A flexible cell scheduling problem with automated guided vehicles and robots under energy-conscious policy

Document Type : Article

Authors

1 Department of Industrial Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial & Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran

Abstract

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.
 

Keywords

Main Subjects


References
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),
pp. 1511-1530 (1989).
2. Suer, G.A. An algorithm to nd the number of parallel stations for optimal cell scheduling", Computers &
Industrial Engineering, 23(1-4), pp. 81-84 (1992).

3. Selim, H.M., Askin, R.G., and Vakharia, A.J. Cell
formation in group technology: Review, evaluation, and directions for future research", Computers &
Industrial Engineering, 34(1), pp. 3-20 (1998).
4. Yu, J.J., Sun, S.D., Si, S.B., Yang, H.G., and Wu, X.L.
A study on the aviation manufacture cell scheduling
based on adaptive ant colony algorithm", Materials
Science Forum, 532-533, pp. 1060-1063 (2007).
5. Saravanan, M. and Haq, A.N. A scatter search
method to minimize make-span of cell scheduling
problem", International Journal of Agile Systems and
Management, 3(1-2), pp. 18-36 (2008).
6. Li, D.N., Wang, Y., and Xiao, G.X. Dynamic parts
scheduling in multiple job shop cells considering intercell
moves and
exible routes", Computers & Operations
Research, 40(5), pp. 2007-2023 (2013).
7. Tang, J.F., Wang, X., Kaku, I., and Yung, K.L.
Optimization of parts scheduling in multiple cells considering
intercell move using scatter search approach",
Journal of Intelligent Manufacturing, 21(4), pp. 525-
537 (2010).
8. Schaller, J. A comparison of heuristics for family
and job scheduling in a
ow-line manufacturing cell",
International Journal of Production Research, 38(2),
pp. 287-308 (2000).
9. Hendizadeh, H., Faramarzi, H., Mansouri, S.A.,
Gupta, J.N.D., and Elmekkawy, T.Y. Meta-heuristics
for scheduling a
ow shop manufacturing cell with
sequence dependent family setup times", International
Journal of Production Economics, 111(2), pp. 593-605
(2008).
10. Lin, S.W., Ying, K.C., Lu, C.C., and Gupta, J.N.D.
Applying multi-start simulated annealing to schedule
a
ow line manufacturing cell with sequence dependent
family setup times", International Journal of Production
Economics, 130(2), pp. 246-254 (2011).
11. Nagarjuna, N., Mahesh, O., and Rajagopal, K. A
heuristic based on multi-stage programming approach
for machine loading problem in a
exible manufacturing
system", Robotics and Computer Integrated
Manufacturing, 22, pp. 342-352 (2006).
12. Saravanan, M. and Noorul Haq, A.N. Evaluation of
scatter-search approach for scheduling optimization of

