ORIGINAL_ARTICLE
Integrated bi-objective project selection and scheduling using Bayesian networks: A risk-based approach
This paper presents a novel formulation of the integrated bi-objective problem of project selection and scheduling. The first objective is to minimize the aggregated risk by evaluating the expected value of schedule delay and the second objective is to maximize the achieved benefit. To evaluate the expected aggregated impacts of risks, an objective function based on the Bayesian Networks is proposed. In the extant mathematical models of the joint problem of project selection and scheduling, projects are selected and scheduled without considering the risk network of the projects indicating the individual and interaction effects of risks impressing the duration of the activities.To solve the model, two solution approaches have been developed, one exact and one metaheuristic approach. Goal Programming method is used to optimally select and schedule projects. Since the problem is NP hard, an algorithm, named GPGA, which combines Goal Programming method and Genetic Algorithm is proposed. Finally, the efficiency of the proposed algorithm is assessed not only based on small size instances but also by generating and testing representative datasets of larger instances. The results of the computational experiments indicate that it has acceptable performance to handle large size and more realistic problems.
https://scientiairanica.sharif.edu/article_21387_752481dbca25e01e9beaeacbfc26bb24.pdf
2019-12-01
3695
3711
10.24200/sci.2019.21387
Project selection and scheduling
Risk analysis
Bayesian Networks
multi-objective programming
Genetic Algorithm
A.
Namazian
a.namazian@ut.ac.ir
1
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, P.O. Box 14155-6619, Iran.
LEAD_AUTHOR
S.
Haji Yakhchali
2
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, P.O. Box 14155-6619, Iran.
AUTHOR
M.
Rabbani
mrabani@ut.ac.ir
3
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, P.O. Box 14155-6619, Iran.
AUTHOR
References:
1
1. Tuli, B., Arindam, S., Bijan, S., and Kumar, S.S. "Introduction to soft-set theoretic solution of project selection problem", Benchmarking: An International Journal, 23(7), pp. 1643-1657 (2016).
2
2. Rathi, R., Khanduja, D., and Sharma, S.K. "A fuzzy MADM approach for project selection: a six sigma case study", Decision Science Letters, 5(2), p. 14 (2016).
3
3. Tahri, H. "Mathematical optimization methods: application in project portfolio management", Procedia - Social and Behavioral Sciences, 210, pp. 339-347 (2015).
4
4. Namazian, A. and Haji Yakhchali, S. "Modeling and solving project portfolio and contractor selection problem based on project scheduling under uncertainty", Procedia - Social and Behavioral Sciences, 226, pp. 35-42 (2016).
5
5. Badri, M.A., Davis, D., and Davis, D. "A comprehensive 0-1 goal programming model for project selection", International Journal of Project Management, 19(4), pp. 243-252 (2001).
6
6. Arratia M., N.M., Lopez I., F., Schaeffer, S.E., and Cruz-Reyes, L. "Static R&D project portfolio selection in public organizations", Decision Support Systems, 84, pp. 53-63 (2016).
7
7. Tavana, M., Keramatpour, M., Santos-Arteaga, F.J., and Ghorbaniane, E. "A fuzzy hybrid project portfolio selection method using data envelopment analysis, TOPSIS and integer programming", Expert Systems with Applications, 42(22), pp. 8432-8444 (2015).
8
8. Fatemeh, P. and Sameh Monir, E.-S. "Project selection using the combined approach of AHP and LP", Journal of Financial Management of Property and Construction, 21(1), pp. 39-53 (2016).
9
9. Kellenbrink, C. and Helber, S. "Scheduling resource constrained projects with a flexible project structure", European Journal of Operational Research, 246(2), pp. 379-391 (2015).
10
10. Ji, X. and Yao, K. "Uncertain project scheduling problem with resource constraints", Journal of Intelligent Manufacturing, 28(3), pp. 575-580 (2017).
11
11. Tofighian, A.A. and Naderi, B. "Modeling and solving the project selection and scheduling", Computers & Industrial Engineering, 83, pp. 30-38 (2015).
12
12. Doerner, K., Gutjahr, W.J., Hartl, R.F., Strauss, C., and Stummer, C. "Pareto ant colony optimization: A metaheuristic approach to multiobjective portfolio selection", Annals of Operations Research, 131(1), pp. 79-99 (2004).
13
13. Ghorbani, S. and Rabbani, M. "A new multi-objective algorithm for a project selection problem", Advances in Engineering Software, 40(1), pp. 9-14 (2009).
14
14. Medaglia, A.L., Graves, S.B., and Ringuest, J.L. "A multiobjective evolutionary approach for linearly constrained project selection under uncertainty", European Journal of Operational Research, 179(3), pp. 869-894 (2007).
15
15. Xiao, J., Ao, X.-T., and Tang, Y. "Solving software project scheduling problems with ant colony optimization", Computers & Operations Research, 40(1), pp. 33-46 (2013).
16
16. Wang, W.-X., Wang, X., Ge, X.-L., and Deng, L. "Multi-objective optimization model for multi-project scheduling on critical chain", Advances in Engineering Software, 68, pp. 33-39 (2014).
17
17. Perez, A., Quintanilla, S., Lino, P., and Valls, V. "A multi-objective approach for a project scheduling problem with due dates and temporal constraints infeasibilities", International Journal of Production Research, 52(13), pp. 3950-3965 (2014).
18
18. Minku, L.L., Sudholt, D., and Yao, X. "Improved evolutionary algorithm design for the project scheduling problem based on runtime analysis", IEEE Transactions on Software Engineering, 40(1), pp. 83-102 (2014).
19
19. Artigues, C., Leus, R., and Talla Nobibon, F. "Robust optimization for resource-constrained project scheduling with uncertain activity durations", Flexible Services and Manufacturing Journal, 25(1), pp. 175-205 (2013).
20
20. Suresh, M., Dutta, P., and Jain, K. "Resource constrained multi-project scheduling problem with resource transfer times", Asia-Pacific Journal of Operational Research, 32(06), p. 1550048 (2015).
21
21. Aminbakhsh, S., Gunduz, M., and Sonmez, R. "Safety risk assessment using analytic hierarchy process (AHP) during planning and budgeting of construction projects", Journal of Safety Research, 46, pp. 99- 105 (2013).
22
22. Dikmen, I., Birgonul, M.T., and Han, S. "Using fuzzy risk assessment to rate cost overrun risk in international construction projects", International Journal of Project Management, 25(5), pp. 494-505 (2007).
23
23. Shi-Ming, H., I-Chu, C., Shing-Han, L., and Ming- Tong, L. "Assessing risk in ERP projects: identify and prioritize the factors", Industrial Management & Data Systems, 104(8), pp. 681-688 (2004).
24
24. Kuo, Y.-C. and Lu, S.-T. "Using fuzzy multiple criteria decision making approach to enhance risk assessment for metropolitan construction projects", International Journal of Project Management, 31(4), pp. 602-614 (2013).
25
25. Rodriguez, A., Ortega, F., and Concepcion, R. "A method for the evaluation of risk in IT projects", Expert Systems with Applications, 45, pp. 273-285 (2016).
26
26. Zavadskas, E.K., Turskis, Z., and Tamosaitiene, J. "Risk assessment of construction projects", Journal of Civil Engineering and Management, 16(1), pp. 33-46 (2010).
27
27. Zeng, J., An, M., and Smith, N.J. "Application of a fuzzy based decision making methodology to construction project risk assessment", International Journal of Project Management, 25(6), pp. 589-600 (2007).
28
28. Cheng, M. and Lu, Y. "Developing a risk assessment method for complex pipe jacking construction projects", Automation in Construction, 58, pp. 48-59 (2015).
29
29. Jamshidi, A., Rahimi, S.A., Ait-kadi, D., Rebaiaia, M.L., and Ruiz, A. "Risk assessment in ERP projects using an integrated method", 3rd International Conference on Control, Engineering & Information Technology (CEIT), Tlemcen, Algeria, pp. 1-5 (2015).
30
30. Ching-Chow, Y., Wen-Tsaan, L., Ming-Yi, L., and Jui-Tang, H. "A study on applying FMEA to improving ERP introduction: An example of semiconductor related industries in Taiwan", International Journal of Quality & Reliability Management, 23(3), pp. 298-322 (2006).
31
31. Gierczak, M. "The quantitative risk assessment of MINI, MIDI and MAXI horizontal directional drilling projects applying fuzzy fault tree analysis", Tunnelling and Underground Space Technology, 43, pp. 67-77 (2014).
32
32. Hyun, K.-C., Min, S., Choi, H., Park, J., and Lee, I.-M. "Risk analysis using fault-tree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels", Tunnelling and Underground Space Technology, 49, pp. 121-129 (2015).
33
33. Liang, W., Hu, J., Zhang, L., Guo, C., and Lin, W. "Assessing and classifying risk of pipeline third-party interference based on fault tree and SOM", Engineering Applications of Artificial Intelligence, 25(3), pp. 594-608 (2012).
34
34. Zeng, Y. and Skibniewski, M.J. "Risk assessment for enterprise resource planning (ERP) system implementations: a fault tree analysis approach", Enterprise Information Systems, 7(3), pp. 332-353 (2013).
35
35. Pavlos, L. and Nick, F. "Risk and uncertainty in development: A critical evaluation of using the Monte Carlo simulation method as a decision tool in real estate development projects", Journal of Property Investment & Finance, 30(2), pp. 198-210 (2012).
36
36. Sadeghi, N., Fayek, A.R., and Pedrycz, W. "Fuzzy Monte Carlo simulation and risk assessment in construction", Computer-Aided Civil and Infrastructure Engineering, 25(4), pp. 238-252 (2010).
37
37. Chin, K.-S., Tang, D.-W., Yang, J.-B., Wong, S.Y., and Wang, H. "Assessing new product development project risk by Bayesian network with a systematic probability generation methodology", Expert Systems with Applications, 36(6), pp. 9879-9890 (2009).
38
38. Hu, Y., Zhang, X., Ngai, E.W.T., Cai, R., and Liu, M. "Software project risk analysis using Bayesian networks with causality constraints", Decision Support Systems, 56, pp. 439-449 (2013).
39
39. Luu, V.T., Kim, S.-Y., Tuan, N.V., and Ogunlana, S.O. "Quantifying schedule risk in construction projects using Bayesian belief networks", International Journal of Project Management, 27(1), pp. 39-50 (2009).
40
40. Leu, S.-S. and Chang, C.-M. "Bayesian-network-based safety risk assessment for steel construction projects", Accident Analysis & Prevention, 54, pp. 122-133 (2013).
41
41. Sousa, R.L. and Einstein, H.H. "Risk analysis during tunnel construction using Bayesian networks: Porto Metro case study", Tunnelling and Underground Space Technology, 27(1), pp. 86-100 (2012).
42
42. Nordgard, D.E. and Sand, K. "Application of Bayesian networks for risk analysis of MV air insulated switch operation", Reliability Engineering & System Safety, 95(12), pp. 1358-1366 (2010).
43
43. Tang, C., Yi, Y., Yang, Z., and Sun, J. "Risk analysis of emergent water pollution accidents based on a Bayesian network", Journal of Environmental Management, 165, pp. 199-205 (2016).
44
44. Shabarchin, O. and Tesfamariam, S. "Internal corrosion hazard assessment of oil & gas pipelines using Bayesian belief network model", Journal of Loss Prevention in the Process Industries, 40, pp. 479-495 (2016).
45
45. Khodakarami, V. and Abdi, A. "Project cost risk analysis: A Bayesian networks approach for modeling dependencies between cost items", International Journal of Project Management, 32(7), pp. 1233-1245 (2014).
46
46. Tripathy, B.B. and Biswal, M.P. "A zero-one goal programming approach for project selection", Journal of Information and Optimization Sciences, 28(4), pp. 619-626 (2007).
47
ORIGINAL_ARTICLE
An architectural solution for virtual computer integrated manufacturing systems using ISO standards
Nowadays, manufacturing environments are faced with globalization which urges new necessities for manufacturing systems. These necessities have been considered from different perspectives and Computer Integrated Manufacturing (CIM) is the most popular and effective one. However, considering rapid rate of manufacturing globalization, traditional and current CIM solutions can be criticized by major deficiencies like high complexity for resource allocation over the globe, global facility sharing, and absence of an efficient way to handle lifecycle issues. Recently, Virtual CIM (VCIM) has been introduced as an effective solution to extend the traditional CIM solutions. This paper has investigated recent researches in VCIM/CIM field considering the necessities of todays’ globalized manufacturing environment. The paper shows the lack of traditional and current CIM/VCIM solutions; then, proposes an effective solution to cover them. Because of the complexities in designing such systems, the paper exploits Axiomatic Design (AD) Theory as a promising tools in this field. This theory is applied for validation of the suggested architectural solution and verification of the implementational aspects. The implementation of the architectural solution is considered based on ISO standards. Finally, the results have approved the feasibility of the suggested solution for manufacturing system and its Implementation aspects.
https://scientiairanica.sharif.edu/article_20799_e8002dfb2f240e461a7319118d0c717d.pdf
2019-12-01
3712
3727
10.24200/sci.2018.20799
CIM (Computer Integrated Manufacturing)
VCIM (Virtual Computer Integrated Manufacturing)
Manufacturing System Architecture
Axiomatic Design (AD) Theory
ISO standards
J.
