad,
295
M.S. A new approach based on queuing theory for
296
solving the assembly line balancing problem using
297
fuzzy prioritization techniques", Journal of Scientia
298
Iranica, 23(1), pp. 387-398 (2016).
299
66. Kumar, A., Prakash, Tiwari, M.K., Shankar, R., and
300
Baveja, A. Solving machine-loading problem of a
301
exible manufacturing system with constraint-based
302
genetic algorithm", European Journal of Operational
303
Research, 175, pp. 1043-1069 (2006).
304
67. Mukhopadhyay, S.K., Maiti, B., and Garg, S. Heuristic
305
solution to the scheduling problem in
306
exible manufacturing
307
system", International Journal of Production
308
Research, 29, pp. 2003-2024 (1991).
309
68. Mukhopadhyay, S.K., Midha, S., and Krishna, V.M.
310
A heuristic procedure for loading problems in
311
manufacturing systems", International Journal of
312
Production Research, 30, pp. 2213-2228 (1992).
313
69. Mukhopadhyay, S.K., Singh, M.K., and Srivastava,
314
R. FMS loading: a simulated annealing approach",
315
International Journal of Production Research, 36, pp.
316
1629-1647 (1998).
317
70. Moreno, A.A. and Ding, F.Y. Heuristic for the FMS
318
loading and part type selection problems", International
319
Journal of Flexible Manufacturing Systems,
320
5(1), pp. 287-300 (1993).
321
71. Shanker, K. and Srinivasulu, A. Some solution
322
methodologies for loading problems in
323
exible manufacturing
324
system", International Journal of Production
325
Research, 27(6), pp. 1019-1034 (1989).
326
72. Shankar, K. and Tzen, Y.J. A loading and dispatching
327
problem in a random
328
exible manufacturing system",
329
International Journal of Production Research, 16, pp.
330
383-393 (1985).
331
358 M. Hemmati Far et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 339{358
332
73. Diabat, A. Hybrid algorithm for a vendor managed
333
inventory system in a two-echelon supply chain", European
334
Journal of Operational Research, 238, pp. 114-
335
121 (2014).
336
74. Sadeghi, J., Mousavi, S.M., Niaki, S.T.A., and
337
Sadeghi, S. Optimizing a multi-vendor multi-retailer
338
vendor managed inventory problem: Two tuned metaheuristic
339
algorithms", Knowledge-Based Systems, 50,
340
pp. 159-170 (2013).
341
75. Chen, G., Govindan, K., and Yang, Z. A method
342
to reduce truck queuing at terminal gates: managing
343
truck arrivals with vessel-dependent time windows",
344
International Journal of Production Economic,
345
141(1), pp. 179-188 (2013).
346
76. Sue-Ann, G., Ponnambalam, S.G., and Jawahar, N.
347
Evolutionary algorithms for optimal operating parameters
348
of vendor managed inventory systems in
349
a two-echelon supply chain", Advances Engineering.
350
Software, 52, pp. 47-54 (2012).
351
77. Pasandideh, S.H.R. and Niaki, S.T.A. A genetic
352
algorithm approach to optimize a multi-products EPQ
353
model with discrete delivery orders and constrained
354
space", Applied Mathematics and Computation, 195,
355
pp. 506-514 (2008).
356
78. Taleizadeh, A.A., Niaki, S.T.A., and Makui, A. Multiproduct
357
multiple-buyer single-vendor supply chain
358
problem with stochastic demand, variable lead-time,
359
and multi-chance constraint", Expert Systems with
360
Applications, 39, pp. 5338-5348 (2012).
361
79. Colorni, A., Dorigo, M., and Maniezzo, V. Distributed
362
optimization by ant colonies", Proceedings of
363
European Conference on Articial Life, Paris, France,
364
pp. 134-142 (1991).
365
80. Colorni, A., Dorigo, M., and Maniezzo, V. An investigation
366
of some properties of an ant algorithm", Proceedings
367
of the Parallel Problem Solving from Nature
368
Conference, Brussels, Belgium, pp. 509-520 (1992).
369
81. Colorni, A., Dorigo, M., and Maniezzo, V. Ant
370
system for job-shop scheduling", Belgian Journal of
371
Operations Research, Statistics and Computer Science,
372
34, pp. 39-53 (1994).
373
82. Yang, W.H. and Tarng, Y.S. Design optimization
374
of cutting parameters for turning operations based
375
on Taguchi method", Journal of Materials Processing
376
Technology, 84, pp. 122-129 (1998).
377
83. Davidson, M.J., Balasubramanian, K., and Tagore,
378
G. Experimental investigation on
379
ow-forming of
380
AA6061 alloy a Taguchi approach", Journal of Materials
381
Processing Technology, 200, pp. 283-287 (2008).
382
84. Du Plessis, B.J. and De Villiers, G.H. The application
383
of the Taguchi method in the evaluation of mechanical
384
otation in waste activated sludge thickening", Resources,
385
Conservation and Recycling, 50, pp. 202-210
386
85. Taguchi, G., Chowdhury, S., and Wu, Y., Taguchi's
387
Quality Engineering Handbook, Hoboken, NJ: Wiley.
388
Talbi, El-Ghazali, Meta-heuristics, New York: Wiley
389
86. Khaw, J.F.C., Lim, B.S., and Lim, L.E.N. Optimal
390
design of neural networks using the Taguchi method",
391
Neurocomputing, 7, pp. 225-245 (1995).
392
87. Wu, Y. and Wu, A. Taguchi Methods for Robust Design,
393
New York: The American Society of Mechanical
394
Engineers (2000).
395
88. Cardenas-Barron, L.E., Trevino-Garza, G., and Wee,
396
H.M. A simple and better algorithm to solve the
397
vendor managed inventory control system of multiproduct
398
multi-constraint economic order quantity
399
model", Expert Systems with Applications, 39, pp.
400
3888-3895 (2012).
401
ORIGINAL_ARTICLE
RENEWABLE POLICIES AND CHALLENGES BY 2020 IN GREECE: A QUESTIONNAIRE SURVEY
In a notable change from the position in the past, the Greek government is committed to greening the economy and has assumed determined policies and actions to boost the utilization of renewable energy. The aim of the paper is, firstly, to present the latest developments of the renewable energy policy in Greece, the current achievements and impediments in the implementation of planned reforms in the accomplishment of its 2020 targets, and the specific policy measures introduced; second, to discuss the pace of respective developments in other EU-28 member countries; and, third, through a questionnaire survey and stratified interviews with market participants, to verify the achievements of the government towards reversing previous bureaucratic and prone to corruption procedures. Respectively, research survey results from our survey and interviews conducted in the second semester of 2014 are presented. The majority of respondents expect that the targets set in the National Renewable Energy Action Plan will be reached by 2020. The paper and the questionnaire survey have been conducted under the auspices of the European research program THALES, which intends to measure various aspects of the shadow economy in Greece, also including the areas of renewable energy trade and finance.
http://scientiairanica.sharif.edu/article_4407_cf612b783df6842c716132ba8a2406e0.pdf
2018-02-01
359
369
10.24200/sci.2017.4407
Renewable Energy
government policy
questionnaire survey
shadow economy
Greece
Aristidis
Bitzenis
1
Department of International and European Studies, University of Macedonia, Research Leader of European Research Fund Thalis, 156 Egnatias Str., GR 54006, Greece
AUTHOR
Panagiotis
Kontakos
pkontakos@uclan.ac.uk
2
School of Business & Management, University of Central Lancashire, 12-14 University Avenue, CY 7080, Cyprus; Postdoctoral Researcher under Thales Program at the University of Macedonia, Greece
LEAD_AUTHOR
Charisios
Kafteranis
3
University of Macedonia, Researcher under Thales Program, Department of International and European Studies, University of Macedonia, 156 Egnatias Str., GR 54006, Greece
AUTHOR
References
1
1. Zerhouni, F., Zerhouni, M., Zegrar, M., Benmessaoud,
2
M., Tilmatine, A. and Stambouli, A., Modelling
3
polycristallin photovoltaic cells using design of experiments",
4
Scientia Iranica, 21(6), pp. 2273-2279 (2014).
5
2. Ghaedi, A., Abbaspour, A., Fotuhi-Friuzabad, M. and
6
Parvania, M. Incorporating large photovoltaic farms
7
in power generation system adequacy assessment",
8
Scientia Iranica, 21(3), pp. 924-934 (2014).
9
3. Carmo, M., Fritz, D., Mergel, J. and Stolten, D.
10
368 A. Bitzenis et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 359{369
11
A comprehensive review on PEM water electrolysis",
12
International Journal of Hydrogen Energy, 38(12), pp.
13
4901-4934 (2013).
14
4. Reinoso, C., Cutrera, M., Battioni, M., Milone, D.
15
and Buitrago, R. Photovoltaic generation model as
16
a function of weather variables using articial intelligence
17
techniques", International Journal of Hydrogen
18
Energy, 37(19), pp. 14781-14785 (2012).
19
5. Ohara, B., Wagner, M., Kunkle, C., Watson, P.,
20
Williams, R., Donohoe, R., Ugarte, K., Wilmoth, R.
21
and Chong, M. Residential solar combined heat and
22
power generation using solar thermoelectric generation",
23
Journal of Electronic Materials, 44(6), pp. 2132-
24
2141 (2015).
25
6. Harman, T. and Zunger, A. Special issue on research
26
opportunities in photovoltaic semiconductors",
27
Journal of Electronic Materials, 22(1), pp. 0361-5235
28
7. Faridimehr, S. and Niaki, S.T.A. Optimal strategies
29
for price, warranty length, and production rate of a
30
new product with learning production cost", Scientia
31
Iranica, Transactions E, 20(6), pp. 2247-2258 (2013).
32
8. Shavandi, H. and Zare, A.G. Analyzing the price
33
skimming strategy for new product pricing", Scientia
34
Iranica E, 20(6), pp. 2099-2108 (2013).
35
9. Zavadskas, E., Antucheviciene, Turskis, J. and Adeli,
36
H. Hybrid multiple-criteria decision-making methods:
37
A review of applications in engineering", Scientia
38
Iranica, 23(1), pp. 1-20 (2016).
39
10. Tsoutsos, T., Maria, E. and Mathioudakis, V. Sustainable
40
siting procedure of small hydroelectric plants:
41
the Greek experience", Energy Policy, 35(5), pp. 2946-
42
2959 (2007).
43
11. Maria, E. and Tsoutsos, T. The sustainable management
44
of renewable energy sources installations: legal
45
aspects of their environmental impact in small Greek
46
islands", Energy Conversion and Management, 45, pp.
47
631-638 (2004).
48
12. Zografakis, N., Menegaki, A. and Tsagarakis, K. Effective
49
education for energy eciency", Energy Policy,
50
36, pp. 3226-3232 (2008).
51
13. International Energy Agency, IEA, Key World Energy
52
Statistics (2014).
53
14. Eurostat, Statistics explained, available at: http://
54
ec.europa.eu/eurostat/statistics-explained/index.php/
55
Renewable energy statistics. Last accessed on 17/
56
15. International Energy Agency, Energy policies of IEA
57
countries, Greece review 2011", OECD/IEA, France,
58
16. European Commission ENERGY: country factsheets",
59
Directorate-General for Energy, V.1.3 (2012).
60
17. Intelligent Energy Europe, Survey Report: Progress in
61
Energy Eciency Policies in the EU Member States
62
- the Experts Perspective, Findings from the Energy
63
Eciency Watch Project, O. O. Energiesparverband,
64
Linz, Austria (2012).