exible manufacturing systems", International Journal
of Advanced Manufacturing Technology, 38(5), pp.
978-986 (2008).
13. Blazevicz, J., Eiselt, H.A., Finke, G., Laporte, G., and
Weglarz, J. Scheduling tasks and vehicles in a
exible
manufacturing system", International Journal of
Flexible Manufacturing System, 4(1), pp. 5-16 (1991).
14. Tanchoco, J.M.A. and Sinriech, D. OSL-optimal
single-loop guide paths for AGVS", International Journal
of Production Research, 30(3), pp. 665-681 (1992).
15. Sinriech, D. and Tanchoco, J.M.A. The centroid
projection method for locating pick-up and delivery
stations in single-loop AGV systems", Journal of
Manufacturing Systems, 11(4), pp. 297-307 (1992).
16. Lengerke, O., Campos, A.M.V., Dutra, M.S., and
Pinto, F.D.N.C. Trajectories and simulation model
of AGVs with trailers", ABCM Symposium Series in
Mechatronics, 4, pp. 509-518 (2010).
17. The Cadmus Group, "Regional lectricity emission
factors nal report" (1998).
18. Ulusoy, G., Sivrikaya-Serifoglus, F., and Bilge, U.
A genetic algorithm approach to the simultaneous
scheduling of machines and automated guided vehicles",
Computers & Operations Research, 24(4), pp.
335-351 (1997).
19. Ali A. Pouyan, Heydar Toossian Shandiz, Soheil
Arastehfar Synthesis a Petri net based control model
for a FMS cell", Computers in Industry, 62, pp. 501-
508 (2011).
20. Pach, C., Bekrar, A., Zbib, N., Sallez, Y. and Trentesaux,
D. An e ective potential eld approach to FMS
holonic heterarchical control", Control Engineering
Practice, 20, pp. 1293-1309 (2012).
21. Leit~ao, P. and Restivo, F. ADACOR: A holonic architecture
for agile and adaptive manufacturing control",
Computers in Industry, 57(2), pp. 121-130 (2006).
22. Abazari, A.M., Solimanpur, M., and Sattari, H. Optimum
loading of machines in a
exible manufacturing
356 M. Hemmati Far et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 339{358
system using a mixed-integer linear mathematical programming
model and genetic algorithm", Computers
& Industrial Engineering, 62, pp. 469-478 (2012).
23. Pach, C., Berger, T., Bonte, T., and Trentesaux, D.
ORCA-FMS: a dynamic architecture for the optimized
and reactive control of
exible manufacturing
scheduling", Computers in Industry, 65(4), pp. 706-
720 (2014).
24. Balaji, A.N. and Porselvi, S. Arti cial immune system
algorithm and simulated annealing algorithm for
scheduling batches of parts based on job availability
model in a multi-cell
exible manufacturing system",
Procedia Engineering, 97, pp. 1524-1533 (2014).
25. Erdin, E.M. and Atmaca, A. Implementation of an
overall design of a
exible manufacturing system",
Procedia Technology, 19, pp. 185-192 (2015).
26. He, Y., Stecke, K., and Smith, M. Robot and machine
scheduling with state-dependent part input sequencing
in
exible manufacturing systems", International
Journal of Production Research (2016).
DOI: 10.1080/00207543.2016.1161252
27. Solimanpur, M., Vrat, P., and Shankar, R. A heuristic
to minimize make-span of cell scheduling problem",
International Journal of Production Economics, 88(3),
pp. 231-241 (2004).
28. Logendran, R., Mai, L., and Talkington, D. Combined
heuristics for bi-level group scheduling problems",
International Journal of Production Economics, 38,
pp. 133-145 (1995).
29. Venkataramanaiah, S. Scheduling in cellular manufacturing
systems: a heuristic approach", International
Journal of Production Research, 46(2), pp. 429-449
(2008).
30. Tavakkoli-Moghaddam, R., Javadian, N., Khorrami,
A., and Gholipour-Kanani, Y. Design of a scatter
search method for a novel multi-criteria group scheduling
problem in a cellular manufacturing system",
Expert Systems with Applications, 37, pp. 2661-2669
(2009).
31. Lin, S.W., Ying, K.C., and Lee, Z.J. Meta-heuristics
for scheduling a non- permutation
ow line manufacturing
cell with sequence dependent family setup
times", Computers and Operations Research, 36, pp.
1110-1121 (2009).
32. Shirazi, B., Fazlollahtabar, H., and Mahdavi, I. A six
sigma based multi-objective optimization for machine
grouping control in
exible cellular manufacturing
systems with guide-path
exibility", Advances in Engineering
Software, 41, pp. 865-873 (2010).
33. Saadettin Erhan Kesen, Sanchoy K. Das, Zulal Gungor
A genetic algorithm based heuristic for scheduling of
virtual manufacturing cells (VMCs)", Computers &
Operations Research, 37, pp. 1148-1156 (2010).
34. Lin, S.W., Ying, K.C., Lu, C.C., and Gupta, J.N.D.
Applying multi-start simulated annealing to schedule
a
ow line manufacturing cell with sequence dependent
family setup times", International Journal of Production
Economics, 130(2), pp. 246-254 (2011).
35. Kia, R., Baboli, A., Javadian, N., Tavakkoli-
Moghaddam, R., Kazemi, M., and Khorrami, J.
Solving a group layout design model of a dynamic
cellular manufacturing system with alternative process
routings, lot splitting and
exible recon guration by
simulated annealing", Computers & Operations Research,
39, pp. 2642-2658 (2012).
36. Batur, G.D., Karasan, O.E., and Akturk, M.S. Multiple
part-type scheduling in
exible robotic cells", International
Journal of Production Economics, 135(2),
pp. 726-740 (2012).
37. Izui, K., Murakumo, Y., Suemitsu, I., Nishiwaki, S.,
Noda, A., and Nagatani, T. Multiobjective layout optimization
of robotic cellular manufacturing Systems",
Computers & Industrial Engineering, 64, pp. 537-544
(2013).
38. Boutsinas, B. Machine-part cell formation using biclustering",
European Journal of Operational Research,
230, pp. 563-572 (2013).
39. Fazlollahtabar, H. and Jalali Naini, S.G. Adapted
Markovian model to control reliability assessment in
multiple AGV manufacturing system", Journal of
Scientia Iranica, 20(6), pp. 2224-2237 (2013).
40. Yan, C.Z.J.T.C. Job-shop cell-scheduling problem
with inter-cell moves and automated guided vehicles",
Journal of Intelligent Manufacturing, 26, pp. 845-859
(2014).
41. Forghani, K. and Mohammadi, M. A genetic algorithm
for solving integrated cell formation and layout
problem considering alternative routings and machine
capacities", Journal of Scientia Iranica, 21(6), pp.
2326-2346 (2014).
42. Zhang, H., Zhao, F., Fang, K., and Sutherland, J.
Energy-conscious
ow shop scheduling under time-ofuse
electricity tari s", CIRP Annals - Manufacturing
Technology, 63, pp. 37-40 (2014).
43. Li, Y., Li, X., and Gupta, J. Solving the multiobjective