Delaram
jalal.delaram@ie.sharif.edu
1
Department of Industrial Engineering, Advanced Manufacturing Laboratory, Sharif University of Technology, Tehran, Iran
AUTHOR
O.
Fatahi Valilai
fvalilai@sharif.edu
2
Department of Industrial Engineering, Advanced Manufacturing Laboratory, Sharif University of Technology, Tehran, Iran
LEAD_AUTHOR
References:
1
1. Valilai, O.F. and Houshmand, M. "A collaborative and integrated platform to support distributed manufacturing system using a service-oriented approach based on cloud computing paradigm", Rob. and Com. Integ. Man., 29(1), pp. 110-127 (2013).
2
2. Koren, Y., The Global Manufacturing Revolution: Product-Process-Business Integration and Reconfigurable Systems, John Wiley & Sons, UK (2010).
3
3. Zhang, W. and Xie, S. "Agent technology for collaborative process planning: a review", The Int. J. of Adv. Man. Tech., 32(3), pp. 315-325 (2007).
4
4. Nagalingam, S.V. and Lin, G.C. "CIM-still the solution for manufacturing industry", Rob. and Com. Integ. Man., 24(3), pp. 332-344 (2008).
5
5. Houshmand, M. and Valilai, O.F. "LAYMOD: a layered and modular platform for CAx product data integration based on the modular architecture of the standard for exchange of product data", Int. J. of Com. Integ. Man., 25(6), pp. 473-487 (2012).
6
6. Valilai, O.F. and Houshmand, M. "INFELT STEP: An integrated and interoperable platform for collaborative CAD/CAPP/CAM/CNC machining systems based on STEP standard", Int. J. of Com. Integ. Man., 23(12), pp. 1095-1117 (2010).
7
7. Wang, X.V. and Xu, X.W. "An interoperable solution for cloud manufacturing", Rob. and Com. Integ. Man., 29(4), pp. 232-247 (2013).
8
8. Li, Q. "Applications integration in a hybrid cloud computing environment: modelling and platform", Ent. Info. Sys., 7(3), pp. 237-271 (2013).
9
9. Valilai, O.F. and Houshmand, M. "A platform for optimisation in distributed manufacturing enterprises based on cloud manufacturing paradigm", Int. J. of Com. Integ. Man., 27(11), pp. 1031-1054 (2014).
10
10. Houshmand, M. and Valilai, O.F. "A layered and modular platform to enable distributed CAx collaboration and support product data integration based on STEP standard", Int. J. of Com. Integ. Man., 26(8), pp. 731- 750 (2013).
11
11. Zhou, N. "Development of an agent based VCIM resource scheduling process for small and medium enterprises", Int. Ass. of Eng., 25(2), pp. 31-50 (2010).
12
12. Nagalingam, S.V. and Lin, G. "Latest developments in CIM", Rob. and Com. Integ. Man., 15(6), pp. 423-430 (1999).
13
13. Wang, D., Nagalingam, S., and Lin, G. "Development of an agent-based virtual CIM architecture for small to medium manufacturers", Rob. and Com. Integ. Man., 23, pp. 1-16 (2007).
14
14. Zhou, N., Nagalingam, S., and Lin, G. "Application of virtual CIM in small and medium manufacturing enterprises", Int. J. of Com. Integ. Man., 25(12), pp. 131-154 (2011).
15
15. Delaram, J. and Valilai, O.F. "Development of a novel solution to enable integration and interoperability for cloud manufacturing", Procedia CIRP, 52, pp. 6-11 (2016).
16
16. Scheer, A.W., CIM Computer Integrated Manufacturing: Computer Steered Industry, Springer Publishing Company (2012).
17
17. Rehg, J.A. and Kraebber, H.W., Computer-Integrated Manufacturing, Prentice Hall (2005).
18
18. McGaughey, R.E. and Roach, D. "Obstacles to computer integrated manufacturing success: a study of practitioner perceptions", Int. J. of Com. Integ. Man., 10(1), pp. 256-265 (1997).
19
19. Delaram, J. and Valilai, O.F. "A novel solution for manufacturing interoperability fulfillment using interoperability service providers", Procedia CIRP, 63, pp. 774-779 (2017).
20
20. Zhou, N. "Inside virtual CIM", Intel. Cont. and Com. Eng., 11(5), pp. 163-175 (2011).
21
21. Mitchell Jr, F., CIM Systems: An Introduction to Computer-Integrated Manufacturing, Prentice-Hall (1991) .
22
22. Groover, M.P., Automation, Production Systems, and Computer-Integrated Manufacturing, Prentice-Hall (2007).
23
23. Wang, X., Wong, T., and Wang, G. "An ontological intelligent agent platform to establish an ecological virtual enterprise", Exp. Sys. with App., 39(8), pp. 7050-7061 (2012).
24
24. Nagalingam, S.V. and Lin, G. "A unified approach towards CIM justification", Com. Integ. Man. Sys., 10(2), pp. 133-145 (1997).
25
25. Zhou, N. "Virtual CIM", Intel. Cont. and Com. Eng., 21(9), pp. 31-40 (2015).
26
26. Valilai, O.F. and Houshmand, M. "Depicting additive manufacturing from a global perspective; using cloud manufacturing paradigm for integration and collaboration", Pro. of the Inst. of Mech. Eng., Part B: J. of Eng. Man., 229(12), pp. 2216-2237 (2014).
27
27. Erenay, O., Hashemipour, M., and Kayaligil, S. "Virtual reality in requirement analysis for CIM system development suitable for SMEs", Int. J. of Pro. Res., 40(15), pp. 3693-3708 (2002).
28
28. Nagalingam, S., Lin, G., and Wang, D. "Resource scheduling for a virtual CIM system", Adv. Man., 10(2), pp. 269-294 (2007).
29
29. Zhou, N., Nagalingam, S., and Lin, G. "Application of virtual CIM in small and medium manufacturing enterprises", Int. J. of Pro. Res., 9(4), pp. 161-164 (2007).
30
30. Browne, J., The Extended Enterprise-Manufacturing and the Value Chain, Springer (1995).
31
31. Jagdev, H. and Browne, J. "The extended enterprisea context for manufacturing", Pro. Plan. and Cont., 9(3), pp. 216-229 (1998).
32
32. Martinez, M.T. "Virtual enterprise-organisation, evolution and control", Int. J. of Pro. Eco., 74(1), pp. 225-238 (2001).
33
33. Park, K.H. and Favrel, J. "Virtual enterpriseinformation system and networking solution", Com. and Ind. Eng., 37(1), pp. 441-444 (1999).
34
34. Liu, N., Li, X., and Shen, W. "Multi-granularity resource virtualization and sharing strategies in cloud manufacturing", J. of Net. and Com. App., 46, pp. 72-82 (2014).
35
35. Morariu, O., Borangiu, T., and Raileanu, S. "vMES: Virtualization aware manufacturing execution system", Comp. in Ind., 67(2), pp. 27-37 (2015).
36
36. Buyya, R., Vecchiola, C., and Selvi, S.T. "Virtualization, in mastering cloud computing", Comp. in Ind., 67(2), pp. 71-109 (2013).
37
37. Van Geenhuizen, M. and Nijkamp, P. "Knowledge virtualization and local connectedness among young globalized high-tech companies", Tech. Fore. and Soc. Chan., 79(7), pp. 1179-1191 (2012).
38
38. Camarinha-Matos, L.M. and Afsarmanesh, H. "A comprehensive modeling framework for collaborative networked organizations", J. of Intel. Man., 18(5), pp. 529-542 (2007).
39
39. Brown, E.A. "Reinventing government research and development: A status report on management initiatives and reinvention efforts at the Army Research Laboratory", J. of Net. and Com. App., 62(1), pp. 78-111 (1998).
40
40. Vassiliou, M. "The virtual research laboratory: taxonomy and analysis. in aerospace", Com. and Ind. Eng., 37(1), pp. 441-444 (1999).
41
41. Narula, R. "R&D collaboration by SMEs: new opportunities and limitations in the face of globalisation", Techno., 24(2), pp. 153-161 (2004).
42
42. Ross, J.W., Weill, P., and Robertson, D. "Enterprise architecture as strategy: Creating a foundation for business execution", Har. Bus. Rev., 65(3), pp. 34-44 (2006).
43
43. Browne, J. and Zhang, J. "Extended and virtual enterprises-similarities and differences", Int. J. of Ag. Man. Sys., 1(1), pp. 30-36 (1999).
44
44. Zhang, J., Chan, F., and Li, P. "Agent-and ORBAbased application integration platform for an agile manufacturing environment", The Int. J. of Adv. Man. Tech., 21(6), pp. 460-468 (2003).
45
45. Huang, B. "A framework for virtual enterprise control with the holonic manufacturing paradigm", Com. In Ind., 49(3), pp. 299-310 (2002).
46
46. Odrey, N.G. and Meji, G. "A re-configurable multiagent system architecture for error recovery in production systems", Rob. and Com. Integ. Man., 19(1), pp. 35-43 (2003).
47
47. Hernandez-Matias, J. "An integrated modelling framework to support manufacturing system diagnosis for continuous improvement", Rob. and Com. Iinteg. Man., 24(2), pp. 187-199 (2008).
48
48. Lin, C.P. and Jeng, M. "An expanded SEMATECH CIM framework for heterogeneous applications integration", Man. and Cyber., 36(1), pp. 76-90 (2006).
49
49. Nahm, Y.E. and Ishikawa, H. "A hybrid multi-agent system architecture for enterprise integration using computer networks", Rob. and Com. Integ. Man., 21(3), pp. 217-234 (2005).
50
50. Williamson, A. and Deasley, P. "Systems thinking and computer-integrated manufacturing", Sys. Prac Tech., 7(1), pp. 9-23 (1994).
51
51. Trappey, A.J., Liu, T.H., and Hwang, C.T. "Using EXPRESS data modeling technique for PCB assembly analysis", Com. In Ind., 34(1), pp. 111-123 (1997).
52
52. Delaram, J. and Valilai, O.F. "An architectural view to computer integrated manufacturing systems based on axiomatic design theory", Com. In Ind., 100, pp. 96-114 (2018).
53
53. Suh, N.P., The Principles of Design, Oxford University Press, UK (1990).
54
54. Kulak, O. and Kahraman, C. "Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process", Info. Sci., 170(2), pp. 191-210 (2005).
55
55. Carnevalli, J.A., Miguel, P.A.C., and Calarge, F.A. "Axiomatic design application for minimising the difficulties of QFD usage", Int. J. of Pro. Eco., 125(1), pp. 1-12 (2010).
56
56. Peck, J., Nightingale, D., and Kim, S.G. "Axiomatic approach for efficient healthcare system design and optimization", CIRP Ann. Man. Tech., 59(1), pp. 469- 472 (2010).
57
57. Linke, B.S. and Dornfeld, D.A. "Application of axiomatic design principles to identify more sustainable strategies for grinding", J. of Man. Sys., 52(4), pp. 49-72 (2012).
58
58. Cochran, D.S. "The application of axiomatic design and lean management principles in the scope of production system segmentation", Int. J. of Pro. Res., 38(6), pp. 1377-1396 (2000).
59
59. Valilai, O.F. and Houshmand, M. "A manufacturing ontology model to enable data integration services in cloud manufacturing using axiomatic design theory", in Cloud-Based Design and Manufacturing (CBDM): A Service-Oriented Product Development Paradigm for the 21st Century, Springer (2014).
60
60. Suh, N.P., Axiomatic Design: Advances and Applications, Oxford University Press, UK (2001).
61
61. Kim, S.J., Suh, N.P., and Kim, S.G. "Design of software systems based on axiomatic design", Rob. and Com. Integ. Man., 8(4), pp. 243-255 (1991).
62
62. Rechtin, E., Systems Architecting: Creating and Building Complex Systems, Prentice-Hall (2001).
63
63. Albano, L.D. and Suh, N.P. "Axiomatic design and concurrent engineering", Com. Aid. Des., 26(7), pp. 499-504 (1994).
64
64. Bae, S., Lee, J.M., and Chu, C.N. "Axiomatic design of automotive suspension systems", CIRP Ann. Man. Tech., 51(1), pp. 115-118 (2002).