65
18. Kontakos, P. and Zhelyazkova, V. Energy infrastructure
66
projects of common interest in the SEE, Turkey
67
& Eastern Mediterranean", In Energy Systems and
68
Management, Chapter 25, A.N. Bilge, A. O. Toy and
69
M.E. Gunay, Eds., Istanbul Bilgi University - Palmet,
70
Istanbul: Springer International Publishing, pp. 261-
71
267 (2015).
72
19. European Union, Statistical Pocketbook, 27 (2015).
73
20. Erbach, G. Promotion of renewable energy sources in
74
the EU", In Depth Analysis, European Parliamentary
75
Research Service, PE 583.810, ISBN 978-92-823-9353-
76
6, pp. 1-20 (2016). DOI: 10.2861/062931
77
21. European Union, Assessing Renewables Policy in the
78
EU, DiaCore project (2016).
79
22. Kaldellis, J., Kapsali, M. and Katsanou, E. Renewable
80
energy applications in Greece. What is the public
81
attitude?", Energy Policy, 3, pp. 37-48 (2012).
82
23. Paravantis, J., Stigka, E. and Mihalakakou, G. An
83
analysis of public attitudes towards renewable energy
84
in Western Greece", The 5th International Conference
85
on Information, Intelligence, Systems and Applications,
86
pp. 300-305 (2014). DOI: 10.1109
87
24. Bitzenis, A. and Kontakos, P. Energy trade and
88
tax evasion in the oil sector in Greece", 7th Annual
89
Conf. of the Euromed Academy, Kristiansand, Norway
90
(September 2014).
91
25. Zarakis, D., Chalvatzis, K. and Kaldellis, J. Socially
92
just", support mechanisms for the promotion of
93
renewable energy sources in Greece", Renewable and
94
Sustainable Energy Reviews, 21, pp. 478-493 (2013).
95
26. Menegaki, A. and Gurluk, S. Greece & Turkey;
96
assessment and comparison of their renewable energy
97
performance", International Journal of Energy Economics
98
and Policy, 3(4), pp. 367-383 (2013).
99
27. Metaxas, A. and Tsinisizelis, M. The development of
100
renewable energy governance in Greece. Examples of a
101
failed (?) policy", In Renewable Energy Governance,
102
23, pp. 155-168 (2013).
103
28. REN 21 Renewables 2016: global status report", Renewable
104
Energy Policy Network for the 21st Century,
105
pp. 1-272 (2016).
106
29. Mondol, J. and Koumpetsos, N. Overview of challenges,
107
prospects, environmental impacts and policies
108
for renewable energy and sustainable development in
109
Greece", Renewable and Sustainable Energy Reviews,
110
1(23), pp. 431-442 (2013).
111
ORIGINAL_ARTICLE
Single machine scheduling to minimize the maximum tardiness under piecewise linear deteriorating jobs
In many realistic production environments, jobs will take longer time if they begin later. This phenomenon is known as deteriorating jobs which have widely been studied. In this paper, the piecewise linear deterioration is discussed in a single machine scheduling problem of minimizing the maximum tardiness. After proving the NP-hardness of problem, a Branch and Bound and a heuristic algorithm with O(n2) are proposed for solving the large scale problems to near optimal solutions. The heuristic approach is also used to determine an upper bound on the solution of B&B algorithm. The computational results for evaluating performance of the two algorithms confirm the excellent performance of B&B algorithm as it is able to solve the problems with at least 32 jobs within a reasonable time. Notably, the heuristic approach is quite accurate and efficient with an average error percentage of less than 0.3%.
http://scientiairanica.sharif.edu/article_4408_ade9762f2cb7a30e00e16179d788070f.pdf
2018-02-01
370
385
10.24200/sci.2017.4408
Scheduling
Piecewise linear deteriorating jobs
single machine
Tardiness
Branch and Bound
Heuristic
Abbas-Ali
Jafari
a.jafari@stu.yazd.ac.ir
1
Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
AUTHOR
M. M.
Lotfi
2
Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
References
1
1. Jafari, A. and Moslehi, G. Scheduling linear deteriorating jobs to minimize the number of tardy jobs", Journal of Global Optimization, 54(2), pp. 389-404 (2012).
2
2. Lee, WC. and Lu, Z.S. Group scheduling with deteriorating jobs to minimize the total weighted number
3
of late jobs", Applied Mathematics and Computation,
4
218(17), pp. 8750-8757 (2012).
5
3. Moslehi, G. and Jafari, A. Minimizing the number
6
of tardy jobs under piecewise-linear deterioration",
7
Computers & Industrial Engineering, 59(4), pp. 573-
8
584 (2010).
9
4. Pinedo, M., Scheduling: Theory, Algorithms, and
10
Systems, 3th Ed., Upper Saddle River, Prentice Hall
11
5. Yin, Y., Cheng, T.C.E. andWu, C.C. Scheduling with
12
time-dependent processing times 2015, Mathematical
13
Problems in Engineering, Article ID 367585, 2 pages
14
6. Yin, Y., Cheng, T.C.E. and Wu, C.C. Scheduling
15
with time-dependent processing times", Mathematical
16
Problems in Engineering, Article ID 201421, 2 pages
17
7. Alidaee, B. and Womer, N.K. Scheduling with time
18
dependent processing times: Review and extensions",
19
Journal of the Operational Research Society, 50(7), pp.
20
711-720 (1999).
21
8. Cheng, T.C.E., Ding, Q. and Lin, B.M.T. A concise
22
survey of scheduling with time-dependent processing
23
times", European Journal of Operational Research,
24
152(1), pp. 1-13 (2004).
25
9. Browne, S. and Yechiali, U. Scheduling deteriorating
26
jobs on a single processor", Computers & Operations
27
Research, 38(3), pp. 495-498 (1990).
28
10. Bachman, A. and Janiak, A. Minimizing maximum
29
lateness under linear deterioration theory and methodology",
30
European Journal of Operational Research,
31
126(3), pp. 557-566 (2000).
32
11. Hsu, Y.S. and Lin, B.M.T. Minimization of maximum
33
lateness under linear deterioration", Omega, 31(6), pp.
34
459-469 (2003).
35
12. Ng, C.T., Wang, J.B., Cheng, T.C.E. and Liu, L.L. A
36
branch-and-bound algorithm for solving a two-machine
37
ow shop problem with deteriorating jobs", Computers
38
& Operation Research, 37(1), pp. 83-90 (2010).
39
13. Lee, W.C., Yeh, W.C. and Chung, Y.H. Total tardiness
40
minimization in permutation
41
owshop with deterioration
42
consideration", Applied Mathematical Modelling,
43
38(13), pp. 3081-3092 (2014).
44
14. Yin, Y., Wang, Y., Cheng, T.C.E., Liu, W. and Li, J.
45
Parallel-machine scheduling of deteriorating jobs with
46
potential machine disruptions", Omega, 69, pp. 17-28
47
15. Luo, W., and Ji, M. Scheduling a variable maintenance
48
and linear deteriorating jobs on a single machine",
49
Information Processing Letters, 115, pp. 33-39
50
16. Lee, W.C., Wu, C.C. and Chung, Y.H. Scheduling
51
deteriorating jobs on a single machine with release
52
times", Computers & Industrial Engineering, 54(3),
53
pp. 441-452 (2008).
54
17. Wu, C.C. and Lee, W.C. Two-machine
55
scheduling to minimize mean
56
ow time under linear
57
deterioration", International Journal of Production
58
Economics, 103(2), pp. 572-584 (2006).
59
18. Lee, W.C., Wu, C.C., Wen, C.C. and Chung, Y.H.
60
A two-machine
61
owshop makespan scheduling problem
62
with deteriorating jobs", Computers & Industrial
63
Engineering, 54(4), pp. 737-749 (2008).
64
19. Wang, J.B. and Wang, M.Z. Minimizing makespan
65
in three-machine
66
ow shops with deteriorating jobs",
67
Computers & Operation Research, 40(2), pp. 547-557
68
20. Yin, Y., Cheng, T.C.E., Wan, L., Wu, C.C. and Liu,
69
J. Two-agent single-machine scheduling with deteriorating
70
jobs", Computers & Industrial Engineering, 81,
71
pp. 177-185 (2015).
72
21. Mosheiov, G. Scheduling jobs under simple linear deA.
73
A. Jafari and M.M. Lot/Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 370{385 383
74
terioration", Computers & Operation Research, 21(6),
75
pp. 653-659 (1994).
76
22. Wang, J.B., Ng, C.T.D., Chen, T.C.E. and Liu,
77
L.L. Minimizing total completion time in a twomachine
78
ow shop with deteriorating jobs", Applied
79
Mathematics and Computation, 180(1), pp. 185-193
80
23. Yang, S.H. andWang, J.B. Minimizing total weighted
81
completion time in a two-machine
82
ow shop scheduling
83
under simple linear deterioration", Applied Mathematics
84
and Computation, 217(9), pp. 4819-4826 (2011).
85
24. Wu, C.C. and Lee, W.C. Scheduling linear deteriorating
86
jobs to minimize makespan with an availability
87
constraint on a single machine", Information Processing
88
Letters, 87(2), pp. 89-93 (2003).
89
25. Cheng, T.C.E., Hsu, C.J., Huang, Y.C. and Lee, W.C.
90
Single-machine scheduling with deteriorating jobs
91
and setup times to minimize the maximum tardiness",
92
Computers & Operation Research, 38(12), pp. 1760-
93
1765 (2011).
94
26. Lee, W.C., Lin, J.B. and Shiau, Y.R. Deteriorating
95
job scheduling to minimize the number of late jobs with
96
setup times", Computers & Industrial Engineering,
97
61(3), pp. 782-787 (2011).
98
27. Kubiak, W. and Velde, S. Scheduling deteriorating
99
jobs to minimize makespan", Naval Research Logistics,
100
45(5), pp. 511-523 (1998).
101
28. Lalla Ruiz, E. and Vob, S. Modeling the parallel machine
102
scheduling problem with step deteriorating jobs",
103
European Journal of Operational Research, 255(1), pp.
104
21-33 (2016).
105
29. Jafari, A. and Moslehi, G. Minimizing the weighted
106
number of tardy jobs on a single machine under piecewise
107
linear deterioration", 9th International Industrial
108
Engineering Conference, Tehran, Iran (2013).
109
30. Lai, P., Wu, C. and Lee, W. Single machine scheduling
110
with logarithm deterioration", Optimization Letters,
111
6(8), pp. 1719-1730 (2011).
112
31. Lee, C. and Yu, G. Parallel machine scheduling under
113
potential disruption", Optimization Letters, 2(1), pp.
114
27-37 (2008).
115
ORIGINAL_ARTICLE
A proposal for modeling and simulating correlated discrete Weibull variables
Researchers in applied sciences are often concerned with multivariate random vari9ables. In particular, multivariate discrete data often arise in many fields (statistical10 quality control, biostatistics, failure and reliability analysis, etc.) and modeling such11 data is a relevant task, as well as simulating correlated discrete data satisfying some spe12cific constraints. Here we consider the discrete Weibull distribution as an alternative to13 the popular Poisson random variable and propose a procedure for simulating correlated14 discrete Weibull random variables, with marginal distributions and correlation matrix as15signed by the user. The procedure indeed relies upon the Gaussian copula model and an16 iterative algorithm for recovering the proper correlation matrix for the copula ensuring17 the desired correlation matrix on the discrete margins. A simulation study is presented,18 which empirically assesses the performance of the procedure in terms of accuracy and19 computational burden, also in relation to the necessary (but temporary) truncation of20 the support of the discrete Weibull random variable. Inferential issues for the proposed21 model are also discussed and are eventually applied to a dataset taken from the literature,22 which shows that the proposed multivariate model can satisfactorily fit real-life correlated23 counts even better than the most popular or recent existing ones.
http://scientiairanica.sharif.edu/article_4412_d1dadf84d563de50f658d597d2900c4c.pdf
2018-02-01
386
397
10.24200/sci.2017.4412
correlated counts
Gaussian copula
parameter estimation
stochastic simulation
A.