ow line manufacturing cell scheduling problem
by hybrid harmony search", Expert Systems with
Applications, 42, pp. 1409-1417 (2015).
44. Zohrevand, A.M., Ra ei, H., and Zohrevand, A.H.
Multi-objective dynamic cell formation problem: A
stochastic programming approach", Journal of Computers
& Industrial Engineering, 98, pp. 323-332
(2016).
45. Majumder, A. and Laha, D. A new cuckoo search
algorithm for 2-machine robotic cell scheduling problem
with sequence-dependent setup times", Journal of
Swarm and Evolutionary Computation, 28, pp. 131-
143 (2016).
46. Gultekin, H., Akturk, M.S., and Karasan, O.E. Bicriteria
robotic operation allocation in a
exible manufacturing
cell", Computers & Operations Research, 37,
pp. 779-789 (2010).
47. Tuysuz, F. and Kahraman, C. Modeling a
exible
manufacturing cell using stochastic Petri nets with
fuzzy parameters", Expert Systems with Applications,
37, pp. 3910-3920 (2010).
M. Hemmati Far et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 339{358 357
48. Naderi, B. and Azab, A. Modeling and scheduling
a
exible manufacturing cell with parallel processing
capability", CIRP Journal of Manufacturing Science
and Technology, 11, pp. 18-27 (2015).
49. Yang, Y., Chen, Y., and Long, C. Flexible robotic
manufacturing cell scheduling problem with multiple
robots", International Journal of Production Research,
pp. 1-14 (2016)
50. Logendran, R., Carson, S., and Hanson, E. Group
scheduling in
exible
ow shops", International Journal
of Production Economics, 96, pp. 143-155 (2004).
51. Logendran, R., de Szoeke, P., and Barnard, F.
Sequence-dependent group scheduling problems in

exible
ow shops", International Journal of Production
Economics, 102, pp. 66-86 (2005).
52. Salmasi, N., Logendran, R., and Skandari, M. Total