65
65. Suh, N.P. "Designing-in of quality through axiomatic design", Rel, Trans., 44(2), pp. 256-264 (1995).
66
66. Goncalves-Coelho, A.M. and Mourao, A.J. "Axiomatic design as support for decision-making in a design for manufacturing context: A case study", Int. J. of Pro. Eco., 109(1), pp. 81-89 (2007).
67
67. Cebi, S. and Kahraman, C. "Indicator design for p passenger car using fuzzy axiomatic design principles", Exp. Sys. with App., 37(9), pp. 6470-6481 (2010).
68
68. Suh, N.P. "Axiomatic design of mechanical systems", J. of Mech. Des., 117(B), pp. 2-10 (1995).
69
69. Gebala, D.A. and Suh, N.P. "An application of axiomatic design", Res. in Eng. Des., 3(3), pp. 149-162 (1992).
70
70. Togay, C., Dogru, A.H., and Tanik, J.U. "Systematic component-oriented development with axiomatic design", J. of Sys. and Soft., 81(11), pp. 1803-1815 (2008).
71
71. Ferrer, I. "Methodology for capturing and formalizing DFM Knowledge", Rob. and Com. Integ. Man., 26(5), pp. 420-429 (2010).
72
72. Cebi, S., Celik, M., and Kahraman, C. "Structuring ship design project approval mechanism towards installation of operator-system interfaces via fuzzy axiomatic design principles", Info. Sci., 180(6), pp. 886-895 (2010).
73
73. Heo, G. and Lee, S.K. "Design evaluation of emergency core cooling systems using axiomatic design", Nuc. Eng. and Des., 237(1), pp. 38-46 (2007).
74
74. Thielman, J. "Evaluation and optimization of General Atomics' GT-MHR reactor cavity cooling system using an axiomatic design approach", Nuc. Eng. and Des., 235(13), pp. 1389-1402 (2005).
75
75. Yi, J.W. and Park, G.J. "Development of a design system for EPS cushioning package of a monitor using axiomatic design", Adv. in Eng. Soft., 36(4), pp. 273- 284 (2005).
76
76. Hirani, H. and Suh, N.P. "Bearing design using ultiobjective genetic algorithm and axiomatic design approaches", Tri. Int., 38(5), pp. 481-491 (2005).
77
77. Janthong, N., Brissaud, D., and Butdee, S. "Combining axiomatic design and case-based reasoning in an innovative design methodology of mechatronics products", CIRP J. of Man. Sci. and Tech., 2(4), pp. 226-239 (2010).
78
78. http://www.iso.org/iso/iso catalogue/catalogue tc/ catalogue detail.htm?csnumber=39926.
79
79. http://www.iso.org/iso/iso catalogue/catalogue tc/ catalogue detail.htm?csnumber=29556.
80
80. http://www.iso.org/iso/iso catalogue/catalogue tc/ catalogue detail.htm?csnumber=24020.
81
81. http://www.iso.org/iso/iso catalogue/catalogue tc/ catalogue detail.htm?csnumber=28777.
82
82. http://www.iso.org/iso/iso catalogue/catalogue tc/ catalogue detail.htm?csnumber=50417.
83
83. http://www.iso.org/iso/catalogue detail.htm? csnumber=46559.
84
84. http://www.iso.org/iso/iso catalogue/catalogue tc/ catalogue detail.htm?csnumber=30418.
85
85. http://www.iso.org/iso/iso catalogue/catalogue tc/ catalogue detail.htm?csnumber=31583.
86
86. http://www.iso.org/iso/catalogue detail.htm? csnumber=57308.
87
87. http://www.iso.org/iso/catalogue detail.htm? csnumber=54497.
88
ORIGINAL_ARTICLE
A tabu search algorithm for a multi-period bank branch location problem: A case study in a Turkish bank
Banks need to open new branches in new sites as a result of increase in the population, individual earnings and the growth in national economy. In this respect, opening new branches or reorganizing the locations of current branches is an important decision problem for banks to accomplish their strategic objectives. This paper presents a decision support method for multi-period bank branch location problems. Our aim is to find bank branch location based on transaction volume, distance between branches, and cost of opening and closing branches. The proposed method not only develops an Integer Program and a Tabu Search algorithm to find the exact places of branches but also presents a structuring method to identify the related criteria and their importance. We demonstrate the effectiveness of the method on random data. In the final stage, the method is applied in a Turkish bank’s branch location problem considering the current and possible places of the branches, availability of the data, and the bank’s strategies for a four-year strategic planning.
https://scientiairanica.sharif.edu/article_20493_133e34bb3c26437c2190418e7d11f93a.pdf
2019-12-01
3728
3746
10.24200/sci.2018.20493
Integer programming
decision support system
Tabu search
case study
banking
location
A.
Basar
1
Department of Industrial Engineering, Istanbul Technical University, Macka Campus 34357, Istanbul, Turkey
AUTHOR
O.
Kabak
kabak@itu.edu.tr
2
Department of Industrial Engineering, Istanbul Technical University, Macka Campus 34357, Istanbul, Turkey
LEAD_AUTHOR
Y. I.
Topcu
3
Department of Industrial Engineering, Istanbul Technical University, Macka Campus 34357, Istanbul, Turkey
AUTHOR
References:
1
1. Demirguc-Kunt, A. and Maksimovic, V. "Law, finance, and firm growth", J. Financ., 53, pp. 2107-2137 (1998).
2
2. Levine, R. and Zervos, S. "Stock markets, banks, and economic growth", Am. Econ. Rev., 88, pp. 537-558 (1998).
3
3. Turkish Banks Association, Banking and Sector Information (2017). http://www.tbb.org.tr/tr/banka-vesektor- bilgileri/ba nka-bilgileri/subeler/65, accessed 15 March 2017.
4
4. Retail Banker International, US Branch Numbers Fall for Fourth Year Running (2014). https:// dscqm8cqg6d5o.cloudfront.net/uploads/articles/pdfs/ mnetisgnefhmblsrdclkablzye rbioct13issue694 usbranches.pdf, accessed 24 April 2017.
5
5. Basar, A., Kabak, O., Topcu, Y.I., and Bozkaya, B. "Location analysis in banking: A new methodology and application for a Turkish bank", In: Eiselt, H.A. and Vladimir, M. (Eds), Applications of Location Analysis, Springer, pp. 25-54 (2014).
6
6. Rajagopalan, H.K., Saydam, C., and Xiao, J. "A multiperiod set covering location model for dynamic redeployment of ambulances", Comput. Oper. Res., 35, pp. 814-826 (2008).
7
7. Manandhar, R. and Tang, J.C.S. "The evaluation of bank branch performance using data envelopment analysis: a framework", Journal of High Technology Management Research, 13, pp. 1-17 (2002).
8
8. Cook, W.D., Seiford, L.M., and Zhu, J. "Models for performance benchmarking: Measuring the effect of ebusiness activities on banking performance", Omega, 32, pp. 313-322 (2004).
9
9. Camanho, A.S. and Dyson, R.G. "Cost efficiency measurement with price uncertainty: A DEA application to bank branch assessments", Eur. J. Oper. Res, 161, pp. 432-446 (2005).
10
10. Portela, M.C.A.S. and Thanassoulis, E. "Comparative efficiency analysis of Portuguese bank branches", Eur. J. Oper. Res., 177, pp.1275-1288 (2007).
11
11. Paradi, J.C. and Zhu, H. "A survey on bank branch efficiency and performance research with data envelopment analysis", Omega, 41(1), pp. 61-79 (2013).
12
12. Paradi, J.C., Min, E., and Yang, X. "Evaluating Canadian bank branch operational efficiency from staff allocation: A DEA approach", Management and Organizational Studies, 2(1), pp. 52-65 (2015).
13
13. LaPlante, A.E. and Paradi, J.C. "Evaluation of bank branch growth potential using data envelopment analysis", Omega, 52, pp. 33-41 (2015).
14
14. Basar, A., Catay, B., and Unluyurt, T. "A taxonomy for emergency service station location problem", Optim. Lett., 6(6), pp. 1147-1160 (2012).
15
15. Arabani, A.B. and Farahani, R.Z. "Facility location dynamics: An overview of classifications and applications", Comput. Ind. Eng., 62, pp. 408-420 (2012).
16
16. Miller, T.C., Friesz, T.L., Tobin, R.L., and Kwon, C. "Reaction function based dynamic location modelling in Stackelberg-Nash-Cournot competition", Netw. Spat. Econ., 7(1), pp. 77-97 (2007).
17
17. Hale, T.S. and Moberg, C.R. "Location science research: A review", Ann. Oper. Res., 123, pp. 21-35 (2003).
18
18. Klose, A. and Drexl, A. "Facility location models for distribution system design", Eur. J. Oper. Res., 162(1), pp. 4-29 (2005).
19
19. Melo, M.T., Nickel, S., and Saldanha-da-Gama, F. "Facility location and supply chain management-A review", Eur. J. Oper. Res., 196(2), pp. 401-412 (2009).
20
20. Wesolowky, G.O. and Truscott, W.G. "The multiperiod location-allocation problem with relocation of facilities", Manage. Sci., 22, pp. 57-65 (1975).
21
21. Schilling, D.A. "Dynamic location modeling for public sector facilities: A multi criteria approach", Decision Sci., 11, pp. 714-724 (1980).
22
22. Gunawardane, G. "Dynamic versions of set covering type public facility location problems", Eur. J. Oper. Res., 10(2), pp. 190-195 (1982).
23
23. Galvao, R.D. and Gonzalez, S.E. "A Lagrangean heuristic for the pk-median dynamic location problem", Eur. J. Oper. Res., 58, pp. 250-262 (1992).
24
24. Drezner, Z. "Dynamic facility location: The progressive p-median problem", Location Science, 3, pp. 1-7 (1995).
25
25. Chardaire, P., Sutter, A., and Costa, M.C. "Solving the dynamic facility location problem", Networks, 28, pp. 117-124 (1996).
26
26. Hormozi, A.M. and Khumawala, B.M. "An improved algorithm for solving a multi-period facility location problem", IIE Trans., 28(2), pp. 105-114 (1996).
27
27. Current, J., Ratick, S., and ReVelle, C. "Dynamic facility location when the total number of facilities is uncertain: A decision analysis approach", Eur. J. Oper. Res., 110, pp. 597-609 (1997).
28
28. Antunes, A. and Peeters, D. "A dynamic optimization model for school network planning", Socio. Econ. Plan. Sci., 34(2), pp. 101-120 (2000).
29
29. Antunes, A. and Peeters, D. "On solving complex multi-period location models using simulated annealing", Eur. J. Oper. Res., 130(1), pp. 190-201 (2001).
30
30. Canel, C., Khumawala, B.M., Law, J., and Loh, A. "An algorithm for the capacitated, multi-commodity multi-period facility location problem", Comput. Oper. Res., 28(5), pp. 411-427 (2001).
31
31. Dias, J., Captivo, M.E., and Climaco, J. "Efficient primal-dual heuristic for a dynamic location Problem", Comput. Oper. Res., 34, pp. 1800-1823 (2007).
32
32. Albareda-Sambola, M., Fernandez, E., Hinojosa, Y., and Puerto, J. "The multi-period incremental service facility location problem", Comput. Oper. Res., 36(5), pp. 1356-1375 (2009).
33
33. Basar, A., Catay, B., and Unluyurt, T. "A multi-period double coverage approach for locating the emergency medical service stations in Istanbul", J. Oper. Res. Soc., 62(4), pp. 627-637 (2011).
34
34. Torres-Soto, J.E. and Uster, H. "Dynamic-demand capacitated facility location problems with and without relocation", Int. J. Prod. Res., 49(13), pp. 3979-4005 (2011).
35
35. Sha, Y. and Huang, J. "The multi-period locationallocation problem of engineering emergency blood supply systems", Systems Engineering Procedia, 5, pp. 21-28 (2012).
36
36. Ghaderi, A. and Jabalameli, M.S. "Modeling the budget-constrained dynamic uncapacitated facility location-network design problem and solving it via two efficient heuristics: A case study of health care", Math. Comput. Model., 57(3), pp. 382-400 (2013).
37
37. Zarandi, M.H.F., Davari, S., and Sisakht, S.A.H. "The large-scale dynamic maximal covering location problem", Math. Comput. Model., 57(3), pp. 710-719 (2013).
38
38. Miskovic, S., Stanimirovic, Z., and Grujicic, I. "An efficient variable neighborhood search for solving a robust dynamic facility location problem in emergency service network", Electronic Notes in Discrete Mathematics, 47, pp. 261-268 (2015).
39
39. Megiddo, N. "Dynamic location problems", Ann. Oper. Res., 6(10), pp. 311-319 (1986).
40
40. Zanjirani Farahani, R., Abedian, M., and Sharahi, S. "Dynamic facility location problem", In: Facility Location: Concepts, Models, Algorithm and Case Studies, Springer (2009).
41
41. Da Gama, F.S. and Captivo, M.E. "A heuristic approach for the discrete dynamic location problem", Location Science, 6, pp. 211-223 (1998).