Barbiero
1
Department of Economics, Management and Quantitative Methods, 4 Universit`a degli Studi di Milano, via Conservatorio 7, 20122 Milan, Italy
LEAD_AUTHOR
References
1
1. Nakagawa, T. and Osaki, S. The discrete Weibull distribution", IEEE Transactions on Reliability, 24(5), pp. 300-301 (1975).
2
2. Stein, W.E. and Dattero, R. A new discrete Weibull distribution", IEEE Transactions on Reliability, 33(2),
3
pp. 196-197 (1984).
4
3. Padgett, W.J. and Spurrier, J.D. Discrete failure
5
models", IEEE Transactions on Reliability, 34(3), pp.
6
253-256 (1985).
7
4. Englehardt, J.D. and Li, R.C. The discrete Weibull
8
distribution: An alternative for correlated counts with
9
conrmation for microbial counts in water", Risk
10
Analysis, 31(3), pp. 370-381 (2011).
11
5. Khan, M.S.A., Khalique, A. and Abouammoh, A.M.
12
On estimating parameters in a discrete Weibull distribution",
13
IEEE Transactions on Reliability, 38(3),
14
pp. 348-350 (1989).
15
6. Kulasekera, K.B. Approximate MLE's of the parameters
16
of a discrete Weibull distribution with type I
17
censored data", Microelectronics Reliability, 34(7), pp.
18
1185-1188 (1984).
19
7. Barbiero, A. A comparison of methods for estimating
20
parameters of the type I discreteWeibull distribution",
21
Statistics and Its Interface, 9(2), pp. 203-212 (2016).
22
8. Bebbington, M., Lai, C.D., Wellington, M. and Zitikis,
23
R. The discrete additive Weibull distribution: A
24
bathtub-shaped hazard for discontinuous failure data",
25
Reliability Engineering & System Safety, 106, pp. 37-
26
44 (2012).
27
9. Lai, C.D. Discrete Weibull Distributions and Their
28
Generalizations", In Generalized Weibull Distributions,
29
Springer Berlin Heidelberg, pp. 97-113 (2014).
30
A. Barbiero/Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 386{397 397
31
10. R Core Team. R: A language and environment for statistical
32
computing", R Foundation for Statistical Computing,
33
Vienna, Austria (2016). URL https://www.Rproject.
34
11. Barbiero, A. DiscreteWeibull: Discrete Weibull
35
distributions (Type 1 and 3)", R package version 1.1.
36
http://CRAN.R-project.org/package=Discrete
37
Weibull (2015).
38
12. Sarabia, J.M. and Gomez-Deniz, E. Construction of
39
multivariate distributions: A review of some recent
40
results", SORT, 32(1), pp. 3-36 (2008).
41
13. Nelsen, R.B., An Introduction to Copulas, Springer,
42
New York (1999).
43
14. Nikoloulopoulos, A.K. Copula-based models for multivariate
44
discrete response data", Copulae in Mathematical
45
and Quantitative Finance, Lecture Notes in
46
Statistics, In P. Jaworski et al., Eds., pp. 231-249
47
15. Lai, C.D. Constructions of discrete bivariate distributions",
48
Advances in Distribution Theory, Order
49
Statistics, and Inference, in N. Balakrishnan et al.,
50
Eds., pp. 29-58 (2006). Birkhauser Boston.
51
16. Englehardt, J. Distributions of Autocorrelated First-
52
Order Kinetic Outcomes: Illness Severity", PLoS
53
ONE, 10(6) (2015). DOI: 10.1371/journal.pone.
54
17. Barbiero, A. and Ferrari, P.A. GenOrd: Simulation
55
of ordinal and discrete variables with given correlation
56
matrix and marginal distributions", R package version
57
1.4.0. http://CRAN.R-project.org/package=GenOrd
58
18. Ferrari, P.A. and Barbiero, A., Simulating ordinal
59
data", Multivariate Behavioral Research, 47(4), pp.
60
566-589 (2012).
61
19. Barbiero, A. and Ferrari, P.A. Simulating correlated
62
ordinal and discrete variables with assigned marginal
63
distributions", in V. Melas, S. Mignani, P. Monari,
64
L. Salmaso, Eds., Topics in Statistical Simulation.
65
Research from the 7th International Workshop on Statistical
66
Simulation, Springer-Verlag, New York (2014).
67
20. Cario, M.C. and Nelson, B.L. Modeling and generating
68
random vectors with arbitrary marginal distributions
69
and correlation matrix", Technical Report, Department
70
of Industrial Engineering and Management
71
Sciences, Northwestern University, Evanston, Illinois
72
21. Nelson, B.L. Foundations and Methods of Stochastic
73
Simulation: A First Course, Springer, New York
74
22. Madsen, L. and Dalthorp, D. Simulating correlated
75
count data", Environmental and Ecological Statistics,
76
14(2), pp. 129-148 (2007).
77
23. Madsen, L. and Birkes, D. Simulating dependent
78
discrete data", Journal of Statistical Computation and
79
Simulation, 83(4), pp. 677-691 (2013).
80
24. Genz, A., Bretz, F., Miwa, T., Mi, X., Leisch, F.,
81
Scheipl, F. and Hothorn, T. mvtnorm: Multivariate
82
normal and t distributions", R package version
83
1.0-2. http://CRAN.R-project.org/package=mvtnorm
84
25. Barbiero, A. and Ferrari, P.A. Simulating correlated
85
Poisson variables", Applied Stochastic Models in Business
86
and Industry, 31, pp. 669-680 (2015).
87
26. Demirtas, H. and Hedeker, D. A practical way for
88
computing approximate lower and upper correlation
89
bounds", The American Statistician, 65, pp. 104-109
90
27. Barbiero, A. Simulating correlated discrete Weibull
91
variables: a proposal and an implementation in the R
92
environment", International Conference of Computational
93
Methods in Science and Engineering, Athens,
94
20-23 March 2015. AIP Conference Proceedings,
95
1702(190017) (2015).
96
28. Joe, H. Asymptotic eciency of the two-stage estimation
97
method for copula-based models", Journal of
98
Multivariate Analysis, 94(2), pp. 401-419 (2005).
99
29. Mitchell, C.R. and Paulson, A.S. A new bivariate negative
100
binomial distribution", Naval Research Logistics
101
Quarterly, 28, pp. 359-374 (1981).
102
30. Famoye, F. A new bivariate generalized Poisson
103
distribution", Statistica Neerlandica, 64(1), pp. 112-
104
124 (2010).
105
ORIGINAL_ARTICLE
The NDEA–MOP Model in the Presence of Negative Data Using Fuzzy Method
In this study, the multi-objective programming (MOP) method was used to solve network DEA (NDEA) models with assumption that, negative data is considered for the proposed NDEA model which consists of semi-negative and semi-positive input and output. At first, two stage and then k stage production models were formulated with consideration of negative data. In the multi-objective programming, two separate objective functions including the divisional efficiencies and the overall efficiency of the organization are modeled. In comparison to conventional DEA with negative data, the advantage of the proposed NDEA models is consideration of intermediate processes and products, in order to calculate the organization's overall efficiency. However, in conventional DEA, sub-stages of the organizations are neglected. To measure the efficiencies of an organization regarding interactive internal process, two case studies were investigated by application of the NDEA-MOP method with negative data. Case study 1 is focused on units with two stages having semi-negative and semi-positive indexes. In case study 2, units with three stages are evaluated. These units also have semi-negative and semi-positive indexes. The overall efficiency of each unit is calculated using the proposed models. Fuzzy approach as a solution procedure is applied.
http://scientiairanica.sharif.edu/article_4413_83c3c58afe2e36df0e3831c996a1c83d.pdf
2018-02-01
398
409
10.24200/sci.2017.4413
Data envelopment analysis
Network DEA
semi-positive data
semi-negative data
overall efficiency
Fuzzy method
Kianoosh
Kianfar
kianfarkianoosh62@gmail.com
1
Department of Industrial Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Mahnaz
Ahadzadeh Namin
mahnazahadzadehnamin@gmail.com
2
Department of Mathematics, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
Akbar
Alam Tabriz
a-tabriz@sbu.ac.ir
3
Department of Management, Shahid Beheshti University, Tehran, Iran
AUTHOR
Esmaeil
Najafi
4
Department of Industrial Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran
AUTHOR
Farhad
Hosseinzadeh Lotfi
farhad@hosseinzadeh.ir
5
Department of Mathematics, Science and Research branch, Islamic Azad University, Tehran, Iran
AUTHOR
References
1
1. Tone, K. and Tsutsui, M. Network DEA: A slacksbased measure approach", Eur. J. Oper. Res., 197(1), pp. 243-252 (2009).
2
2. Sexton, T.R. and Lewis, H.F. Two-stage DEA: An application to major league baseball", J. Prod. Anal. 19(2), pp. 227-249 (2003).
3
3. Despotis, D.K. and Koronakos, G. Eciency assessment
4
in two-stage processes: A novel network DEA
5
Approach", Pro. Computer Science, 31, pp. 299-307
6
4. Carayannis, E.G., Goletsis, Y. and Grigoroudis, E.
7
Multi-level multi-stage eciency measurement: the
8
case of innovation systems", Oper. Res., 15(2) pp. 253-
9
274 (2015).
10
5. Jarosz, P., Kusiak, J., MaBecki, S.B., Oprocha, P.,
11
Sztangret, A. and Wilkus, M.A. Methodology for
12
optimization in multistage industrial processes: A
13
Pilot Study", Math. Prob. Eng., 2015, pp. 1-10 (2015).
14
6. Gang, D., Li, C., Yin-Zhen, L. and Jie-Yan, S.A. Tanweer,
15
optimization on production-inventory problem
16
with multistage and varying demand", J. Appl. Math.,
17
2012, pp. 1-17 (2012).
18
7. Charnes, A., Cooper, W.W. and Rhodes, E. Measuring
19
the eciency of decision making units", Eur. J.
20
Oper. Res., 2(6), pp. 429-444 (1978).
21
8. Banke, R., Charnes, A. and Cooper, W.W. Some
22
models for estimating technical and scale ineciencies
23
in data envelopment analysis", Manag. Sci., 30(9), pp.
24
1078-1092 (1984).
25
9. Cheng, H., Zhang, Y., Cai, J. and Huang, W. A
26
multiobjective programming method for ranking all
27
units based on compensatory DEA model", Math.
28
Prob. Eng., 2014, pp. 1-14. (2014).
29
10. Kao, H.Y., Chan, C.Y. and Wu, D.J. A multiobjective
30
programming method for solving network
31
DEA", Appl. Soft Comput., 24(2014), pp. 406-413
32
11. Kazemi Matin, R. and Azizi, R. A unied network-
33
DEA model for performance measurement of production
34
systems", Measurement., 60, pp. 186-193 (2015).
35
12. Wang, W.K., Lu, W.M. and Liu, P.Y. A fuzzy multiobjective
36
two-stage DEA model for evaluating the
37
K. Kianfar et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 398{409 409
38
performance of US bank holding companies", Expert.
39
Syst. Appl., 41(9), pp. 4290-4297 (2014).
40
13. Dimitris, K. and Despotis, G.K. Eciency assessment
41
in two-stage processes: A novel network DEA
42
approach", Procedia. Comput. Sci., 31, pp. 299-307
43
14. Halkos, G.E., Tzeremes, N.G. and Kourtzidis, S.A. A
44
unied classication of two-stage DEA models", Sur.