ow time minimization in a
ow shop sequencedependent
group scheduling problem", Computers and
Operations Research, 37, pp. 199-212 (2010).
53. Bruzzone, A.A.G., Anghinol , D., Paolucci, M., and
Tonelli, F. Energy-aware scheduling for improving
manufacturing process sustainability: A mathematical
model for
exible
ow shops", CIRP Annals - Manufacturing
Technology, 61, pp. 459-462 (2012).
54. Mozdgir, A., Fatemi Ghomi, S.M.T., Jolai, F., and
Navaei, J. Three meta-heuristics to solve the nowait
two-stage assembly
ow-shop scheduling problem",
Journal of Scientia Iranica, 20(6), pp. 2275-2283
(2013).
55. Jolai, F. and Abedinnia, H. Consideration of transportation
lags in a two-machine Flow shop scheduling
problem", Journal of Scientia Iranica, 20(6), pp. 2215-
2223 (2013).
56. Jolai, F., Tavakkoli-Moghaddam, R., Rabiee, M., and
Gheisariha, E. An enhanced invasive weed optimization
for make-span minimization in a
exible
ow
shop scheduling problem", Journal of Scientia Iranica,
21(3), pp. 1007-1020 (2014).
57. Seidgar, H., Zandieh, M., and Mahdavi, I. Biobjective
optimization for integrating production and
preventive maintenance scheduling in two-stage assembly

ow shop problem", Journal of Industrial and
Production Engineering, 33(6), pp. 404-425 (2016).
58. Fang, K., Uhan, N., Zhao, F., and Sutherland, J.
A new approach to scheduling in manufacturing for
power consumption and carbon footprint reduction",
Journal of Manufacturing Systems, 30, pp. 234-240
(2011).
59. Dai, M., Tang, D., Giret, A., Salido, M., and Li,
W.D. Energy-ecient scheduling for a
exible
ow
shop using an improved genetic-simulated annealing
algorithm", Robotics and Computer-Integrated Manufacturing,
29, pp. 418-429 (2013).
60. Zhang, R., Chang, P.C., and Wu, C. A hybrid
genetic algorithm for the job shop scheduling problem
with practical considerations for manufacturing costs:
Investigations motivated by vehicle production", International
Journal of Production Economics, 145(1),
pp. 38-52 (2013).
61. Nourali, S. and Imanipour, N. A particle swarm
optimization-based algorithm for
exible assembly job
shop scheduling problem with sequence dependent
setup times", Journal of Scientia Iranica, 21(3), pp.
1021-1033 (2014).
62. Yazdani, M., Zandieh, M., Tavakkoli-Moghaddam, R.,
and Jolai, F. Two meta-heuristic algorithms for the
dual-resource constrained
exible job-shop scheduling
problem", Journal of Scientia Iranica, 22(3), pp. 1242-
1257 (2015).
63. Zhang, J., Yang, J., and Zhou, Y. Robust scheduling
for multi-objective
exible job shop Problems with

exible workdays", Journal of Engineering Optimization,
48(11), pp. 1973-1989 (2016).
64. Hamta, N., Fatemi Ghomi, S.M.T., Tavakkoli-
Moghaddam, R., and Jolai, F. A hybrid metaheuristic
for balancing and scheduling assembly lines
with sequence-independent setup times by considering
deterioration tasks and learning e ect", Journal of
Scientia Iranica, 21(3), pp. 963-979 (2014).
65. Khalili, S., Mohammadzade, H., and Fallahnezhad,
M.S. A new approach based on queuing theory for
solving the assembly line balancing problem using
fuzzy prioritization techniques", Journal of Scientia
Iranica, 23(1), pp. 387-398 (2016).
66. Kumar, A., Prakash, Tiwari, M.K., Shankar, R., and
Baveja, A. Solving machine-loading problem of a