42
42. Clawson, C.J. "Fitting branch locations, performance standards, and marketing strategies to local conditions", J. Marketing, 38, pp. 8-14 (1974).
43
43. Boufounou, P.V. "Evaluating bank branch location and performance: A case study", Eur. J. Oper. Res., 87, pp. 389-402 (1995).
44
44. Ravallion, M. and Wodon, Q. "Banking on the poor? Branch location and nonfarm rural development in Bangladesh", Rev. Dev. Econ., 4, pp. 121-139 (2000).
45
45. Basar, A., Kabak, O., and Topcu, Y.I. "A new mathematical programming formulation for locating bank branches in Turkey", Proc. of XX EURO Working Group on Locational Analysis, (EWGLA), Ankara, Turkey: pp. 37-38 (2013).
46
46. Aggelopoulos, E. and Georgopoulos, A. "Bank branch efficiency under environmental change: a bootstrap DEA on monthly profit and loss accounting statements of Greek retail branches", Eur. J. Oper. Res., 261(3), pp. 1170-1188 (2017).
47
47. Cvetkoska, V. and Savic, G. "Efficiency of bank branches: empirical evidence from a two-phase research approach", Economic Research-Ekonomska Istra zivanja, 30(1), pp. 318-333 (2017).
48
48. Quaranta, A.G., Raffoni, A., and Visani, F. "A multidimensional approach to measuring bank branch efficiency", Eur. J. Oper. Res., 266(2), pp. 746-760 (2018).
49
49. Basar, A., Kabak, O., and Topcu, Y.I. "A decision support methodology for locating bank branches: A case study in Turkey", Int. J. Inf. Tech. Decis., 16(1), pp. 59-86 (2017).
50
50. Min, H. "A model based decision support system for locating banks", Inform. Manage., 17, pp. 207-215 (1989).
51
51. Cinar, N. "A decision support model for bank branch location selection", World Academy of Science, Engineering & Technology, 60, pp. 126-131 (2009).
52
52. Rahgan, S.H. and Mirzazadeh, A. "A new method in the location problem using fuzzy evidential reasoning", Engineering and Technology, 4(22), pp. 4636-4645 (2012).
53
53. Morrison, P.S. and O'Brien, R. "Bank branch closures in New Zealand: The application of a spatial interaction model", Appl. Geog., 21, pp. 301-330 (2001).
54
54. Gorener, A., Dincer, H., and Hacioglu, U. "Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for bank branch location selection", International Journal of Finance & Banking Studies, 2(2), pp. 41-52 (2016).
55
55. Min, H., and Melachrinoudis, E. "The threehierarchical location-allocation of banking facilities with risk and uncertainty", Int. T. Oper. Res., 8, pp. 381-401 (2001).
56
56. Miliotis, P., Dimopoulou, M., and Giannikos, I. "A hierarchical location model for locating bank branches in a competitive environment", Int. T. Oper. Res., 9, pp. 549-565 (2002).
57
57. Wang, Q., Batta, R., Bhadury, J., and Rump, C.M. "Budget constrained location problem with opening and closing of facilities", Comput. Oper. Res., 30, pp. 2047-2069 (2003).
58
58. Monteiro, M. and Fontes, D. "Locating and sizing bank-branches by opening, closing or maintaining facilities", Operat. Res. Proceed., pp. 303-308 (2005).
59
59. Zhang, L. and Rushton, G. "Optimizing the size and locations of facilities in competitive multi-site service systems", Comput. Oper. Res., 35, pp. 327-338 (2008).
60
60. Alexandris, G. and Giannikos, I. "A new model for maximal coverage exploiting GIS capabilities", Eur. J. Oper. Res., 202, pp. 328-338 (2010).
61
61. Xia, L., Yin, W., Dong, J., Wu, T., Xie, M., and Zhao, Y. "Hybrid nested partitions algorithm for banking facility location problems", IEEE T. Autom. Sci. Eng., 7(3), pp. 654-658 (2010).
62
62. Jablonsky, J., Fiala, P., Smirlis, Y., and Despotis, D.K. "DEA with interval data: An illustration using the evaluation of branches of a Czech bank", Cent. Eur. J. Oper. Res., 12, pp. 323-337 (2004).
63
63. Badri, M.A. "A combined AHP-GP model for quality control systems", Int. J. Prod. Econ., 72, pp. 27-40 (2001).
64
64. Saaty, T.L., The Analytic Hierarchy Process, New York, McGraw-Hill, Inc. (1980).
65
65. Ahsan, M.K. and Bartlema, J. "Monitoring healthcare performance by analytic hierarchy process: A developing-country perspective", Int. T. Oper. R., 11, pp. 465-478 (2004).
66
66. Tzeng, G.H., Teng, M.H., Chen, J.J., and Opricovic, S. "Multi criteria selection for a restaurant location in Taipei", Int. J. Hosp. Manag., 21(2), pp. 171-187 (2002).
67
67. Wu, C.R., Lin, C.T., and Chen, H.C. "Optimal selection of location for Taiwanese hospitals to ensure a competitive advantage by using the analytic hierarchy process and sensitivity analysis", Build. Environ., 42(3), pp. 1431-1444 (2007).
68
68. Fernandez, I. and Ruiz, M.C. "Descriptive model and evaluation system to locate sustainable industrial areas", J. Clean. Prod., 17(1), pp. 87-100 (2009).
69
69. Marianov, V., ReVelle, C.S., Facility Location, Berlin, Springer (1995).
70
70. Silva, G.C., Bahiense, L., Ochi, L.S., and Netto, P.O.B. "The dynamic space allocation problem: Applying hybrid GRASP and tabu search metaheuristics", Comput. Oper. Res., 39(3), pp. 671-677 (2012).
71
71. Naama, B., Bouzeboudja, H., and Allali, A. "Application of tabu search and genetic algorithm in minimize losses in power system. Using the B-coefficient method", Energy. Proced., 36, pp. 687-693 (2013).
72
72. Ros lon, J. and Zawistowski, J. "Construction projects' indicators improvement using selected metaheuristic algorithms", Procedia Engineer., 153, pp. 595-598 (2016).
73
73. Hamta, N., Fatemi Ghomi, S.M.T., Tavakkoli Moghaddam, R., and Jolai, F. "A hybrid meta-heuristic for balancing and scheduling assembly lines with sequenceindependent setup times by considering deterioration tasks and learning effect", Scientia Iranica, 21(3), pp. 963-979 (2014).
74
74. Imanipour, N. and Zegordi S.H.A.D. "A heuristic approach based on tabu search for early/tardy flexible job shop problems", Scientia Iranica, 13(1), pp. 1-13 (2006).
75
75. Glover, F., Taillard, E., and de Werra, D. "A user's guide to Tabu search", Ann. Oper. Res.-Special issue on Tabu search, 41(1-4), pp. 3-28 (1993).
76
76. Zhang, G., Habenicht, W. and Spiefi, W.E.L. "Improving the structure of deep frozen and chilled food chain with tabu search procedure", J. Food Eng., 60(1), pp. 67-79 (2003).
77
77. Grabowski, J. and Wodecki, M. "A very fast tabu search algorithm for the permutation flow shop problem with makespan criterion", Comput. Oper. Res., 31, pp. 1891-1909 (2004).
78
ORIGINAL_ARTICLE
A robust bi-level programming model for designing a closed-loop supply chain considering government's collection policy
This study aims in providing a new approach regarding design of a closed loop supply chain network through emphasizing on the impact of the environmental government policies based on a bi-level mixed integer linear programming model. Government is considered as a leader in the first level and tends to set a collection rate policy which leads to collect more used products in order to ensure a minimum distribution ratio to satisfy a minimum demands. In the second level, private sector is considered as a follower and tries to maximize its profit by designing its own closed loop supply chain network according to the government used products collection policy. A heuristic algorithm and an adaptive genetic algorithm based on enumeration method are proposed and their performances are evaluated through computational experiences. The comparison among numerical examples reveals that there is an obvious conflict between the government and CLSC goals. Moreover, it shows that this conflict should be considered and elaborated in uncertain environment by applying Min-Max regret scenario based robust optimization approach. The results show the necessity of using robust bi-level programming in closed loop supply chain network design under the governmental legislative decisions as a leader-follower configuration.
https://scientiairanica.sharif.edu/article_20609_c7d2f14998f74784bc0163d3e5916372.pdf
2019-12-01
3747
3764
10.24200/sci.2018.20609
Bi-level Programming
Closed-loop supply chain
Government regulations
Genetic Algorithm
robust optimization
Scenario
A.
Hassanpour
1
Department of Industrial Engineering, Faculty of Engineering and Technology, Alzahra University, Tehran, P.O. Box 199389373, Iran
AUTHOR
J.
Bagherinejad
jbagheri@alzahra.ac.ir
2
Department of Industrial Engineering, Faculty of Engineering and Technology, Alzahra University, Tehran, P.O. Box 199389373, Iran
LEAD_AUTHOR
M.
Bashiri
bashiri@shahed.ac.ir
3
Department of Industrial Engineering, Faculty of Engineering and Technology, Alzahra University, Tehran, P.O. Box 199389373, Iran
AUTHOR
References:
1
1. Altmann, M. and Bogaschewsky, R. "An environmentally conscious robust closed-loop supply chain design", Journal of Business Economics, 84, pp. 613- 637 (2014).
2
2. Zeballos, L.J., Mendez, C.A., Barbosa-Povoa, A.P., and Novais, A.Q. "Multi-period design and planning of closed-loop supply chains with uncertain supply and demand", Computers & Chemical Engineering, 66, pp. 151-164 (2014).
3
3. Ma, R., Yao, L., Jin, M., Ren, P., Lv, Z. "Robust environmental closed-loop supply chain design under uncertainty", Chaos, Solitons & Fractals, 89, pp. 195- 202 (2015).
4
4. Talaei, M., Farhang Moghaddam, B., Pishvaee, M.S., Bozorgi-Amiri, A., and Gholamnejad, S. "A robust fuzzy optimization model for carbon-efficient closedloop supply chain network design problem: A numerical illustration in electronics industry", Journal of Cleaner Production, 113, pp. 662-673 (2015).
5
5. Giri, B.C. and Sharma, S. "Optimal production policy for a closed-loop hybrid system with uncertain demand and return under supply disruption", Journal of Cleaner Production, 112, pp. 2015-2028 (2016).
6
6. Keyvanshokooh, E., Ryan, S.M., and Kabir, E. "Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated benders decomposition", European Journal of Operational Research, 249, pp.76-92 (2016).
7
7. Dutta, P., Das, D., Schultmann, F., and Frohling, M. "Design and planning of a closed-loop supply chain with three way recovery and buy-back offer", Journal of Cleaner Production, 135, pp. 604-619 (2016).
8
8. Zeballos, L.J. and Mendez, C.A. "Chapter 16 - managing risk in the design of product and closed-loop supply chain structure", In Computer Aided Chemical Engineering, Eds.: Mario R. Eden Mariano Martin and G. Chemmangattuvalappil Nishanth, 39, pp. 443-474 (2017).
9
9. Jeihoonian, M., Kazemi Zanjani, M., and Gendreau, M. "Closed-loop supply chain network design under uncertain quality status: case of durable products", International Journal of Production Economics, 183, pp. 470-486 (2017).
10
10. Huang, M., Yi, P., Guo, L., and Shi, T. "A modal interval based genetic algorithm for closed-loop supply chain network design under uncertainty", IFACPapersOnLine, 49, pp. 616-621 (2016).
11
11. Fareeduddin, M., Shokri, Z., Adnan, H., and Mujahid, N. "Multi-period planning of closed-loop supply chain with carbon policies under uncertainty", Transportation Research Part D: Transport and Environment, 51, pp. 146-172 (2017).
12
12. Safaei, A.S., Roozbeh, A., and Paydar, M.M. "A robust optimization model for the design of a cardboard closed-loop supply chain", Journal of Cleaner Production, 166, pp. 1154-1168 (2017).
13
13. Hassanzadeh Amin, S., Zhang, G., and Akhtar, P. "Effects of uncertainty on a tire closed-loop supply chain network", Expert Systems with Applications, 73, pp. 82-91 (2017).
14
14. Farrokh, M., Azar, A., Jandaghi, G., and Ahmadi, E. "A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty", Fuzzy Sets and Systems, 341, pp. 69-91 (2017).
15
15. Haddadsisakht, A. and Ryan, S.M. "Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax", International Journal of Production Economics, 195, pp. 118-131 (2018).
16
16. Govindan, K., Soleimani, H., and Kannan, D. "Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future", European Journal of Operational Research, 240(3), pp. 603-626 (2015).
17
17. Toffel, M.W. "Strategic management of product recovery", California Management Review, 46(2), pp. 120- 141 (2004).
18
18. European Parliament and council "Directive 2008/98/EC of the European parliament and of the council of 19 November 2008 on waste and repealing certain directives", (2008). ELI: http://data,europa.eu/eli/2008/98/oj.