45
Oper. Res. Manag. Sci., 19(1), pp. 1-16 (2014).
46
15. Lee, E.S. and Li, R.J. Fuzzy multiple objective programming
47
and compromise programming with Pareto
48
optimum", Fuzzy Set. Syst., 53(3), pp. 275-288 (1993).
49
16. Izadikhah, M. and Farzipoor Saen, R. Evaluating
50
sustainability of supply chains by two-stage range
51
directional measure in the presence of negative data",
52
Trans. Res. Part D., 49, pp. 110-126 (2016).
53
17. Olfata, L., Amiri, M., Sou, J.B. and Pishdar, M. A
54
dynamic network eciency measurement of airports
55
performance considering sustainable development concept:
56
A fuzzy dynamic network-DEA approach", J.
57
Air. Trans. Manag., 57, pp. 272-290 (2016).
58
18. Chen, Y. and Zhu, J. Measuring information technology's
59
indirect impact on rm performance", Inform.
60
Tech. Manag. J., 5(1-2) pp. 9-22 (2004).
61
19. Tone, K. and Tsutsui, M. Network DEA: a slacksbased
62
measure approach", Eur. J.Oper. Res 197, pp.
63
243-252 (2009).
64
ORIGINAL_ARTICLE
Integrated and Dynamic Design of Sustainable Closed-loop Supply Chain Network Considering Pricing
In this paper, a novel multi-objective model for dynamic and integrated network design of sustainable closed-loop supply chain network is proposed, which aims to optimize economic, environmental, and social concerns, simultaneously. In order to have a dynamic design, multiple strategic periods are considered during the planning horizon. Furthermore, different short-term decisions are integrated with long-term decisions related to the network design problem. Two of these short-term decisions are determining selling price of products in forward logistics and buying price of used products from customer zones in reverse logistics. Based on the complexity of proposed multi-objective model, a multi-objective imperialist competitive algorithm (MOICA) is proposed to solve the model, and the results are compared with a non-dominated sorting genetic algorithm (NSGA-II). Finally, to evaluate the performance of proposed algorithm, several numerical examples are used, which the results indicate the efficiency of the proposed algorithm.
http://scientiairanica.sharif.edu/article_4411_6b9d84032eb1b6e926b98b7d767a9250.pdf
2018-02-01
410
430
10.24200/sci.2017.4411
Dynamic supply chain network design
Integrated planning
Sustainability
Pricing
Pareto-based multi-objective metaheuristic algorithm
Arash
Nobari
arashnob@yahoo.com
1
Industrial Engineering Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
LEAD_AUTHOR
Amirsaman
Kheirkhah
amirsamankheirkhah@yahoo.com
2
Industrial Engineering Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
AUTHOR
References
1
1. Chopra, S. and Meindl, P., Supply Chain Management: Strategy, Planning and Operations, New Jersey: Prentice Hall (2007).
2
2. Melo, M.T., Nickel, S. and Saldanha-da-Gama F. Facility location and supply chain management - a review", European Journal of Operation Research, 196, pp. 401-412 (2009).
3
3. Simchi-Levi, D., Kaminsky, P. and Simchi-Levi, E., Designing and Managing the Supply Chain: Concepts,
4
Strategies, and Cases, New York: McGraw-Hill (1999).
5
4. Altiparmak, F., Gen, M. Lin, L. and Paksoy, T.
6
A genetic algorithm approach for multi-objective
7
optimization of supply chain networks", Computers &
8
Industrial Engineering, 51, pp. 197-216 (2006).
9
5. Zanjirani Farahani, R., Rezapour, S., Drezner, T.
10
and Fallah, S. Competitive supply chain network
11
design: An overview of classications, models, solution
12
techniques and applications", Omega, 45, pp. 92-118
13
6. Fattahi, M., Mahootchi, M., Govindan, K. and Moattar
14
Husseini, S.M. Dynamic supply chain network
15
design with capacity planning and multi-period pricing",
16
Transportation Research Part E, 81, pp. 169-202
17
7. Ahi, P. and Searcy, C. An analysis of metrics used to
18
measure performance in green and sustainable supply
19
chains", Journal of Cleaner Production, 86, pp. 360-
20
377 (2015).
21
8. Zhang, S. On a prot maximizing location model",
22
Annual of Operations Research, 103(1), pp. 251-260
23
9. Shen, J.M. A prot-maximizing supply chain network
24
design model with demand choice
25
exibility", Operations
26
Research Letters, 34(6), pp. 673-682 (2006).
27
10. Nagurney, A. and Nagurney, L.S. Sustainable supply
28
chain network design: a multi-criteria perspective", International
29
Journal of Sustainable Engineering, 3(3),
30
pp. 189-197 (2010).
31
11. Pishvaee, M.S., Razmi, J. and Torabi, S.A. Robust
32
possibilistic programming for socially responsible supply
33
chain network design: A new approach", Fuzzy Sets
34
and Systems, 206, pp. 1-20 (2012).
35
12. Badri, H., Bashiri, M. and Hejazi, T.H. Integrated
36
strategic and tactical planning in a supply chain
37
network design with a heuristic solution method",
38
Computers & Operations Research, 40(4), pp. 1143-
39
1154 (2013).
40
13. Mota, B., Gomes, M.I., Carvalho, A. and Barbosa-
41
Povoa, A.P. Towards supply chain sustainability: economic,
42
environmental and social design and planning",
43
Journal of Cleaner Production, 105, pp. 14-27 (2014).
44
14. Ahmadi-Javid, A. and Ghandali, R. An ecient
45
optimization procedure for designing a capacitated
46
distribution network with price-sensitive demand",
47
Optimization and Engineering, 15(3), pp. 801-817
48
15. Ahmadi-Javid, A. and Hoseinpour, P. Incorporating
49
location, inventory and price decisions into a supply
50
chain distribution network design problem", Computers
51
& Operations Research, 56, pp. 110-119 (2015).
52
16. Govindan, K., Jafarian, A. and Nourbakhsh, V. Biobjective
53
integrating sustainable order allocation and
54
sustainable supply chain network strategic design with
55
stochastic demand using a novel robust hybrid multiobjective
56
metaheuristic", Computers & Operations
57
Research, 62, pp. 112-130 (2015b).
58
17. Aras, N., Aksen, D. and Gonul Tanugur, A. Locating
59
collection centers for incentive-dependent returns
60
under a pick-up policy with capacitated vehicles",
61
European Journal of Operational Research, 191(3), pp.
62
1223-1240 (2008).
63
18. Aras, N. and Aksen, D. Locating collection centers
64
for distance- and incentive-dependent returns", International
65
Journal of Production Economics, 111(2), pp.
66
316-333 (2008).
67
19. Dehghanian, F. and Mansour, S. Designing sustainable
68
recovery network of end-of-life products using
69
genetic algorithm", Resources, Conservation and Recycling,
70
53, pp. 559-570 (2009).
71
20. Tuzkaya, G., Gulsun, B., and Onsel, S. A methodology
72
for the strategic design of reverse logistics networks
73
and its application in the Turkish white goods industry",
74
International Journal of Production Research,
75
49(15), pp. 4543-4571 (2011).
76
21. Neto, J.Q.F., Bloemhof-Ruwaard, J., van Nunen, J.
77
and van Heck, E. Designing and evaluating sustainable
78
logistics networks", International Journal of
79
Production Economics, 111(2), pp. 195-208 (1998).
80
22. Shi, J., Zhang, G. and Sha, J. Optimal production
81
and pricing policy for a closed loop system", Resources,
82
Conservation and Recycling, 55, pp. 639-647 (2011a).
83
A. Nobari and A. Kheirkhah/Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 410{430 429
84
23. Shi, J., Zhang, G. and Sha, J. Optimal production
85
planning for a multi-product closed loop system with
86
uncertain demand and return", Computers & Operation
87
Researches, 38(3), pp. 641-650 (2011b).
88
24. Chaabane, A., Ramudhin, A. and Paquet, M. Design
89
of sustainable supply chains under the emission
90
trading scheme", International Journal of Production
91
Economics, 135(1), pp. 37-49 (2012).
92
25. Pishvaee, M.S. and Razmi, J. Environmental supply
93
chain network design using multi-objective fuzzy
94
mathematical programming", Applied Mathematical
95
Modelling, 36, pp. 3433-3446 (2012).
96
26. Keyvanshokooh, E., Fattahi, M., Seyed-Hosseini, S.M.
97
and Tavakkoli-Moghaddam, R. A dynamic pricing
98
approach for returned products in integrated forward/
99
reverse logistics network design", Applied Mathematical
100
Modelling, 37, pp. 10182-10202 (2013).
101
27. Devika, K., Jafarian, A. and Nourbakhsh, V. Designing
102
a sustainable closed-loop supply chain network
103
based on triple bottom line approach: A comparison
104
of metaheuristics hybridization techniques", European
105
Journal of Operational Research, 235, pp. 594-615
106
28. Kaya, O. and Urek, B. A mixed integer nonlinear programming
107
model and heuristic solutions for location,
108
inventory and pricing decisions in a closed loop supply
109
chain", Computers & Operations Research, 65, pp. 93-
110
103 (2015).
111
29. Dubey, R., Gunasekaran, A. and Childe S.J. The
112
design of a responsive sustainable supply chain network
113
under uncertainty", International Journal of Advanced
114
Manufacturing Technology, 80(1), pp. 427-445 (2015).
115
30. Accorsi, R., Manzini, R., Pini, C. and Penazzi, S. On
116
the design of closed-loop networks for product life cycle
117
management: Economic, environmental and geography
118
considerations", Journal of Transport Geography,
119
48, pp. 121-134 (2015).
120
31. Govindan, K., Soleimani, H. and Kannan, D. Reverse
121
logistics and closed-loop supply chain: A comprehensive
122
review to explore the future", European Journal
123
of Operational Research, 240, pp. 603-626 (2015a).
124
32. Elkington, J., Cannibals with Forks: The Triple Bottom
125
Line of 21st Century Business (The Conscientious
126
Commerce Series) (1998).
127
33. Brundtland, G.H., Our Common Future, Oxford paperbacks
128
34. Seuring, S. A review of modeling approaches for sustainable
129
supply chain management", Decision Support
130
Systems, 54(4), pp. 1513-1520 (2013).
131
35. Eskandarpour, M., Dejax, P., Miemczyk, J. and Peton,
132
O. Sustainable supply chain network design: an
133
optimization-oriented review", Omega, 54, pp. 11-32
134
36. Thanh, P.N., Bostel, N. and Peton, O. A dynamic
135
model for facility location in the design of complex
136
supply chains", International Journal of Production
137
Economic, 113(2), pp. 678-693 (2008).
138
37. Shen, Z.J. Integrated supply chain design models:
139
a survey and future research directions", Journal of
140
Industrial and Management Optimization, 3(1), pp. 1-
141
27 (2007).
142
38. Salema, M.I.G., Barbosa-Povoa, A.P. and Novais, A.Q.
143
Simultaneous design and planning of supply chains
144
with reverse
145
ows: a generic modelling framework",
146
European Journal of Operation Research, 203(2), pp.
147
336-349 (2010).
148
39. Huang, J., Leng, M. and Parlar, M. Demand function
149
in decision modeling: a comprehensive survey and
150
research directions", Decision Sciences, 44(3), pp. 557-
151
609 (2013).
152
40. Liu, Z., Qiu, T. and Chen, B. A study of the LCA
153
based biofuel supply chain multi-objective optimization
154
model with multi-conversion paths in China",
155
Applied Energy, 126, pp. 221-234 (2014).