exible manufacturing system with constraint-based
genetic algorithm", European Journal of Operational
Research, 175, pp. 1043-1069 (2006).
67. Mukhopadhyay, S.K., Maiti, B., and Garg, S. Heuristic
solution to the scheduling problem in
exible manufacturing
system", International Journal of Production
Research, 29, pp. 2003-2024 (1991).
68. Mukhopadhyay, S.K., Midha, S., and Krishna, V.M.
A heuristic procedure for loading problems in
exible
manufacturing systems", International Journal of
Production Research, 30, pp. 2213-2228 (1992).
69. Mukhopadhyay, S.K., Singh, M.K., and Srivastava,
R. FMS loading: a simulated annealing approach",
International Journal of Production Research, 36, pp.
1629-1647 (1998).
70. Moreno, A.A. and Ding, F.Y. Heuristic for the FMS
loading and part type selection problems", International
Journal of Flexible Manufacturing Systems,
5(1), pp. 287-300 (1993).
71. Shanker, K. and Srinivasulu, A. Some solution
methodologies for loading problems in
exible manufacturing
system", International Journal of Production
Research, 27(6), pp. 1019-1034 (1989).
72. Shankar, K. and Tzen, Y.J. A loading and dispatching
problem in a random
exible manufacturing system",
International Journal of Production Research, 16, pp.
383-393 (1985).
358 M. Hemmati Far et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 339{358
73. Diabat, A. Hybrid algorithm for a vendor managed
inventory system in a two-echelon supply chain", European
Journal of Operational Research, 238, pp. 114-
121 (2014).
74. Sadeghi, J., Mousavi, S.M., Niaki, S.T.A., and
Sadeghi, S. Optimizing a multi-vendor multi-retailer
vendor managed inventory problem: Two tuned metaheuristic
algorithms", Knowledge-Based Systems, 50,
pp. 159-170 (2013).
75. Chen, G., Govindan, K., and Yang, Z. A method
to reduce truck queuing at terminal gates: managing
truck arrivals with vessel-dependent time windows",
International Journal of Production Economic,
141(1), pp. 179-188 (2013).
76. Sue-Ann, G., Ponnambalam, S.G., and Jawahar, N.
Evolutionary algorithms for optimal operating parameters
of vendor managed inventory systems in
a two-echelon supply chain", Advances Engineering.
Software, 52, pp. 47-54 (2012).
77. Pasandideh, S.H.R. and Niaki, S.T.A. A genetic
algorithm approach to optimize a multi-products EPQ
model with discrete delivery orders and constrained
space", Applied Mathematics and Computation, 195,
pp. 506-514 (2008).
78. Taleizadeh, A.A., Niaki, S.T.A., and Makui, A. Multiproduct
multiple-buyer single-vendor supply chain
problem with stochastic demand, variable lead-time,
and multi-chance constraint", Expert Systems with
Applications, 39, pp. 5338-5348 (2012).
79. Colorni, A., Dorigo, M., and Maniezzo, V. Distributed
optimization by ant colonies", Proceedings of
European Conference on Arti cial Life, Paris, France,
pp. 134-142 (1991).
80. Colorni, A., Dorigo, M., and Maniezzo, V. An investigation
of some properties of an ant algorithm", Proceedings
of the Parallel Problem Solving from Nature
Conference, Brussels, Belgium, pp. 509-520 (1992).
81. Colorni, A., Dorigo, M., and Maniezzo, V. Ant
system for job-shop scheduling", Belgian Journal of
Operations Research, Statistics and Computer Science,
34, pp. 39-53 (1994).
82. Yang, W.H. and Tarng, Y.S. Design optimization
of cutting parameters for turning operations based
on Taguchi method", Journal of Materials Processing
Technology, 84, pp. 122-129 (1998).
83. Davidson, M.J., Balasubramanian, K., and Tagore,
G. Experimental investigation on
ow-forming of
AA6061 alloy a Taguchi approach", Journal of Materials
Processing Technology, 200, pp. 283-287 (2008).
84. Du Plessis, B.J. and De Villiers, G.H. The application
of the Taguchi method in the evaluation of mechanical

otation in waste activated sludge thickening", Resources,
Conservation and Recycling, 50, pp. 202-210
(2007).
85. Taguchi, G., Chowdhury, S., and Wu, Y., Taguchi's
Quality Engineering Handbook, Hoboken, NJ: Wiley.
Talbi, El-Ghazali, Meta-heuristics, New York: Wiley
(2005).
86. Khaw, J.F.C., Lim, B.S., and Lim, L.E.N. Optimal
design of neural networks using the Taguchi method",
Neurocomputing, 7, pp. 225-245 (1995).
87. Wu, Y. and Wu, A. Taguchi Methods for Robust Design,
New York: The American Society of Mechanical
Engineers (2000).
88. Cardenas-Barron, L.E., Trevino-Garza, G., and Wee,
H.M. A simple and better algorithm to solve the
vendor managed inventory control system of multiproduct
multi-constraint economic order quantity
model", Expert Systems with Applications, 39, pp.
3888-3895 (2012).