19
19. Echefu, N. and Akpofure, E. "Environmental impact assessment in Nigeria: Regulatory background and procedural framework", UNEP EIA Training Resource Manual (2002).
20
20. Bracken, J. and McGill, J.T. "Mathematical programs with optimization problems in the constraints", Operations Research, 21(1), pp. 37-44 (1973).
21
21. Amouzegar, M.A. and Jacobsen, S.E. "A decision support system for regional hazardous waste management alternatives", Advances in Decision Sciences, 2(1), pp. 23-50 (1998).
22
22. Kulshreshtha, P. and Sarangi, S. ""No return, no refund": an analysis of deposit-refund systems", Journal of Economic Behavior & Organization, 46(4), pp. 379- 394 (2001).
23
23. Kara, B.Y. and Verter, V. "Designing a road network for hazardous materials transportation", Transportation Science, 38(2), pp. 188-196 (2004).
24
24. Sheu, J.-B., Chou, Y.-H., and Hu, C.-C. "An integrated logistics operational model for green-supply chain management", Transportation Research Part E: Logistics and Transportation Review, 41(4), pp. 287- 313 (2005).
25
25. Wojanowski, R., Verter, V., and Boyaci, T. "Retailcollection network design under deposit-refund", Computers & Operations Research, 34(2), pp. 324-345 (2007).
26
26. Erkut, E. and Gzara, F. "Solving the hazmat transport network design problem", Computers & Operations Research, 35(7), pp. 2234-2247 (2008).
27
27. de Figueiredo, J.N. and Mayerle, S.F. "Designing minimum-cost recycling collection networks with required throughput", Transportation Research Part E: Logistics and Transportation Review, 44(5), pp. 731- 752 (2008).
28
28. Mitra, S. and Webster, S. "Competition in remanufacturing and the effects of government subsidies", International Journal of Production Economics, 111(2), pp. 287-298 (2008).
29
29. Plambeck, E. and Wang, Q. "Effects of e-waste regulation on new product introduction", Management Science, 55(3), pp. 333-347 (2009).
30
30. Aksen, D., Aras, N., and Karaarslan, A.G. "Design and analysis of government subsidized collection systems for incentive-dependent returns", International Journal of Production Economics, 119(2), pp. 308-327 (2009).
31
31. Sheu, J.-B. and Chen, Y.J. "Impact of government financial intervention on competition among green supply chains", International Journal of Production Economics, 138(1), pp. 201-213 (2012).
32
32. Wang, W., Zhang, Y., Zhang, K., Bai, T., and Shang, J. "Reward-penalty mechanism for closed-loop supply chains under responsibility-sharing and different power structures", International Journal of Production Economics, 170, pp. 178-190 (2015).
33
33. Rezapour, S., Farahani, R.Z., Fahimnia, B., Govindan, K., and Mansouri, Y. "Competitive closed-loop supply chain network design with price-dependent demands", Journal of Cleaner Production, 93, pp. 251-272 (2015).
34
34. Moore, J.T. and Bard, J.F. "The mixed integer linear bilevel programming problem", Operations Research, 38(5), pp. 911-921 (1990).
35
ORIGINAL_ARTICLE
Multi-objective mathematical modeling of an integrated train makeup and routing problem in an Iranian railway company
Train formation planning faces two types of challenges; namely, the determination of the quantity of cargo trains run known as the frequency of cargo trains and the formation of desired allocations of demands to a freight train. To investigate the issues of train makeup and train routing simultaneously, this multi-objective model optimizes the total profit, satisfaction level of customers, yard activities in terms of the total size of a shunting operation, and underutilized train capacity. It also considers the guarantee for the yard-demand balance of flow, maximum and minimum limitations for the length of trains, maximum yard limitation for train formation, maximum yard limitation for operations related to shunting, maximum limitation for the train capacity, and upper limit of the capacity of each arc in passing trains. In this paper, a goal programming approach and an Lp norm method are applied to the problem. Furthermore, a simulated annealing (SA) algorithm is designed. Some test problems are also carried out via simulation and solved using the SA algorithm. Furthermore, a sample investigation is carried out in a railway company in Iran. The findings show the capability and performance of the proposed approach to solve the problems in a real rail network.
https://scientiairanica.sharif.edu/article_20782_06bc1359fc53bc2357beeb0ff1d21b60.pdf
2019-12-01
3765
3779
10.24200/sci.2018.20782
Train makeup and routing problem
Optimization with multiple objectives
Lp norm
Goal-oriented optimization (GP)
Simulated annealing
R.
Alikhani-Kooshkak
1
School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
AUTHOR
R.
Tavakkoli-Moghaddam
2
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Postal Code: 1439957131, Iran; c.LCFC, Metz, France.
LEAD_AUTHOR
A.
Jamili
3
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Postal Code: 1439957131, Iran.
AUTHOR
S.
Ebrahimnejad
ibrahimnejad@kiau.ac.ir
4
Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.
AUTHOR
References:
1
1. Berechman, J. "Urban and regional economic impacts of transportation investment: a critical assessment and proposed methodology", Transportation Research Part A: Policy and Practice, 28(4), pp. 351-362 (1994).
2
2. Huddleston, J.R. and Pangotra, p.p. "Regional and local economic impacts of transportation investments", Transportation Quarterly, 44(4), pp. 579-594 (1990).
3
3. Yaghini, M., Momeni, M., and Sarmadi, M. "A hybrid solution method for fuzzy train formation planning", Applied Soft Computing, 31, pp. 257-265 (2015).
4
4. Yaghini, M., Momeni, M., and Sarmadi, M. "Solving train formation problem using simulated annealing algorithm in a simplex framework", Journal of Advanced Transportation, 48(5), pp. 402-416 (2014).
5
5. Assad, A.A. "Modelling of rail networks: Toward a routing/makeup model", Transportation Research Part B: Methodological, 14(1), pp. 101-114 (1980).
6
6. Crainic, T., Ferland, J.A., and Rousseau, J.M. "A tactical planning model for rail freight transportation", Transportation Science, 18(2), pp. 165-184 (1984).
7
7. Keaton, M.H. "Designing railroad operating plans: A dual adjustment method for implementing Lagrangian relaxation", Transportation Science, 26(4), pp. 263- 279 (1992).
8
8. Morlok, E.K. and Thomas, E.N. Final Report on the Development of a Geographic Transportation Network Generation and Evaluation Model, Transportation Center Northwestern University (1970)
9
9. Huntley, C.L., Brown, D.E., Sappington, D.E., and Markowicz, B.P. "Freight routing and scheduling at CSX transportation", Interfaces, 25(3), pp. 58-71 (1995).
10
10. Bagheri, M., Saccomanno, F., and Fu, L. "Modeling hazardous materials risks for different train make-up plans", Transportation Research Part E: Logistics and Transportation Review, 48(5), pp. 907-918 (2012).
11
11. Shafia, M.A., Sadjadi, S.J., and Jamili, A. "Robust train formation planning", Proceedings of the Institutionof Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 224(2), pp. 75-90 (2010).
12
12. Sun, Y., Cao, C., and Wu, C. "Multi-objective optimization of train routing problem combined with train scheduling on a high-speed railway network", Transportation Research Part C: Emerging Technologies, 44, pp. 1-20 (2014).
13
13. Masek, J., Camaj, J., and Nedeliakova, E. "Innovative methods of improving train formation in freight transport", World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 9(11), pp. 1947-1950 (2015).
14
14. Borndorfer, R., Klug, T., Schlechte, T., Fugenschuh, A., Schang, T., and Schulldorf, H. "The freight train routing problem for congested railway networks with mixed traffic", Transportation Science, 50(2), pp. 408- 423 (2016).
15
15. Boysen, N., Emde, S., and Fliedner, M. "The basic train makeup problem in shunting yards", OR Spectrum, 38(1), pp. 207-233 (2016).
16
16. Cheng, J., Verma, M., and Verter, V. "Impact of train makeup on hazmat risk in a transport corridor", Journal of Transportation Safety & Security, 9(2), pp. 167-194 (2017).
17
17. Bahrami, F., Safari, H., Tavakkoli-Moghaddam, R., and Modarres Yazdi, M. "On modeling door-to-door parcel delivery services in Iran", Iranian Journal of Management Studies, 9(4), pp. 883-906 (2017).
18
18. Gallardo-Bobadilla, R. and Doucette, J. "A linear programming model for optimization of the railway blocking problem", Proceeding of the American Railway Engineering and Maintenance-of-Way Association Annual Conference (AREMA 2014), Chicago, IL, September 28 - October 1 (2014).
19
19. Yaghini, M., Momeni, M., and Sarmadi, M. "An improved local branching approach for train formation planning", Applied Mathematical Modelling, 37(4), pp.2300-2307 (2013).
20
20. Jamili, A. "A mathematical model and a hybrid algorithm for robust periodic single-track train-scheduling problem", International Journal of Civil Engineering, 15(1), pp. 63-75 (2017).
21
21. Tavakkoli-Moghaddam, R., Safaei, N., and Sassani, F. "A new solution for a dynamic cell formation problem with alternative routing and machine costs using simulated annealing", Journal of the Operational Research Society, 59(4), pp. 443-454 (2008).
22
22. Alikhani-Kooshkak, R., Tavakkoli-Moghaddam, R., Jamili, A., and Ebrahimnejad, S. "Solving a multiobjective train makeup model with locomotive limitation by a fire y algorithm: A case study", Proc. of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 232(5), pp. 1483- 1499 (2018).
23
23. Hwang, C.L. and Masud, A.S.M., Multiple Objective Decision Making-Methods and Applications: A stateof- the-Art Survey, (164). Springer Science & Business Media (2012).
24
24. Coello Coello, C.A. and Christiansen, A.D. "MOSES: A multi-objective optimization tool for engineering design", Engineering Optimization, 31(3), pp. 337-368 (1999).
25
25. Evans, G.W. "An overview of techniques for solving multi-objective mathematical programs", Management Science, 30(11), pp. 1268-1282 (1984).
26
26. Homma, T. and Saltelli, A. "Importance measures in global sensitivity analysis of nonlinear models", Reliability Engineering & System Safety, 52(1), pp. 1- 17 (1996).
27
27. Saltelli, A., Chan, K., and Scott, E.M. (Eds.), Sensitivity Analysis, 1, New York: Wiley (2000).
28
28. Kirkpatrick, S., Gelatt, C.D., and Vecchi, M.P. Optimization by simulated annealing", Science, 220(4598), pp. 671-680 (1983).
29
ORIGINAL_ARTICLE
Simulation-based optimization of a stochastic supply chain considering supplier disruption: Agent-based modeling and reinforcement learning
Many researchers and practitioners in the recent years have been attracted to investigate the role of uncertainties in the supply chain management concept. In this paper a multi-period stochastic supply chain with demand uncertainty and supplier disruption is modeled. In the model, two types of retailers including risk sensitive and risk neutral, with many capacitated suppliers are considered. Autonomous retailers have three choices to satisfy demands: ordering from primary suppliers, reserved suppliers and spot market. The goal is to find the best behavior of the risk sensitive retailer, regarding the forward and option contracts, during several contract periods based on the profit function. Hence, an agent-based simulation approach has been developed to simulate the supply chain and transactions between retailers and unreliable suppliers. In addition, a Q-learning approach (as a method of reinforcement learning) has been developed to optimize the simulation procedure. Furthermore, different configurations for simulation procedure are analyzed. The R-netlogo package is used to implement the algorithm. Also a numerical example has been solved using the proposed simulation-optimization approach. Several sensitivity analyzes are conducted regarding different parameters of the model. Comparison of the numerical results with a genetic algorithm shows a significant efficiency of the proposed Q-leaning approach.
https://scientiairanica.sharif.edu/article_20789_183e9662826ea92c91fcd359191e6d06.pdf
2019-12-01
3780
3795
10.24200/sci.2018.20789
Supply chain management
simulation based optimization
reinforcement learning
demand uncertainty
supplier disruption
A.
Aghaie
1
Department of Industrial Engineering, K.N. Toosi University of Technology, Pardis Street, Mollasadra Street, Vanaq Square, Tehran, 1999143344, Iran
LEAD_AUTHOR
M.
Hajian Heidary
2
Department of Industrial Engineering, K.N. Toosi University of Technology, Pardis Street, Mollasadra Street, Vanaq Square, Tehran, 1999143344, Iran
AUTHOR
References:
1
1. Merzifonluoglu, Y. "Risk averse supply portfolio selection with supply, demand and spot market volatility", Omega, 57, pp. 40-53 (2015).
2
2. Ray, P. and Jenamani, M. "sourcing under supply disruption with capacity-constrained suppliers", Journal of Advances in Management Research, 10(2), pp. 192- 205 (2013).