156
41. GRI, Global Reporting Initiative - G4 Sustainability
157
Reporting Guidelines, Amsterdam (2013).
158
42. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T.
159
A fast and elitist multi-objective genetic algorithm:
160
NSGA-II", IEEE Transactions on Evolutionary Computation,
161
6, pp. 182-197 (2002).
162
43. Atashpaz-Gargari E. and Lucas C. Imperialist competitive
163
algorithm: An algorithm for optimization
164
inspired by imperialist competition", IEEE Congress
165
on Evolutionary Computation (2007).
166
44. Enayatifar, R., Youse, M., Abdullah, A.H. and
167
Darus, A.N. MOICA: A novel multi-objective approach
168
based on imperialist competitive algorithm",
169
Applied Mathematics and Computation, 219, pp. 8829-
170
8841 (2013).
171
45. Srinivas, N. and Deb, K. Multi-objective function
172
optimization using non-dominated sorting genetic algorithms",
173
Evolutionary Computation, 2(3), pp. 221-
174
248 (1995).
175
46. Zitzler, E. and Thiele L. Multi-objective optimization
176
using evolutionary algorithms a comparative case
177
study", Fifth International Conference on Parallel
178
Problem Solving from Nature (PPSN-V), Berlin, Germany,
179
pp. 292-301 (1998).
180
47. Zitzler, E. Evolutionary Algorithms for multiobjective
181
optimization: method and applications",
182
Ph.D. Thesis, Swaziland Federal Institute of Technology
183
Zorikh, Switzerland (1999).
184
48. Mousavi, M., Hajipour, V., Niaki, S.T.A. and Aalikar,
185
N. Optimizing multi-item multi-period inventory control
186
system with discounted cash
187
ation approaches:
188
Two calibrated meta-heuristic algorithms",
189
Applied Mathematical Modelling, 37, pp. 2241-2256
190
49. Hajipour, V., Rahmati, S.H.A., Pasandideh, S.H.R.
191
and Niaki S.T.A. A multi-objective harmony search
192
algorithm to optimize multi-server location-allocation
193
problem in congested systems", Computers & Industrial
194
Engineering, 72, pp. 187-197 (2014).
195
430 A. Nobari and A. Kheirkhah/Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 410{430
196
50. Fattahi, P., Hajipour, V. and Nobari, A. A biobjective
197
continuous review inventory control model:
198
Pareto-based metaheuristic algorithms", Applied Soft
199
Computing, 32, pp. 211-223 (2015).
200
51. MATLAB Version 7.10.0.499 (R2010a). The Math-
201
Works, Inc. Protected by U.S. and International
202
Patents (2010).
203
ORIGINAL_ARTICLE
A fuzzy multi-objective multi-product supplier selection and order allocation problem in supply chain under coverage and price considerations: An urban agricultural case study
In this paper, a fuzzy multi-objective model is presented to select and allocate the order to the suppliers in uncertainty conditions, considering multi-period, multi-source, and multi-product cases at two levels of a supply chain with pricing considerations. Objective functions considered in this study as the measures to evaluate the suppliers are the purchase, transportation, and ordering costs, timely delivering or deference shipment quality or wastages which are amongst major quality aspects. Partial and general coverage of suppliers in respect of distance and finally suppliers' weights make the amounts of products orders more realistic. Deference and coverage parameters in the model are considered as uncertain and random triangular fuzzy number. Since the proposed mathematical model is NP-hard, multi-objective particle swarm optimization (MOPSO) algorithm is presented. To validate the performance of MOPSO, we applied non-dominated sorting genetic algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms. A practical case study in an agricultural industry is shown to demonstrate that the proposed algorithm applies to the real-world problems. The results are analyzed using quantitative criteria, performing parametric, and non-parametric statistical analysis.
http://scientiairanica.sharif.edu/article_4409_f4ce8f549d930d3755477f9dbc0f15f9.pdf
2018-02-01
431
449
10.24200/sci.2017.4409
Multi-objective Supplier Selection Problem
Coverage
Fuzzy logic
MOPSO
NSGA-II
Alborz
Hajikhani
alborz.hajikhani@gmail.com
1
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Mohammad
Khalilzadeh
2
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
Seyed Jafar
Sadjadi
sjsadjadi@iust.ac.ir
3
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
References
1
1. Simchi-Levi, D., Kaminsky, P. and Simchi-Levi, E., Designing and Managing the Supply Chain, 2nd Ed., Boston: Irwin McGraw-Hill (2003).
2
2. Ghiani, G., Laporte, G. and Musmanno, R., Introduction To Logistics Systems Planning and Control, John
3
Wiley & Sons, Inc., Hoboken, New Jersey (2004).
4
3. Dulmin, R. and Mininno, V. Supplier selection using
5
a multi-criteria decision aid method", Journal of
6
Purchasing and Supply Management, 9, pp. 177-187
7
4. Hugos, M.E., Essentials of Supply chain Management,
8
2nd Ed. New Jersey: Wiley (2006).
9
5. Karasakal, O. and Karasakal, E. A maximal covering
10
location model in the presence of partial coverage",
11
Computers & Operations Research, 31(9), pp. 1515-
12
1526 (2004).
13
6. Liang, T. Fuzzy multi-objective
14
production/distribution planning decisions with
15
multi-product", Computers & Industrial Engineering,
16
55(3), pp. 676-694 (2008).
17
7. Torabi, S. and Hassini, E. Multi-site production planning
18
integrating procurement and distribution plans
19
in multi-echelon supply chains: An interactive fuzzy
20
goal programming approach", International Journal of
21
Production Research, 159, pp. 193-214 (2008).
22
8. Fatih, E., Serkan, G., Mustafa, K. and Diyar, A.
23
A multi-criteria intuitionistic fuzzy group decision
24
making for supplier selection with TOPSIS method",
25
Expert Systems With Applications, 36(8), pp. 11363-
26
11368 (2009).
27
9. Onot, S., Selin, S. and Isik, E. Long term supplier
28
selection using a combined fuzzy MCDM approach:
29
A case study for a telecommunication company",
30
Expert Systems With Applications, 36(2), pp. 3887-
31
3895 (2009).
32
10. Amid, A., Ghodsypour, S. and O'Brien, C. A
33
weighted additive fuzzy multi objective model for
34
the supplier Selection problem under price breaks
35
in a supply chain", International Journal Production
36
Economics, 131(1), pp. 323-332 (2009).
37
11. Kokangol, A. and Susuz, Z. Integrated analytical
38
hierarch process and mathematical programming to
39
supplier selection problem with quantity discount",
40
Applied Mathematical Modeling, 33(3), pp. 1417-1420
41
12. Tsai, W. and Wang, C. Decision making of sourcing
42
and order allocation with price discounts", Journal of
43
Manufacturing Systems, 29, pp. 47-54 (2010).
44
13. Atakhan, Y. and Ali Fuat, G. A weighted additive
45
fuzzy programming approach for multi-criteria supplier
46
selection", Expert Systems With Applications,
47
38(5), pp. 6281-6286 (2011).
48
14. Haleh, H. and Hamidi, A. A fuzzy MCDM model
49
for allocating orders to suppliers in a supply chain
50
under uncertainty over a multi-period time horizon",
51
448 A. Hajikhani et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 431{449
52
Expert Systems With Applications, 38(8), pp. 9076-
53
9083 (2011).
54
15. Liao, S.H., Lin, H. and Lai, P. An evolutionary
55
approach for multi-objective optimization of the integrated
56
location-inventory distribution network problem
57
in vendor-managed inventory", Expert Systems
58
With Applications, 38(6), pp. 6768-6776 (2011).
59
16. Lin, H. An integrated model for supplier selection
60
under a fuzzy situation", International Journal Production
61
Economics, 138, pp. 55-61 (2012).
62
17. Shaw, K., Shankar, R., Yadav, S. and Thakur, L.
63
Supplier selection using fuzzy AHP and fuzzy multiobjective
64
linear programming for developing low carbon
65
supply chain", Expert System With Applications,
66
39, pp. 8182-8192 (2012).
67
18. Nazari Shirkouhi, S., Shakouri, H., Javadi, B. and
68
Keramati, A. Supplier selection and order allocation
69
problem using a two-phase fuzzy multi-objective linear
70
programming", Applied Mathematical Modelling, 37,
71
pp. 9308-9323 (2013).
72
19. Esfandiari, N. and Seifbarghy, M. Modeling a stochastic
73
multi-objective supplier quota allocation problem
74
with price-dependent ordering", Applied Mathematical
75
Modelling, 37(8), pp. 5790-5800 (2013).
76
20. Arikan, F. A fuzzy solution approach for multi
77
objective supplier selection", Expert Systems With
78
Applications, 40(3), pp. 947-952 (2013).
79
21. Meena, P. and Sarmah, S. Multiple sourcing under
80
supplier failure risk and quantity discount: A genetic
81
algorithm approach", Transportation Research Part E,
82
50, pp. 84-97 (2013).
83
22. Hajipour, V., Khodakarami, V. and Tavana, M. The
84
redundancy queuing-location-allocation problem: A
85
novel approach", IEEE Transactions on Engineering
86
Management, 61(3), pp. 534-544 (2014a).
87
23. Hajipour, V., Rahmati, S.H.A., Pasandideh, S.H.R.
88
and Niaki, S.T.A. A multi-objective harmony search
89
algorithm to optimize multi-server location-allocation
90
problem in congested systems", Computers & Industrial
91
Engineering, 72, pp. 187-197 (2014b).
92
24. Patra, K. and Kumar Mondal, S. Multi-item supplier
93
selection model with fuzzy risk analysis studied by possibility
94
and necessity constraints", Fuzzy Information
95
and Engineering, 7(4), pp. 451-474 (2015).
96
25. Orji, I.J. and Wei, S. An innovative integration
97
of fuzzy-logic and systems dynamics in sustainable
98
supplier selection: A case on manufacturing industry",
99
Computers & Industrial Engineering, 88, pp. 1-12
100
26. Rahiminezhad Galankashi, M., Helmi, S.A. and
101
Hashemzahi, P. Supplier selection in automobile
102
industry: A mixed balanced scorecard-fuzzy AHP
103
approach", Alexandria Engineering Journal, In press
104
(2016). DOI: 10.1016/j.aej..01.005
105
27. Amorim, P., Curcio, E., Almada-Lobo, B., Barbosa-
106
Povoa, A.P., and Grossmann, I.E. Supplier selection
107
in the processed food industry under uncertainty",
108
European Journal of Operational Research, 252(3), pp.
109
801-814 (2016).
110
28. C ebi, F. and Otay, I. A two-stage fuzzy approach
111
for supplier evaluation and order allocation problem
112
with quantity discounts and lead time", Information
113
Sciences, 339(20), pp. 143-157 (2016).
114
29. Niaki, S.T.A., Taleizadeh, A. and Barzinpour, F.
115
Multiple-buyer multiple-vendor multi-product multiconstraint
116
supply chain problem with stochastic demand
117
and variable lead-time", Applied Mathematics
118
and Computation, 217, pp. 9234-9253 (2011).
119
30. Jiuping, Xu, Qiang, Liu, Rui Wang. A class of
120
multi-objective supply chain networks optimal model
121
under random fuzzy environment and its application to
122
the industry of Chinese liquor", Information Sciences,
123
178, pp. 2022-2043 (2008).
124
31. Kamran, S. and Moghaddam, K.S. Fuzzy multiobjective
125
model for supplier selection and order allocation
126
in reverse logistics systems under supply and demand
127
uncertainty", Expert Systems with Applications,
128
42(15-16), pp. 6237-6254 (2015).
129
32. Sodenkamp, M.A., Tavana, M. and Di Caprio, D.
130
Modeling synergies in multi-criteria supplier selection
131
and order allocation: An application to commodity
132
trading", European Journal of Operational Research,
133
254(3), pp. 859-874 (2016)
134
33. Mei, Y., Salim, F.D. and Li, X. Ecient metaheuristics
135
for the multi-objective time-dependent orienteering
136
problem", European Journal of Operational
137
Research, 254(2), pp. 443-457 (2016).
138
34. Ozcan-Deniz, G. and Zhu, Y. Multi-objective optimization
139
of greenhouse gas emissions in highway
140
construction projects", Sustainable Cities and Society,
141
28, pp. 162-171 (2017).
142
35. Hussain, M., Khan, M. and Al-Aomar, R. A framework
143
for supply chain sustainability in service industry
144
with conrmatory factor analysis", Renewable and
145
Sustainable Energy Reviews, 55, pp. 1301-1312 (2016).
146
36. Sadeghi, J., Mousavi, S.M., Niaki, S.T.A. and Sadeghi,
147
S. Optimizing a multi-vendor multi-retailer vendor
148
managed inventory problem: Two tuned metaheuristic
149
algorithms", Knowledge-Based Systems, 50,
150
pp. 159-170 (2013).
151
37. Lu, J., Han, J., Hu, Y. and Zhang, G. Multilevel
152
decision-making: A survey", Information Sciences,
153
346, pp. 463-487 (2016).
154
38. Lemmens, S., Decouttere, C., Vandaele, N. and
155
Bernuzzi, M. A review of integrated supply chain
156
network design models: Key issues for vaccine supply
157
chains", Chemical Engineering Research and Design,
158
109, pp. 366-384 (2016).
159
39. Zadeh, L. Fuzzy set as a basis for a theory of possibility",
160
Fuzzy Sets and Systems, 1, pp. 3-28 (1978).
161
40. Chen, C. Extensions of TOPSIS for group decision
162
making under fuzzy environment", Fuzzy Set and
163
System, 114, pp. 1-9 (2000).
164
A. Hajikhani et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 431{449 449
165
41. Ross, T.J., Fuzzy Logic with Engineering Applications,
166
John Willey & Sons (2005).
167
42. Deb, K., Pratap, A., Agarwal, S. and Meyarivan,
168
T.A.M.T. A fast and elitist multiobjective genetic
169
algorithm: NSGA-II", IEEE Transactions on Evolutionary
170
Computation, 6(2), pp. 182-197 (2002).
171
43. Coello Coello, Carlos, Lamont, Gary B., van Veldhuizen,
172
David A., Evolutionary Algorithms for Solving
173
Multi-Objective Problems, New York: Kluwer Academic
174
Publishers (2002).
175
44. Eberhart, R. and Kennedy, J. A new optimizer using
176
particle swarm theory", In Proceedings of the Sixth
177
International Symposium on Micro and Machine and
178
Human Science, 47, pp. 39-43 (1995).
179
45. Boyd, R. and Richerson, P.J., Culture and the Evolutionary
180
Process, University of Chicago Press, Chicago
181
46. Boeringer, D.W. and Werner, D.H. Particle swarm
182
optimization versus genetic algorithms for phased array
183
synthesis", IEEE Trans. Antennas Propag., pp.
184
771-779 (2004).
185
47. Hajipour, V. and Pasandideh, S.H.R. Proposing an
186
adaptive particle swarm optimization for a novel biobjective
187
queuing facility location model", Economic
188
Computation and Economic Cybernetics Studies and
189
Research, 47(3), pp. 112-129 (2012).
190
48. Szidarovszky, F., Gersbon, M.E. and Duckstein, L.,
191
Techniques for Multiobjective Decision Making in Systems
192
Management, Elsevier Publishers B.V. (1985).
193
49. Boloori Arabani, A., Zandieh, M. and Fatemi Ghomi,
194
S. Multi-objective genetic-based algorithms for a
195
cross-docking scheduling problem", Applied Soft Computing,
196
pp. 4954-4970 (2011).
197
ORIGINAL_ARTICLE
Two-dimensional uncertain linguistic generalized normalized weighted geometric Bonferroni mean and its application to multiple-attribute decision making
2-dimension uncertain linguistic variables (2DULVs) are a powerful tool to express the fuzzy or uncertain information, and the weighted Bonferroni mean can not only take the attribute importance into account but also capture the interrelationship between the attributes. However, the traditional Bonferroni mean can only deal with the crisp numbers. In this paper, Bonferroni mean was extended to process the 2DULVs. Firstly, we proposed the normalized weighted geometric Bonferroni mean (NWGBM) operator and the generalized normalized weighted geometric Bonferroni mean (GNWGBM) operator, which have the characteristics of reducibility and also consider the interrelationships between two attributes. Then we introduced the computation rules, characteristics, the expected value and comparison method of the 2DULVs. Further, we developed the 2-dimension uncertain linguistic normalized weighted geometric Bonferroni mean (2DULNWGBM) and the 2-dimension uncertain linguistic generalized normalized weighted geometric Bonferroni mean (2DULGNWGBM), and explored some properties and discussed some special cases of them. Finally, we developed a new decision making method based on these operators, and an example is given to compare with the existing methods.
http://scientiairanica.sharif.edu/article_4402_b8051d22f0c212ce9e22c1458fc7b312.pdf
2018-02-01
450
465
10.24200/sci.2017.4402
Aggregation operators
Multiple attribute decision making
Bonferroni mean
2-dimension uncertain linguistic variables
Peide
Liu
peide.liu@gmail.com
1
School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan Shandong 250014, P.R. of China
LEAD_AUTHOR
References
1
1. Chopra, S. and Meindl, P., Supply Chain Management: Strategy, Planning and Operations, New Jersey: Prentice Hall (2007).
2
2. Melo, M.T., Nickel, S. and Saldanha-da-Gama F. Facility location and supply chain management -a review", European Journal of Operation Research, 196, pp. 401-412 (2009).
3
3. Simchi-Levi, D., Kaminsky, P. and Simchi-Levi, E.,
4
Designing and Managing the Supply Chain: Concepts,
5
Strategies, and Cases, New York: McGraw-Hill (1999).
6
4. Altiparmak, F., Gen, M. Lin, L. and Paksoy, T.
7
A genetic algorithm approach for multi-objective
8
optimization of supply chain networks", Computers &
9
Industrial Engineering, 51, pp. 197-216 (2006).
10
5. Zanjirani Farahani, R., Rezapour, S., Drezner, T.
11
and Fallah, S. Competitive supply chain network
12
design: An overview of classications, models, solution
13
techniques and applications", Omega, 45, pp. 92-118
14
6. Fattahi, M., Mahootchi, M., Govindan, K. and Moattar
15
Husseini, S.M. Dynamic supply chain network
16
design with capacity planning and multi-period pricing",
17
Transportation Research Part E, 81, pp. 169-202
18
7. Ahi, P. and Searcy, C. An analysis of metrics used to
19
measure performance in green and sustainable supply
20
chains", Journal of Cleaner Production, 86, pp. 360-
21
377 (2015).
22
8. Zhang, S. On a prot maximizing location model",
23
Annual of Operations Research, 103(1), pp. 251-260
24
9. Shen, J.M. A prot-maximizing supply chain network
25
design model with demand choice
26
exibility", Operations
27
Research Letters, 34(6), pp. 673-682 (2006).
28
10. Nagurney, A. and Nagurney, L.S. Sustainable supply
29
chain network design: a multi-criteria perspective", International
30
Journal of Sustainable Engineering, 3(3),
31
pp. 189-197 (2010).
32
11. Pishvaee, M.S., Razmi, J. and Torabi, S.A. Robust
33
possibilistic programming for socially responsible supply
34
chain network design: A new approach", Fuzzy Sets
35
and Systems, 206, pp. 1-20 (2012).
36
12. Badri, H., Bashiri, M. and Hejazi, T.H. Integrated
37
strategic and tactical planning in a supply chain
38
network design with a heuristic solution method",
39
Computers & Operations Research, 40(4), pp. 1143-
40
1154 (2013).
41
13. Mota, B., Gomes, M.I., Carvalho, A. and Barbosa-
42
Povoa, A.P. Towards supply chain sustainability: economic,
43
environmental and social design and planning",
44
Journal of Cleaner Production, 105, pp. 14-27 (2014).
45
14. Ahmadi-Javid, A. and Ghandali, R. An ecient
46
optimization procedure for designing a capacitated
47
distribution network with price-sensitive demand",
48
Optimization and Engineering, 15(3), pp. 801-817
49
15. Ahmadi-Javid, A. and Hoseinpour, P. Incorporating
50
location, inventory and price decisions into a supply
51
chain distribution network design problem", Computers
52
& Operations Research, 56, pp. 110-119 (2015).
53
16. Govindan, K., Jafarian, A. and Nourbakhsh, V. Biobjective
54
integrating sustainable order allocation and
55
sustainable supply chain network strategic design with
56
stochastic demand using a novel robust hybrid multiobjective
57
metaheuristic", Computers & Operations
58
Research, 62, pp. 112-130 (2015b).
59
17. Aras, N., Aksen, D. and Gonul Tanugur, A. Locating
60
collection centers for incentive-dependent returns
61
under a pick-up policy with capacitated vehicles",
62
European Journal of Operational Research, 191(3), pp.
63
1223-1240 (2008).
64
18. Aras, N. and Aksen, D. Locating collection centers
65
for distance- and incentive-dependent returns", International
66
Journal of Production Economics, 111(2), pp.
67
316-333 (2008).
68
19. Dehghanian, F. and Mansour, S. Designing sustainable
69
recovery network of end-of-life products using
70
genetic algorithm", Resources, Conservation and Recycling,
71
53, pp. 559-570 (2009).
72
20. Tuzkaya, G., Gulsun, B., and Onsel, S. A methodology
73
for the strategic design of reverse logistics networks
74
and its application in the Turkish white goods industry",
75
International Journal of Production Research,
76
49(15), pp. 4543-4571 (2011).
77
21. Neto, J.Q.F., Bloemhof-Ruwaard, J., van Nunen, J.
78
and van Heck, E. Designing and evaluating sustainable
79
logistics networks", International Journal of
80
Production Economics, 111(2), pp. 195-208 (1998).
81
22. Shi, J., Zhang, G. and Sha, J. Optimal production
82
and pricing policy for a closed loop system", Resources,
83
Conservation and Recycling, 55, pp. 639-647 (2011a).
84
A. Nobari and A. Kheirkhah/Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 410{430 429
85
23. Shi, J., Zhang, G. and Sha, J. Optimal production
86
planning for a multi-product closed loop system with
87
uncertain demand and return", Computers & Operation
88
Researches, 38(3), pp. 641-650 (2011b).
89
24. Chaabane, A., Ramudhin, A. and Paquet, M. Design
90
of sustainable supply chains under the emission
91
trading scheme", International Journal of Production
92
Economics, 135(1), pp. 37-49 (2012).
93
25. Pishvaee, M.S. and Razmi, J. Environmental supply
94
chain network design using multi-objective fuzzy
95
mathematical programming", Applied Mathematical
96
Modelling, 36, pp. 3433-3446 (2012).
97
26. Keyvanshokooh, E., Fattahi, M., Seyed-Hosseini, S.M.
98
and Tavakkoli-Moghaddam, R. A dynamic pricing
99
approach for returned products in integrated forward/
100
reverse logistics network design", Applied Mathematical
101
Modelling, 37, pp. 10182-10202 (2013).