3
3. Ray, P. and Jenamani, M. "sourcing decision under disruption risk with supply and demand uncertainty: A newsvendor approach", Annals of Operations Research, 237(1), pp. 237-262 (2016).
4
4. Kim, G., Wu, K., and Huang, E. "Optimal inventory control in a multi-period newsvendor problem with non-stationary demand", Advanced Engineering Informatics, 29(1), pp. 139-145 (2015).
5
5. Chopra, S. and Meidl, P., Supply Chain Management: Strategy, Planning and Operation, Pearson, Sixth edition, USA (2016).
6
6. Chiacchio, F., Pennisi, M., Russo, G., Motta, S., and Pappalardo, F. "Agent-based modeling of the immune system: NetLogo, a promising framework", BioMed Research International, 2, pp. 1-6 (2014).
7
7. Humann, J. and Madni, A.M. "Integrated agentbased modeling and optimization in complex systems analysis", Procedia Computer Science, 28, pp. 818-827 (2014).
8
8. Macal, C.M. "Everything you need to know about agent-based modelling and simulation", Journal of Simulation, 10, pp. 144-156 (2016).
9
9. Avci, M.G. and Selim, H. "A multi-objective, simulation-based optimization framework for supply chains with premium freights", Expert Systems with Applications, 67, pp. 95-106 (2017).
10
10. Sutton, R.S. and Barto, A.G., Reinforcement Learning: An Introduction, MIT press, Cambridge (1998).
11
11. Gosavi, A. "Reinforcement learning for long-run average cost", European Journal of Operational Research, 155, pp. 654-674 (2004).
12
12. Merzifonluoglu, Y. and Feng, Y. "Newsvendor problem with multiple unreliable suppliers", International Journal of Production Research, 52(1), pp. 221-242 (2014).
13
13. Merzifonluoglu, Y. "Impact of risk aversion and backup supplier on sourcing decisions of a firm", International Journal of Production Research, 53(22), pp. 6937-6961 (2015).
14
14. Merzifonluoglu, Y. "Integrated demand and procurement portfolio management with spot market volatility and option contracts", European Journal of Operational Research, 258(1), pp. 181-192 (2017).
15
15. Bouakiz, M. and Sobel, M.J. "Inventory control with an exponential utility criterion", Operations Research, 40(3), pp. 603-608 (1992).
16
16. Wang, H.F., Chen, B.C., and Yan, H.M. "Optimal inventory decisions in a multi period newsvendor problem with partially observed Markovian supply capacities", European Journal of Operational Research, 202, pp. 502-517 (2010).
17
17. Densing, M. "Dispatch planning using newsvendor dual problems and occupation times: application to hydropower", European Journal of Operational Research, 228, pp. 321-330 (2013).
18
18. Jalali, H. and Nieuwenhuyse, I.V. "Simulation optimization in inventory replenishment: a classification", IIE Transactions, 47, pp. 1217-1235 (2015).
19
19. Nikolopoulou, A. and Ierapetritou, M.G. "Hybrid simulation based optimization approach for supply chain management", Computers & Chemical Engineering, 47, pp. 183-193 (2012).
20
20. Kwon, O., Im, G.P., and Lee, K.C. "MACE-SCM: A multi-agent and case-based reasoning collaboration mechanism for supply chain management under supply and demand uncertainties", Expert Systems with Applications, 33(3), pp. 690-705 (2007).
21
21. Chaharsooghi, S.K., Heydari, J., and Zegordi, S.H. "A reinforcement learning model for supply chain ordering management: An application to the beer game", Decision Support Systems, 45(4), pp. 949-959 (2008).
22
22. Sun, R. and Zhao, G. "Analyses about efficiency of reinforcement learning to supply chain ordering management", IEEE 10th International Conference on Industrial Informatics, China (2012).
23
23. Dogan, I. and Guner, A.R. "A reinforcement learning approach to competitive ordering and pricing problem", Expert Systems, 32(1), pp. 39-48 (2015).
24
24. Jiang, C. and Sheng, Z. "Case-based reinforcement learning for dynamic inventory control in a multi-agent supply-chain system", Expert Systems with Applications, 36(3), pp. 6520-6526 (2009).
25
25. Kim, C.O., Kwon, I.-H., and Kwak, C. "Multiagent based distributed inventory control model", Expert Systems with Applications, 37(7), pp. 5186-5191 (2010).
26
26. Mortazavi, A., Khamseh, A.A., and Azimi, P. "Designing of an intelligent self-adaptive model for supply chain ordering management system", Engineering Applications of Artificial Intelligence, 37, pp. 207-220 (2015).
27
27. Rabe, M. and Dross, F. "A reinforcement learning approach for a decision support system for logistics networks", Winter Simulation Conference, USA (2015).
28
28. Zhou, J., Purvis, M., and Muhammad, Y. "A combined modelling approach for multi-agent collaborative planning in global supply chains", 8th International Symposium on Computational Intelligence and Design, China (2015).
29
29. Thiele, J. and Marries, R. "NetLogo: introduction to the RNetLogo package", Journal of Statistical Software, 58, pp. 1-41 (2014).
30
30. Liu, R., Tao, Y., Hu, Q., and Xie, X. "Simulationbased optimisation approach for the stochastic twoechelon logistics problem", International Journal of Production Research, 55(1), pp. 187-201 (2017).
31
ORIGINAL_ARTICLE
New Shewhart-EWMA and Shewhart-CUSUM control charts for monitoring process mean
In this paper, we propose new Shewhart-EWMA (SEWMA) and Shewhart-CUSUM (SCUSUM) control charts using the varied L ranked set sampling (VLRSS) for monitoring the process mean, namely the SEWMA-VLRSS and SCUSUM-VLRSS charts. The run length characteristics of the proposed charts are computed using extensive Monte Carlo simulations. The proposed charts are compared with their existing counterparts in terms of the average and standard deviation of run lengths. It is found that, with perfect and imperfect rankings, the SEWMA-VLRSS and SCUSUM-VLRSS charts are more sensitive than their analogous charts based on simple random sampling, ranked set sampling (RSS) and median RSS schemes. A real dataset is also used to explain the implementation of the proposed control charts.
https://scientiairanica.sharif.edu/article_20637_9978e7fda2350871faf1ecd8c7ed510f.pdf
2019-12-01
3796
3818
10.24200/sci.2018.4962.1011
Average Run Length
CUSUM
Control chart
EWMA
Perfect and imperfect rankings
Ranked set sampling
Statistical process control
M.
Awais
iawais3232@gmail.com
1
Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
AUTHOR
A.
Haq
aaabdulhaq@yahoo.com
2
Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
LEAD_AUTHOR
References;
1
1. Page, E.S. "Continuous inspection schemes", Biometrika, 41(1-2), pp. 100-115 (1954).
2
2. Lucas, J.M. and Crosier, R.B. "Fast initial response for CUSUM quality-control schemes: Give your CUSUM a head start", Technometrics, 24(3), pp. 199-205 (1982).
3
3. Lucas, J.M. "Combined Shewhart-CUSUM quality control schemes", Journal of Quality Technology, 14(2), pp. 51-59 (1982).
4
4. Roberts, W.S. "Control chart tests based on geometric moving averages", Technometrics, 1(3), pp. 239-250 (1959).
5
5. Lucas, J.M. and Saccucci, M.S. "Exponentially weighted moving average control schemes: Properties and enhancements", Technometrics, 32(1), pp. 1-12 (1990).
6
6. Knoth, S. "Fast initial response features for EWMA control charts", Statistical Papers, 46(1), pp. 47-64 (2005).
7
7. Chiu, W.C. "Generally weighted moving average control charts with fast initial response features", Journal of Applied Statistics, 36(3), pp. 255-275 (2009).
8
8. Abbas, N., Riaz, M., and Does, R.J.M.M. "Mixed exponentially weighted moving average cumulative sum charts for process monitoring", Quality and Reliability Engineering International, 29(3), pp. 345-356 (2013).
9
9. Haq, A. "A new hybrid exponentially weighted moving average control chart for monitoring process mean", Quality and Reliability Engineering International, 29(7), pp. 1015-1025 (2013).
10
10. Haq, A., Brown, J., and Moltchanova, E. "Improved fast initial response features for exponentially weighted moving average and cumulative sum control charts", Quality and Reliability Engineering International, 30(5), pp. 697-710 (2014).
11
11. McIntyre, G.A. "A method for unbiased selective sampling, using ranked sets", Crop and Pasture Science, 3(4), pp. 385-390 (1952).
12
12. Dell, T.R. and Clutter, J.L. "Ranked set sampling theory with order statistics background", The International Biometric Society, 28(2), pp. 545-555 (1972).
13
13. Stokes, S.L. "Ranked set sampling with concomitant variables", Communications in Statistics Theory and Methods, 6(12), pp. 1207-1211 (1977).
14
14. Samawi, H.M., Ahmed, M.S., and Abu-Dayyeh, W. "Estimating the population mean using extreme ranked set sampling", Biometrical Journal, 38(5), pp. 577-586 (1996).
15
15. Muttlak, H.A. "Median ranked set sampling", Journal of Applied Statistical Science, 6(4), pp. 245-255 (1997).
16
16. Muttlak, H.A. "Investigating the use of quartile ranked set samples for estimating the population mean", Applied Mathematics and Computation, 146(2-3), pp. 437-443 (2003).
17
17. Al-Nasser, A.D. "L ranked set sampling: A generalization procedure for robust visual sampling", Communications in Statistics - Simulation and Computation, 36(1), pp. 33-43 (2007).
18
18. Haq, A., Brown, J., Moltchanova, E., and Al-Omari, A.I. "Varied L ranked set sampling scheme", Journal of Statistical Theory and Practice, 9(4), pp. 741-767 (2015).
19
19. Salazar, R.D. and Sinha, A.K. "Control chart x based on ranked set sampling", Comunicacion Tecnica, No. 1-97-09 (PE/CIMAT) (1997).
20
20. Muttlak, H.A. and Al-Sabah, W. "Statistical quality control based on ranked set sampling", Journal of Applied Statistics, 30(9), pp. 1055-1078 (2003).
21
21. Abujiya, M.R. and Muttlak, H. "Quality control chart for the mean using double ranked set sampling", Journal of Applied Statistics, 31(10), pp. 1185-1201 (2004).
22
22. Al-Omari, A.I. and Haq, A. "Improved quality control charts for monitoring the process mean, using doubleranked set sampling methods", Journal of Applied Statistics, 39(4), pp. 745-763 (2012).
23
23. Al-Sabah, W.S. "Cumulative sum statistical control charts using ranked set sampling data", Pakistan Journal of Statistics, 26(2), pp. 365-378 (2010).
24
24. Abujiya, M.R., Riaz, M., and Lee, M.H. "Enhancing the performance of combined Shewhart-EWMA charts", Quality and Reliability Engineering International, 29(8), pp. 1093-1106 (2013).
25
25. Abujiya, M.R., Riaz, M., and Lee, M.H. "Improving the performance of combined Shewhart-cumulative sum control charts", Quality and Reliability Engineering International, 29(8), pp. 1193-1206 (2013).
26
26. Awais, M. and Haq, A. "An EWMA chart for monitoring process mean", Journal of Statistical Computation and Simulation, 88(5), pp. 1003-1025 (2018).
27
27. Awais, M. and Haq, A. "A new cumulative sum control chart for monitoring the process mean using varied L ranked set sampling", Journal of Industrial and Production Engineering, 35(2), pp. 74-90 (2018).
28
28. Haq, A. "An improved mean deviation exponentially weighted moving average control chart to monitor process dispersion under ranked set sampling", Journal of Statistical Computation and Simulation, 84(9), pp. 2011-2024 (2014).
29
29. Mehmood, R., Riaz, M., and Does, R.J.J.M. "Control charts for location based on different sampling schemes", Journal of Applied Statistics, 40(3), pp. 483- 494 (2013).
30
30. Mehmood, R., Riaz, M., and Does, R.J.J.M. "Quality quandaries: On the application of different ranked set sampling schemes", Quality Engineering, 26(3), pp. 370-378 (2014).
31
31. Haq, A., Brown, J., and Moltchanova, E. "New exponentially weighted moving average control charts for monitoring process dispersion", Quality and Reliability Engineering International, 30(8), pp. 1311-1332 (2014).
32
32. Haq, A., Brown, J., and Moltchanova, E. "A new maximum exponentially weighted moving average control chart for monitoring process mean and dispersion", Quality and Reliability Engineering International, 31(8), pp. 1587-1610 (2015).
33
33. Haq, A., Brown, J., and Moltchanova, E. "A new exponentially weighted moving average control chart for monitoring the process mean", Quality and Reliability Engineering International, 31(8), pp. 1623-1640 (2015).
34
34. Haq, A., Brown, J., and Moltchanova, E. "A new maximum exponentially weighted moving average control chart for monitoring process mean and dispersion", Quality and Reliability Engineering International, 31(8), pp. 1587-1610 (2015).
35
35. Haq, A., Brown, J., and Moltchanova, E. "New synthetic control charts for monitoring process mean and process dispersion", Quality and Reliability Engineering International, 31(8), pp. 1305-1325 (2015).
36
36. Haq, A., Brown, J., Moltchanova, E., and Al-Omari, A.I. "Effect of measurement error on exponentially weighted moving average control charts under ranked set sampling schemes", Journal of Statistical Computation and Simulation, 85(6), pp. 1224-1246 (2015).
37
37. Abbasi, S.A. and Riaz, M. "On dual use of auxiliary information for efficient monitoring", Quality and Reliability Engineering International, 32(2), pp. 705-714 (2016).
38
38. Abid, M., Nazir, H.Z., Riaz, M., and Lin, Z. "Use of ranked set sampling in nonparametric control charts", Journal of the Chinese Institute of Engineers, 39(5), pp. 627-636 (2016).
39
39. Abid, M., Nazir, H.Z., Riaz, M., and Lin, Z. "Investigating the impact of ranked set sampling in nonparametric CUSUM control charts", Quality and Reliability Engineering International, 33(1), pp. 203- 214 (2016).
40
40. Munir, W. and Haq, A. "New cumulative sum control charts for monitoring process variability", Journal of Statistical Computation and Simulation, 87(15), pp. 2882-2899 (2017).
41
41. Rhoads, T.R., Montgomery, D.C., and Mastrangelo, C.M. "A fast initial response scheme for the exponentially weighted moving average control chart", Quality Engineering, 9(2), pp. 317-327 (1996).
42
42. Steiner, S.H. "EWMA control charts with time-varying control limits and fast initial response", Journal of Quality Technology, 31(1), pp. 75-86 (1999).
43
43. David, H.A. and Nagaraja, H.N., Order Statistics, 3rd Edn., John Wiley & Sons, Inc., Hoboken, New Jersey (2003).
44
44. Montgomery, D.C., Introduction to Statistical Quality Control, 6th Edn., John Wiley & Sons, Inc., New York (2007).
45
ORIGINAL_ARTICLE
A Malmquist productivity index with the directional distance function and uncertain data
We present an integrated data envelopment analysis (DEA) and Malmquist productivity index (MPI) to evaluate the performance of decision making units (DMUs) by using a directional distance function with undesirable interval outputs. The MPI calculation is performed to compare the efficiency of the DMUs in distinct time periods. The uncertainty inherent in real-world problems is considered by using the best and worst-case scenarios, defining an interval for the MPI and reflecting the DMUs’ advancement or regress. The optimal solution of the robust model lies in the efficiency interval, i.e., it is always equal to or less than the optimal solution in the optimistic case and equal to or greater than the optimal solution in the pessimistic case. We also present a case study in the banking industry to demonstrate applicability and efficacy of the proposed integrated approach.
https://scientiairanica.sharif.edu/article_20698_156a76a4e9750942911ff491913e1498.pdf
2019-12-01
3819
3834
10.24200/sci.2018.5259.1173
Data envelopment analysis
Malmquist productivity index
Interval approach
directional distance function
undesirable outputs
N.
Aghayi
nazila.aghayi@gmail.com
1
Department of Mathematics, Ardabil Branch, Islamic Azad University, Ardabil, Iran
LEAD_AUTHOR
M.
Tavana
tavana@lasalle.edu
2
Department of Business Systems and Analytics, Lindback Distinguished Chair of Information Systems and Decision Sciences, La Salle University, Philadelphia, PA 19141, USA.; Department of Business Information Systems, Faculty of Business Administration and Economics, University of Paderborn, D-33098 Paderborn, Germany.
AUTHOR
B.
Maleki
hodamaleki62@yahoo.com
3
Department of Mathematics, Ardabil Branch, Islamic Azad University, Ardabil, Iran
AUTHOR
References:
1
1. Farrell, M.J. "The measurement of productive efficiency", Journal of the Royal Statistical Society, 120(3), pp. 253-281 (1957).
2
2. Charnes, A., Cooper, W.W., and Rhodes, E. "Measuring the efficiency of decision making units", European Journal of Operational Research, 2(6), pp. 429-444 (1978).
3
3. Banker, R.D., Charnes, A., and Cooper, W.W. "Some models for estimating technical and scale inefficiencies in data envelopment analysis", Management Science, 30(9), pp. 1078-1092 (1984).
4
4. Pittman, R.W. "Multilateral productivity comparisons with undesirable outputs", Economic Journal, 93(372), pp. 883-891 (1983).
5
5. Caves, D.W., Christensen, L.R., and Diewert, E. "Multilateral comparisons of output, input and productivity using superlative index numbers", The Economic Journal, 92(365), pp. 73-86 (1982).
6
6. Ardabili, J.S., Aghayi, N., and Monzali, A.L. "New efficiency using undesirable factors of data envelopment analysis", Modeling & Optimization, 9(2), pp. 249-255 (2007).
7
7. Malmquist, S. "Index numbers and indifference surfaces", Trabajos de Estatistica, 4(2), pp. 209- 242 (1953).
8
8. Fare, R., Grosskopf, S., and Logan, J. "The relative efficiency of Illinois electric utilities", Resources and Energy, 5, pp. 349-367 (1983).
9
9. Soyster, A.L. "Convex programming with set- inclusive constraints and applications to inexact linear programming", Operational Research, 21, pp. 1154-1157 (1972).
10
10. Seiford, L.M. and Zhu, J. "Modeling undesirable factors inefficiency valuation", European Journal of Operational Research, 142(1), pp. 16-20 (2002).
11
11. Chambers, R.G., Chung, Y., and Fare, R. "Benefit and distance function", Journal of Economic Theory, 70(2), pp. 407-419 (1996).
12
12. Chung, Y.H., Fare, R., and Grosskopf, S. "Productivity and undesirable outputs a directional distance function approach", Journal of Environmental Management, 51(3), pp. 229-240 (1997).
13
13. Shepherd, R.W., Theory of Cost and Production Functions, Princeton, NJ, USA: Princeton University press (1970).
14
14. Zanella, A., Camanho, A., and Dias, T. "Undesirable outputs and weighting schemes in composite indicators based on data envelopment analysis", European Journal of Operational Research, 245, pp. 517-530 (2015).
15
15. Iftikhar, Y., Wang, Z., Zhang, B., and Wang, B. "Energy and CO2 emissions efficiency of major economies: A network DEA approach", Energy, 147, pp. 197-207 (2018).
16
16. Khoshandam, L., Kazemi, R., and Amirteimoori, A. "Marginal rate of substitution in data envelopment analysis with undesirable outputs: A directional approach", Measurement, 68, pp. 49-57 (2015).
17
17. Barnabe, W. "Disaggregation of the cost Malmquist productivity index with joint and output-specific inputs", Omega, 75, pp. 1-12 (2018).
18
18. Sueyoshi, T., Goto, M., and Wang, D. "Malmquist index measurement for sustainability enhancement in Chinese municipalities and provinces", Energy Economics, 67, pp. 554-571 (2017).
19
19. Sueyoshi, T. and Goto, M. "DEA environmental assessment in time horizon: Radial approach Malmquist index measurement on petroleum companies", Energy Economics, 51, pp. 329-345 (2015).
20
20. Fuentes, R. and Lillo-Banuls, A. "Smoothed bootstrap Malmquist index based on DEA model to compute productivity of tax offices", Expert Systems with Applications, 42, pp. 2442-2450 (2015).
21
21. Yu, C., Shi, L., Wang, Y., Chang, Y., and Cheng, B. "The eco-efficiency of pulp and paper industry in China: an assessment based on slacks-based measure and Malquist-Luenberger index", Journal of Cleaner Production, 127, pp. 511-521 (2016).
22
22. Kao, C. "Measurement and decomposition of the Malmquist productivity index for parallel production systems", Omega, 67, pp. 54-59 (2016).
23
23. Maroto, A. and Zofio, J. "Accessibility gains and road transport infrastructure in Spain: A productivity approach based on the Malmquist index", Journal of Transport Geography, 52, pp. 143-152 (2016).
24
24. Emrouznejad, A., Rostamy-Malkhalifeh, M., Hatami-Marbini, A., Tavana, M., and Aghayi, N. "An overall profit Malmquist productivity index with fuzzy and interval data", Mathematical and Computer Modelling, 54(11-12), pp. 2827-2838 (2011).
25
25. Wanke, P., Barros, C.P., and Emrouznejad, A. "Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping a case of Mozambican banks", European Journal of Operational Research, 249(1), pp. 378-389 (2016).
26
26. Mashayekhi, Z. and Omrani, H. "An integrated multiobjective Markowitz-DEA cross efficiency model with fuzzy returns for portfolio selection problem", Operation Research, 38, pp. 1-9 (2016).
27
27. Aghayi, N. "Cost efficiency measurement with fuzzy data in DEA", Journal of Intelligent and Fuzzy Systems, 32, pp. 409-420 (2017).
28
28. Toloo, M., Aghayi, N., and Rostamy-Malkhalifeh, M. "Measuring overall profit efficiency with interval data", Applied Mathematics and Computation, 201(1-2), pp. 640-649 (2008).
29
29. Hatami-Marbini, A., Emrouznejad, A., and Agrell,P. "Interval data without sign restrictions in DEA", Applied Mathematical Modelling, 38(7-8), pp. 2028-2036 (2014).
30
30. Salehpour, S. and Aghayi, N. "The most revenue efficiency with price uncertainty", International Journal of Data Envelopment Analysis, 3, pp. 575-592 (2015).
31
31. Kouvelis, P. and Yu, G., Robust Discrete Optimization and Its Applications, Kluwer Academic publishers Norwell, MA (1997).
32
32. Ben-Tall, A. and Nemirovski, A. "Robust convex optimization", Mathematical Operation Research, 23, pp. 769-805 (1998).
33
33. El-Ghaoui, L. and Lebret, H. "Robust solutions to least-squares problems to uncertain data matrices", Sima Journal on Matrix Analysis and Applications, 18, pp. 1035-1064 (1997).
34
34. Bertsimas, D. and Sim, M. "The price of the robustness", Operation Research, 52, pp. 35-53 (2004).
35
35. Zahedi-Seresht, M., Jahanshahloo, G.R., and Jablonsky, J. "A robust data envelopment analysis model with different scenarios", Applied Mathematical Modelling, 52, pp. 306-319 (2017).
36
36. Yousefi, S., Soltani, R., Saen, R.F., and Pishvaee, M.S. "A robust fuzzy possibilistic programming for a new network GP-DEA model to evaluate sustainable supply chains", Journal of Cleaner Production, 166, pp. 537-549 (2017).
37
37. Chung-Cheng, L. "Robust data envelopment analyses approaches for evaluating algorithmic performance", Computers and Industrial Engineering, 81, pp. 78-89 (2015).
38
38. Mardani, M. and Salarpour, M. "Measuring technical efficiency of potato production in Iran using robust data envelopment analysis", Information Processing in Agriculture, 2(1), pp. 6-14 (2015).
39
39. Aghayi, N., Tavana, M., and Raayatpanah, M.A. "Robust efficiency measurement with common set of weights under varying degrees of conservatism and data uncertainty", European Journal of Industrial Engineering, 10(30), pp. 385-405 (2016).
40
40. Aghayi, N. and Maleki, B. "Efficiency measurement of DMUs with undesirable outputs under uncertainty based on the directional distance function: Application on Bank Industry", Energy, 112, pp. 376-387 (2016).
41
41. Ray, C. and Desli, E. "Productivity growth, technical progress, and efficiency change in industrialized countries: comment", The American Economic Review, 87, pp. 1033-1039 (1997).
42
ORIGINAL_ARTICLE
Hartley-Ross type unbiased estimators of population mean using two auxiliary variables
In survey sampling, most of the research work based on the fact that utilizing the information of auxiliary variable(s) boosts the efficiency of estimators. Keeping this fact in mind we used the information of two auxiliary variables to propose a family of Hartley-Ross type unbiased estimators for estimating population mean under simple random sampling without replacement. Minimum variance of the new family is derived up to first order of approximation. Three real data sets are used to verify that the new family acts efficiently than the usual unbiased, Hartley and Ross (1954), Grover and Kaur (2014), Singh et al. (2014), Cekim and Kadilar (2016), Muneer et al. (2017) and Shabbir and Gupta (2017) estimators.
https://scientiairanica.sharif.edu/article_20726_3f1958ebf619cf67e87d3b08adce1978.pdf
2019-12-01
3835
3845
10.24200/sci.2018.5648.1397
Auxiliary variable
Hartley-Ross type Estimator
Unbiased
Variance
M.