102
27. Devika, K., Jafarian, A. and Nourbakhsh, V. Designing
103
a sustainable closed-loop supply chain network
104
based on triple bottom line approach: A comparison
105
of metaheuristics hybridization techniques", European
106
Journal of Operational Research, 235, pp. 594-615
107
28. Kaya, O. and Urek, B. A mixed integer nonlinear programming
108
model and heuristic solutions for location,
109
inventory and pricing decisions in a closed loop supply
110
chain", Computers & Operations Research, 65, pp. 93-
111
103 (2015).
112
29. Dubey, R., Gunasekaran, A. and Childe S.J. The
113
design of a responsive sustainable supply chain network
114
under uncertainty", International Journal of Advanced
115
Manufacturing Technology, 80(1), pp. 427-445 (2015).
116
30. Accorsi, R., Manzini, R., Pini, C. and Penazzi, S. On
117
the design of closed-loop networks for product life cycle
118
management: Economic, environmental and geography
119
considerations", Journal of Transport Geography,
120
48, pp. 121-134 (2015).
121
31. Govindan, K., Soleimani, H. and Kannan, D. Reverse
122
logistics and closed-loop supply chain: A comprehensive
123
review to explore the future", European Journal
124
of Operational Research, 240, pp. 603-626 (2015a).
125
32. Elkington, J., Cannibals with Forks: The Triple Bottom
126
Line of 21st Century Business (The Conscientious
127
Commerce Series) (1998).
128
33. Brundtland, G.H., Our Common Future, Oxford paperbacks
129
34. Seuring, S. A review of modeling approaches for sustainable
130
supply chain management", Decision Support
131
Systems, 54(4), pp. 1513-1520 (2013).
132
35. Eskandarpour, M., Dejax, P., Miemczyk, J. and Peton,
133
O. Sustainable supply chain network design: an
134
optimization-oriented review", Omega, 54, pp. 11-32
135
36. Thanh, P.N., Bostel, N. and Peton, O. A dynamic
136
model for facility location in the design of complex
137
supply chains", International Journal of Production
138
Economic, 113(2), pp. 678-693 (2008).
139
37. Shen, Z.J. Integrated supply chain design models:
140
a survey and future research directions", Journal of
141
Industrial and Management Optimization, 3(1), pp. 1-
142
27 (2007).
143
38. Salema, M.I.G., Barbosa-Povoa, A.P. and Novais, A.Q.
144
Simultaneous design and planning of supply chains
145
with reverse
146
ows: a generic modelling framework",
147
European Journal of Operation Research, 203(2), pp.
148
336-349 (2010).
149
39. Huang, J., Leng, M. and Parlar, M. Demand function
150
in decision modeling: a comprehensive survey and
151
research directions", Decision Sciences, 44(3), pp. 557-
152
609 (2013).
153
40. Liu, Z., Qiu, T. and Chen, B. A study of the LCA
154
based biofuel supply chain multi-objective optimization
155
model with multi-conversion paths in China",
156
Applied Energy, 126, pp. 221-234 (2014).
157
41. GRI, Global Reporting Initiative - G4 Sustainability
158
Reporting Guidelines, Amsterdam (2013).
159
42. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T.
160
A fast and elitist multi-objective genetic algorithm:
161
NSGA-II", IEEE Transactions on Evolutionary Computation,
162
6, pp. 182-197 (2002).
163
43. Atashpaz-Gargari E. and Lucas C. Imperialist competitive
164
algorithm: An algorithm for optimization
165
inspired by imperialist competition", IEEE Congress
166
on Evolutionary Computation (2007).
167
44. Enayatifar, R., Youse, M., Abdullah, A.H. and
168
Darus, A.N. MOICA: A novel multi-objective approach
169
based on imperialist competitive algorithm",
170
Applied Mathematics and Computation, 219, pp. 8829-
171
8841 (2013).
172
45. Srinivas, N. and Deb, K. Multi-objective function
173
optimization using non-dominated sorting genetic algorithms",
174
Evolutionary Computation, 2(3), pp. 221-
175
248 (1995).
176
46. Zitzler, E. and Thiele L. Multi-objective optimization
177
using evolutionary algorithms a comparative case
178
study", Fifth International Conference on Parallel
179
Problem Solving from Nature (PPSN-V), Berlin, Germany,
180
pp. 292-301 (1998).
181
47. Zitzler, E. Evolutionary Algorithms for multiobjective
182
optimization: method and applications",
183
Ph.D. Thesis, Swaziland Federal Institute of Technology
184
Zorikh, Switzerland (1999).
185
48. Mousavi, M., Hajipour, V., Niaki, S.T.A. and Aalikar,
186
N. Optimizing multi-item multi-period inventory control
187
system with discounted cash
188
ation approaches:
189
Two calibrated meta-heuristic algorithms",
190
Applied Mathematical Modelling, 37, pp. 2241-2256
191
49. Hajipour, V., Rahmati, S.H.A., Pasandideh, S.H.R.
192
and Niaki S.T.A. A multi-objective harmony search
193
algorithm to optimize multi-server location-allocation
194
problem in congested systems", Computers & Industrial
195
Engineering, 72, pp. 187-197 (2014).
196
430 A. Nobari and A. Kheirkhah/Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 410{430
197
50. Fattahi, P., Hajipour, V. and Nobari, A. A biobjective
198
continuous review inventory control model:
199
Pareto-based metaheuristic algorithms", Applied Soft
200
Computing, 32, pp. 211-223 (2015).
201
51. MATLAB Version 7.10.0.499 (R2010a). The Math-
202
Works, Inc. Protected by U.S. and International
203
Patents (2010).
204
ORIGINAL_ARTICLE
Prioritized averaging/geometric aggregation operators under the intuitionistic fuzzy soft set environment
Soft set theory acts as a fundamental tool for handling the uncertainty in the data by adding aparameterizations factor during the process as compared to fuzzy and intuitionistic fuzzy set theory.In the present manuscript, the work has been done under the intuitionistic fuzzy soft sets (IFSSs)environment and proposed some new averaging/geometric prioritized aggregation operators in whichthe preferences related to attributes are taken in form of IFSSs. Desirable properties of its have alsobeen investigated. Furthermore, based on these operators, an approach to investigate the multi-criteria decision making (MCDM) problem has been presented. The eectiveness of these operatorshas been demonstrated through a case study.
http://scientiairanica.sharif.edu/article_4410_c7f1e5aa3c69044064c037825329e859.pdf
2018-02-01
466
482
10.24200/sci.2017.4410
MCDM
IFSS
Aggregation operator
Decision-Making
Rishu
Arora
1
School of Mathematics, Thapar University Patiala 147004, Punjab, India
AUTHOR
Harish
Garg
harishg58iitr@gmail.com
2
School of Mathematics, Thapar University Patiala 147004, Punjab, India
LEAD_AUTHOR
References
1
1. Garg, H., Agarwal, N. and Tripathi, A. Some improved interactive aggregation operators under
2
interval-valued intuitionistic fuzzy environment and its application to decision making process", Scientia Iranica,
3
Transactions E: Industrial Engineering, 24(5), pp. 2581-2604 (2017).
4
2. Zadeh, L.A. Fuzzy sets", Inform. and Control, 8, pp. 338-353 (1965).
5
3. Attanassov, K.T. Intuitionistic fuzzy sets", Fuzzy Sets and Systems, 20, pp. 87-96 (1986).
6
4. Atanassov, K. and Gargov, G. Interval-valued intuitionistic
7
fuzzy sets", Fuzzy Sets and Systems, 31, pp. 343-349 (1989).
8
5. Xu, Z.S. Intuitionistic fuzzy aggregation operators"
9
IEEE Trans. of Fuzzy System, 15, pp. 1179-1187
10
6. Xu, Z.S. and Yager, R.R. Some geometric aggregation
11
operators based on intuitionistic fuzzy sets", Int. J.
12
Gen. Syst., 35, pp. 417-433 (2006).
13
7. Wang, W.Z. and Liu, X.W. Intuitionistic fuzzy geometric
14
aggregation operators based on Einstein operations",
15
Int. J. of Intell. Syst, 26, pp. 1049-1075 (2011).
16
8. Wang, W. and Liu, X. Intuitionistic fuzzy information
17
aggregation using Einstein operations", IEEE Trans.
18
Fuzzy Systems, 20(5), pp. 923-938 (2012).
19
9. Garg, H. Generalized intuitionistic fuzzy interactive
20
geometric interaction operators using Einstein t-norm
21
and t-conorm and their application to decision making",
22
Comput. Ind. Eng, 101, pp. 53-69 (2016).
23
10. Garg, H., Agarwal, N. and Tripathi, A. Entropy
24
based multi-criteria decision making method under
25
fuzzy environment and unknown attribute weights",
26
Global Journal of Technology and Optimization, 6, pp.
27
13-20 (2015).
28
11. Garg, H. A new generalized improved score function
29
of interval-valued intuitionistic fuzzy sets and applications
30
in expert systems", Appl. Soft Comput., 38, pp.
31
988-999 (2016).
32
12. Verma, R. and Sharma, B. Intuitionistic fuzzy Einstein
33
prioritized weighted average operators and their
34
application to multiple attribute group decision making",
35
Applied Mathematics & Information Sciences,
36
9(6), pp. 3095-3107 (2015).
37
13. Xu, Z. and Chen, J. Approach to group decision making
38
based on interval valued intuitionistic judgment
39
matrices", Systems Engineering - Theory and Practice,
40
27(4), pp. 126-133 (2007).
41
14. Xu, Z.S. Methods for aggregating interval-valued
42
intuitionistic fuzzy information and their application
43
to decision making", Control and Decision, 22(2), pp.
44
215-219 (2007).
45
15. Wei, G. Some induced geometric aggregation operators
46
with intuitionistic fuzzy information and their
47
application to group decision making", Appl. Soft
48
Comput., 10, pp. 423-431 (2010).
49
16. Garg, H. A novel accuracy function under intervalvalued
50
pythagorean fuzzy environment for solving
51
multicriteria decision making problem", Journal of
52
Intelligent and Fuzzy Systems, 31(1), pp. 529-540
53
17. Garg, H. A novel correlation coecients between
54
pythagorean fuzzy sets and its applications to decisionmaking
55
processes", Inter. Intell. Syst, 31(12), pp.
56
1234-1253 (2016).
57
18. Garg, H. A new generalized pythagorean fuzzy information
58
aggregation using Einstein operations and
59
its application to decision making", Inter. Intell. Syst,
60
31(9), pp. 886-920 (2016).
61
19. Garg, H. Generalized intuitionistic fuzzy multiplicative
62
interactive geometric operators and their application
63
to multiple criteria decision making", Inter. J. of
64
Mach. Learn. Cybernet, 7(6), pp. 1075-1092 (2016).
65
20. Wei, G.W. and Merigo, J.M. Methods for strategic
66
decision-making problems with immediate probabiliR.
67
Arora and H. Garg/Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 466{482 481
68
ties in intuitionistic fuzzy setting", Scientia Iranica,
69
19(6), pp. 1936-1946 (2012).
70
21. Garg, H. Some series of intuitionistic fuzzy interactive
71
averaging aggregation operators", SpringerPlus, 5(1),
72
pp. 1-27 (2016). DOI: 10.1186/s40064-016-2591-9
73
22. Wei, G.W. Some arithmetic aggregation operators
74
with intuitionistic trapezoidal fuzzy numbers and their
75
application to group decision making", Journal Comput.,
76
5(3), pp. 345-351 (2010).
77
23. Wei, G. and Zhao, X. An approach to multiple
78
attribute decision making with combined weight information
79
in interval-valued intuitionistic fuzzy environmental",
80
Control Cybernet., 41(1), pp. 97-112 (2012).
81
24. Molodtsov, D. Soft set theory - rst results", Computer
82
and Mathematics with Applications, 27(4-5), pp.
83
19-31 (1999).
84
25. Maji, P.K., Biswas, R. and Roy, A.R. Soft set theory",
85
Computers and Mathematics with Applications, 45(4-
86
5), pp. 555-562 (2003).
87
26. Ali, M., Feng, F., Liu, X., Min, W. and Shabir, M. On
88
some new operations in soft set theory", Computer and
89
Mathematics with Applications, 57(9), pp. 1547-1553
90
27. Maji, P.K., Biswas, R. and Roy, A.R. Fuzzy soft sets",
91
Journal Fuzzy Math., 9(3), pp. 589-602 (2001).
92
28. Maji, P.K., Biswas, R. and Roy, A. Intuitionistic
93
fuzzy soft sets", Journal Fuzzy Math., 9(3), pp. 677-
94
692 (2001).
95
29. Cagman, N. and Deli, I. Intuitionistic fuzzy parameterized
96
soft set theory and its decision making", Appl.
97
Soft. Comput., 28, pp. 109-113 (2015).
98
30. Alkhazaleh, S. and Salleh, A.R. Fuzzy soft expert set
99
and its application", Appl. Math., 5, pp. 1349-1368
100
31. Yang, H.L. Notes on generalized fuzzy soft sets",
101
Journal of Mathematical Research and Exposition,
102
31(3), pp. 567-570 (2011).
103
32. Majumdar, P. and Samanta, S.K. Generalized fuzzy
104
soft sets", Computers and Mathematics with Applications,
105
59(4), pp. 1425-1432 (2010).
106
33. Agarwal, M., Biswas, K.K. and Hanmandlu, M. Generalized
107
intuitionistic fuzzy soft sets with applications
108
in decision-making", Appl. Soft Comput., 13, pp. 3552-
109
3566 (2013).
110
34. Majumdar, P. and Samanta, S. Similarity measure
111
of soft sets", New Math. Nat. Comput, 4(1), pp. 1{12
112
35. Feng, Q. and Zheng, W. New similarity measures
113
of fuzzy soft sets based on distance measures", Ann.
114
Fuzzy Math. Inform., 7(4), pp. 669-686 (2014).
115
36. Mukherjee, A. and Sarkar, S. Similarity measures
116
for interval-valued intuitionistic fuzzy soft sets and its
117
application in medical diagnosis problem", New Trends
118
Math. Sci., 2(3), pp. 159-165 (2014).
119
37. Khalid, A. and Abbas, M. Distance measures and
120
operations in intuitionistic and interval-valued intuitionistic
121
fuzzy soft set theory", Int. J. Fuzzy Syst.,
122
17(3), pp. 490-497 (2015).
123
38. Liu, Z., Qin, K. and Pei, Z. Similarity measure
124
and entropy of fuzzy soft sets", The Scientic World
125
Journal, 2014 Article ID 161607, 10 pages (2014).
126
39. Garg, H., Agarwal, N. and Tripathi, A. Fuzzy number
127
intuitionistic fuzzy soft sets and its properties", Journal
128
of Fuzzy Set Valued Analysis, 2016(3), pp. 196-213
129
40. Feng, F., Juna, Y.B., Liu, X. and Li, L. An adjustable
130
approach to fuzzy soft set based decision making", J.
131
Comput. Appl. Math., 234(1), pp. 10-20 (2010).
132
41. Peng, X. and Yang, Y. Algorithms for interval-valued
133
fuzzy soft sets in stochastic multi-criteria decision
134
making based on regret theory and prospect theory
135
with combined weight", Appl. Soft Comput., 54, pp.
136
415-430 (2017).
137
42. Yager, R.R. Prioritized aggregation operators", Internat.
138
J. Approx. Reason., 48(1), pp. 263-274, (2008).
139
43. Roy, A.R. and Maji, P.K. A fuzzy soft set theoretic
140
approach to decision making problems", J. Comput.
141
Appl. Math., 203(2), pp. 412-418 (2007).
142
ORIGINAL_ARTICLE
Efficiency assessment of Iranian Handmade Carpet Company by network DEA
Different categories of Iranian handmade carpet are produced each year. Since of resource limitation, it is so important for managers to allocate more resources to the most efficient categories. So the main purpose of this illustration is to consider most efficient types of carpet in production and sales stages. To do so, different categories of Iranian handmade carpet are considered as DMUs. This study utilizes network DEA for constructing a model to analyze total and partial efficiency of Iranian Handmade Carpet Company (IHCC) simultaneously. IHCC consists of three main departments that are working jointly to maximize productivity of the firm; therefore, the case of IHCC is a multi-stage system with shared intermediate variables, extra inputs to the second stage and undesired outputs. The novelty of this paper is the methodology used for calculating the efficiency which is based on multi-objective programming. Results of experimental data of IHCC is summarized in order to prepare some brilliant management strategies based on partial and total efficiency scores of different carpet categories. Since the lack of familiar researches in the area of carpet industry efficiency measurement, this research will provide valuable information for decision makers.
http://scientiairanica.sharif.edu/article_20006_188f5670707b362ac58d91ab4d8e1759.pdf
2018-02-01
483
491
10.24200/sci.2017.20006
Data envelopment analysis
Efficiency
Multistage
Multi-objective
Network
Undesired outputs
S. H.
Zegordi
1
Industrial Engineering Dept., School of Engineering, Tarbiat Modares University, Al-Ahmad Ave., Tehran, Iran
LEAD_AUTHOR
A.
Omid
2
Industrial Engineering Dept., School of Engineering, Tarbiat Modares University, Al-Ahmad Ave., Tehran, Iran
AUTHOR
References
1
1. Charnes, A., Cooper, W.W. and Rhodes, E. Measuring the eciency of decision making units", European Journal of Operational Research, 2, pp. 429-444 (1978).
2
2. Cook, W.D. and Seiford, L.M. Data envelopment analysis (DEA)-thirty years on", European Journal of Operational Research, 192, pp. 1-17 (2009).
3
3. Emrouznejad, A., Parker, B.R. and Tavares, G. Evaluation
4
of research in eciency and productivity: A survey and analysis of the rst 30 years of scholarly literature
5
in DEA", Socio-Economic Planning Sciences,
6
42, pp. 151-157 (2008).
7
4. Liu, J.S., Lu, L.Y., Lu, W.-M. and Lin, B.J. A survey
8
of DEA applications", Omega, 41, pp. 893-902 (2013).
9
5. Liu, J.S., Lu, L.Y., Lu, W.-M. and Lin, B.J. Data
10
envelopment analysis 1978-2010: A citation-based literature
11
survey", Omega, 41, pp. 3-15 (2013).
12
6. Zhou, P., Ang, B.W. and Poh, K.-L. A survey of data
13
envelopment analysis in energy and environmental
14
studies", European Journal of Operational Research,
15
189, pp. 1-18 (2008).
16
7. Liu, J.S., Lu, L.Y.Y., Lu, W.-M. and Lin, B.J.Y.
17
Data envelopment analysis 1978-2010: A citationbased
18
literature survey", Omega, 41, pp. 3-15 (2013).
19
8. Fare, R. and Grosskopf, S. Network DEA", Socio-
20
Economic Planning Sciences, 34, pp. 35-49 (2000).
21
S.H. Zegordi and A. Omid/Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 483{491 491
22
9. Kao, C. and Hwang, S.-N. Eciency decomposition in
23
two-stage data envelopment analysis: An application
24
to non-life insurance companies in Taiwan", European
25
Journal of Operational Research, 185, pp. 418-429
26
10. Kao, C. and Hwang, S.-N. Eciency measurement
27
for network systems: IT impact on rm performance",
28
Decision Support Systems, 48, pp. 437-446 (2010).
29
11. Kao, C. Network data envelopment analysis: A
30
review", European Journal of Operational Research,
31
239, pp. 1-16 (2014).
32
12. Cook, W.D., Liang, L. and Zhu, J. Measuring performance
33
of two-stage network structures by DEA: A
34
review and future perspective", Omega, 38, pp. 423-
35
430 (2010a).
36
13. Tone, K. and Tsutsui, M. Network DEA: a slacksbased
37
measure approach", European Journal of Operational
38
Research, 197, pp. 243-252 (2009).
39
14. Kao, C. A linear formulation of the two-level DEA
40
model", Omega, 36, pp. 958-962 (2008).
41
15. Kao, C. Eciency decomposition in network data
42
envelopment analysis: A relational model", European
43
Journal of Operational Research, 192, pp. 949-962
44
16. Hsieh, L.-F. and Lin, L.-H. A performance evaluation
45
model for international tourist hotels in Taiwan-An
46
application of the relational network DEA", International
47
Journal of Hospitality Management, 29, pp. 14-
48
24 (2010).
49
17. Liu, J.S. and Lu, W.-M. DEA and ranking with the
50
network-based approach: a case of R&D performance",
51
Omega, 38, pp. 453-464 (2010).
52
18. Li, Y., Chen, Y., Liang, L. and Xie, J. DEA models
53
for extended two-stage network structures", Omega,
54
40, pp. 611-618 (2012).
55
19. Liu, W., Zhou, Z., Ma, C., Liu, D. and Shen,
56
W. Two-stage DEA models with undesirable inputintermediate-
57
outputs", Omega, 56, pp. 74-87 (2015).
58
20. Matin, R.K. and Azizi, R. A unied network-DEA
59
model for performance measurement of production
60
systems", Measurement, 60, pp. 186-193 (2015).
61
21. Chen, Y., Du, J., David Sherman, H. and Zhu, J.
62
DEA model with shared resources and eciency
63
decomposition", European Journal of Operational Research,
64
207, pp. 339-349 (2010).
65
22. Cook, W.D., Zhu, J., Bi, G. and Yang, F. Network
66
DEA: Additive eciency decomposition", European
67
Journal of Operational Research, 207, pp. 1122-1129
68
23. Kao, C. and Llu, S.-T. Multi-period eciency measurement
69
in data envelopment analysis: The case of
70
Taiwanese commercial banks", Omega, 47, pp. 90-98
71
24. Kao, H.-Y., Chan, C.-Y. and Wu, D.-J. A multiobjective
72
programming method for solving network
73
DEA", Applied Soft Computing, 24, pp. 406-413
74
25. Despotis, D.K., Koronakos, G. and Sotiros, D. A
75
multi-objective programming approach to network
76
DEA with an application to the assessment of the academic
77
research activity", Procedia Computer Science,
78
55, pp. 370-379 (2015).
79
26. Liang, L., Cook, W.D. and Zhu, J. DEA models for
80
two-stage processes: Game approach and eciency
81
decomposition", Naval Research Logistics (NRL), 55,
82
pp. 643-653 (2008).
83
27. Zimmermann, H.-J. Fuzzy programming and linear
84
programming with several objective functions", Fuzzy
85
Sets and Systems, 1, pp. 45-55 (1978).
86
28. Bellman, R.E. and Zadeh, L.A. Decision-making in
87
a fuzzy environment", Management Science, 17, pp.
88
B-141-B-164 (1970).
89
29. Li, R.J. Multiple objective decision making in a fuzzy
90
environment", Ph.D. Thesis, Department of Industrial
91
Engineering, Kansas State University, Manhattan, US
92
30. Shahnazari-Shahrezaei, P., Tavakkoli-Moghaddam, R.
93
and Kazemipoor, H. Solving a multi-objective multiskilled
94
manpower scheduling model by a fuzzy goal
95
programming approach", Applied Mathematical Modelling,
96
37, pp. 5424-5443 (2013).
97
31. Charnes, A. and Cooper, W.W. Programming with
98
linear fractional functionals", Naval Research Logistics
99
Quarterly, 9, pp. 181-186 (1962).
100