Javed
mariajaved@gcuf.edu.pk
1
a. Department of Mathematics, Institute of Statistics, Zhejiang University, Hangzhou 310027, China. b. Department of Statistics, Government College University, Faisalabad, Pakistan
LEAD_AUTHOR
M.
Irfan
mirfan@zju.edu.cn
2
a. Department of Mathematics, Institute of Statistics, Zhejiang University, Hangzhou 310027, China. b. Department of Statistics, Government College University, Faisalabad, Pakistan
AUTHOR
T.
Pang
txpang@zju.edu.cn
3
Department of Mathematics, Institute of Statistics, Zhejiang University, Hangzhou 310027, China.
AUTHOR
References:
1
1. Abu-Dayyeh, W.A., Ahmed, M.S., Ahmed, R.A., and Muttlak, H.A. "Some estimators of finite population mean using auxiliary information", Applied Mathematics and Computation, 139, pp. 287-298 (2003).
2
2. Kadilar, C. and Cingi, H. "A new estimator using two auxiliary variables", Applied Mathematics and Computation, 162, pp. 901-908 (2005).
3
3. Singh, H.P. and Tailor, R. "Estimation of finite population mean using known correlation coefficient between auxiliary characters", Statistica, 65, pp. 407- 418 (2005).
4
4. Lu, J. and Yan, Z. "A class of ratio estimators of a finite population mean using two auxiliary variables", PLOS ONE, 9(2), pp. 1-6 (2014).
5
5. Lu, J., Yan, Z., and Peng, X. "A new exponential ratio type estimator with linear combination of two auxiliary variables", PLOS ONE, 9(12), pp. 1-10 (2014).
6
6. Vishwakarma., G.K. and Kumar, M. "A general family of dual to ratio-cum-product estimators of population mean in simple random sampling", Chilean Journal of Statistics, 6(2), pp. 69-79 (2015).
7
7. Sharma, P. and Singh, R. "A class of exponential ratio estimators of finite population mean using two auxiliary variables", Pakistan Journal of Statistics and Operational Research, 11(2), pp. 221-229 (2015).
8
8. Yasmeen, U., Amin, N.M., and Hanif, M. "Exponential ratio and product type estimators of finite population mean", Journal of Statistics and Management Systems, 19(1), pp. 55-71 (2016).
9
9. Lu, J. "Efficient estimator of a finite population mean using two auxiliary variables and numerical application in agriculture, biomedical and power engineering", Mathematical Problems in Engineering, Article ID 8704734 (2017). https://doi.org/10.1155/2017/8704734.
10
10. Muneer, S., Shabbir, J., and Khalil, A. "Estimation of finite population mean in simple random sampling and stratified random sampling using two auxiliary variables", Communications in Statistics- Theory and Methods, 46(5), pp. 2181-2192 (2017).
11
11. Shabbir, J. and Gupta, S. "Estimation of finite population mean in simple and stratified random sampling using two auxiliary variables", Communications in Statistics- Theory and Methods, 46(20), pp. 10135-10148 (2017).
12
12. Hartley, H.O. and Ross, A. "Unbiased ratio estimators", Nature, 174, pp. 270-272 (1954).
13
13. Robson, D.S. "Application of multivariate polykays to the theory of unbiased ratio type estimation", Journal of American Statistical Association, 50, pp. 1225-1226 (1957).
14
14. Murthy, M.N. and Nanjamma, N.S. "Almost unbiased estimator based on interpenetrating sub-sample estimates", Sankhya, 21, pp. 381-392 (1959).
15
15. Biradar, R.S. and Singh, H.P. "A note on almost unbiased ratio-cum-product estimator", Metron, 40(1-2), pp. 249-255 (1992).
16
16. Biradar, R.S. and Singh, H.P. "On a class of almost unbiased ratio estimators", Biomedical Journal, 34(8), pp. 937-944 (1992).
17
17. Biradar, R.S. and Singh, H.P. "A class of unbiased ratio estimators", Journal of Indian Society Agricultural Statistics, 47(3), pp. 230-239 (1995).
18
18. Sahoo, J., Sahoo, L.N., and Mohanty, S. "An alternative approach to estimation in two phase sampling using two auxiliary variables", Biometrical Journal, 36, pp. 293-298 (1994).
19
19. Singh, H.P., Sharma, B., and Tailor, R. "Hartley-Ross type estimators for population mean using known parameters of auxiliary variate", Communications in Statistics- Theory and Methods, 43, pp. 547-565(2014).
20
20. Cekim, H.O. and Kadilar, C. "New unbiased estimators with the help of Hartley-Ross type estimators", Pakistan Journal of Statistics, 32(4), pp. 247-260 (2016).
21
21. Khan, L., Shabbir, J., and Gupta, S. "Unbiased ratio estimators of the mean in stratified ranked set sampling", Hacettepe Journal of Mathematics and Statistics, 46(6), pp. 1151-1158 (2017).
22
22. Singh, R. and Mangat, N.S., Elements of Survey Sampling, Norwell, MA: Kluwer Academic Publishers (1996).
23
23. Grover, L.K. and Kaur, P. "A generalized class of ratio type exponential estimators of population mean under linear transformation of auxiliary variable", Communications in Statistics- Simulation and Computation, 43, pp. 1552-1574 (2014).
24
24. Gupta, S. and Shabbir, J. "On improvement in estimating the population mean in simple random sampling", Journal of Applied Statistics, 35(5), pp. 559- 566 (2008).
25
25. Shabbir, J. and Gupta, S. "On estimating finite population mean in simple and stratified random sampling", Communications in Statistics- Theory and Methods, 40(2), pp. 199-212 (2011).
26
26. Kadilar, C. and Cingi, H., A New Ratio Estimator Using Correlation Coefficient, Inter-Stat, pp. 1-11 (2006).
27
27. Upadhyaya, L.N. and Singh, H.P. "Use of transformed auxiliary variable in estimating the finite population mean", Biometrical Journal, 41(5), pp. 627-636 (1999).
28
28. Khoshnevisan, M., Singh, R., Chauhan, P., Sawan, N., and Smarandache, F. "A general family of estimators for estimating population mean using known value of some population parameter(s)", Far East Journal of Theoretical Statistics, 22, pp. 181-191 (2007).
29
29. Koyuncu, N. and Kadilar, C. "Efficient estimators for the population mean", Hacettepe Journal of Mathematics and Statistics, 38(2), pp. 217-225 (2009).
30
30. Singh, V.K. and Singh, R. "Performance of an estimator for estimating population mean using simple and stratified random sampling", SOP Transactions on Statistics and Analysis, 1(1), pp. 1-8 (2014).
31
31. Grover, L.K. and Kaur, P. "An improved estimator of the finite population mean in simple random sampling", Model Assisted Statistics and Application, 6(1), pp. 47-55 (2011).
32
32. Koyuncu, N. and Kadilar, C. "Family of estimators of population mean using two auxiliary variables in stratified sampling", Communications in Statistics- Theory and Methods, 38, pp. 2398-2417 (2009).
33
ORIGINAL_ARTICLE
An intelligent model for predicting the day-ahead deregulated market clearing price: A hybrid NN-PSO-GA approach
Under restructuring of electric power industry and changing traditional vertically integrated electric utility structure to competitive, market clearing price (MCP) prediction models are essential for all generation company (GenCos) for their survival under new deregulated environment. In this paper, a hybrid model is presented to predict hourly electricity MCP. The model contains a Neural Network (NN), Particle swarm optimization (PSO) and Genetic Algorithm (GA). The NN is used as the major forecasting module to predict the electricity MCP values and PSO applied to improve the traditional neural network learning capability and optimizing the weights of the NN and GA applied to optimize NN architecture. The main contribution includes: presenting a hybrid intelligent model for MCP prediction; applying K-Means algorithm to clustering NN’s test set and seasonality pattern detection; and evaluation of its performance by improved MAE with penalty factor for positive error. It has been tested on Iranian real-world electricity market for the one month of the year 2010-2013 that result shown average weighted MAE for day ahead MCP prediction is equal to 0.12 and forecasting of MCP can be improved by more than 6.7% and 4%in MAE in compare of simple NN and combination of NN and bat algorithm.
https://scientiairanica.sharif.edu/article_20615_2de046f3971939ac39d0d9952e0252cb.pdf
2019-12-01
3846
3856
10.24200/sci.2018.50910.1909
Neural network
Genetic Algorithm
particle swarm optimization
market clearing price
Pay as a bid
B.
Ostadi
bostadi@modares.ac.ir
1
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran
LEAD_AUTHOR
O.
Motamedi Sedeh
omid.motamedi@modares.ac.ir
2
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran
AUTHOR
A.
Husseinzadeh Kashan
a.kashan@modares.ac.ir
3
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran
AUTHOR
M.R.
Amin-Naseri
amin_nas@modares.ac.ir
4
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran
AUTHOR
References:
1
1. Zaman, F., Elsayed, S.M., Ray, T., and Sarker, R.A. "Co-evolutionary approach for strategic bidding in competitive electricity markets", Applied Soft Computing, 51, pp. 1-22 (2017).
2
2. Lotfi, M.M. and Ghaderi, S.F. "Possibilistic programming approach for mid-term electric power planning in deregulated markets", International Journal of Electrical Power & Energy Systems, 34, pp. 161-170 (2012).
3
3. Sandhu, H.S., Fang, L., and Guan, L. "Forecasting day-ahead price spikes for the Ontario electricity market". Electric Power Systems Research, 141, pp. 450- 459 (2016).
4
4. Girish, G.P. "Spot electricity price forecasting in Indian electricity market using autoregressive-Garch models", Energy Strategy Reviews, 11-12, pp. 52-57 (2016).
5
5. Abedinia, O., Amjadi, N., Shafie-Khah, M., and Catalao, J.P.S. "Electricity price forecast using combinatorial neural network trained by a new stochastic search method", Energy Conversion and Management, 105, pp. 642-654 (2015).
6
6. Grilli, L. "Deregulated electricity market and auctions: The Italian case", Scientific Research an Academic Publisher, 2, pp. 238-242 (2010).
7
7. Bunn, D.W. "Forecasting loads and prices in competitive power markets", IEEE Xplore, 88, pp. 163-169 (2000).
8
8. Girish, G.P., Rath, B.N., and Akram, V. "Spot electricity price discovery in Indian electricity market", Renewable and Sustainable Energy Reviews, 82, pp. 73-79 (2018).
9
9. Khosravi, A., Nahavandi, S., and Creighton, D. "A neural network-GARCH-based method for construction of prediction intervals", Electric Power Systems Research, 96, pp. 185-193 (2013).
10
10. Janczura, J., Truck, S., Weron, R., and Wolff, R.C. "Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling", Energy Economics, 38, pp. 96-110 (2013).
11
11. Nogales, F.J., Contreras, J., Conejo, A.J., and Espinola, R. "Forecasting next-day electricity prices by time series models", IEEE Transactions on Power Systems, 17, pp. 342-348 (2002).
12
12. Zhang, J., Tan, Z., and Yang, S. "Day-ahead electricity price forecasting by a new hybrid method", Computers & Industrial Engineering, 63, pp. 695-701 (2012).
13
13. Yang, Z., Ce, L., and Lian, L. "Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods", Applied Energy, 190, pp. 291-305 (2017).
14
14. Khashei, M., Mokhatab Rafeiei, F., and Bijari, M. "Hybrid fuzzy auto-regressive integrated moving average (FARIMAH) model for forecasting the foreign exchange markets", International Journal of Computational Intelligence Systems, 6(5), pp. 954-968 (2013).
15
15. Qi, Y., Liu, Y., and Wu, Q. "Non-cooperative regulation coordination based on game theory for wind farm clusters during ramping events", Energy, 132, pp. 136- 146 (2017).
16
16. Lago, J., De Ridder, F., Vrancx, P., and de Schutter, B. "Forecasting day-ahead electricity prices in Europe: The importance of considering market integration", Applied Energy, 211, pp. 890-903 (2018).
17
17. Gholipour Khajeh, M., Maleki, A., Rosen, M.A., and Ahmadi, M.H. "Electricity price forecasting using neural networks with an improved iterative training algorithm", International Journal of Ambient Energy, 39, pp. 147-158 (2018).
18
18. Bento, P.M.R., Pombo, J.A.N., Calado, M.R.A., and Mariano, S.J.P.S. "A bat optimized neural network and wavelet transform approach for short-term price forecasting", Applied Energy, 210, pp. 88-97 (2018).
19
19. Aggarwal, S.K., Saini, L.M., and Kumar, A. "Electricity price forecasting in deregulated markets: A review and evaluation", International Journal of Electrical Power & Energy Systems, 31, pp. 13-22 (2009).
20
20. Singhal, D. and Swarup, K.S. "Electricity price forecasting using artificial neural networks", International Journal of Electrical Power & Energy Systems, 33, pp. 550-555 (2011).
21
21. Mandal, P., Senjyu, T., and Funabashi, T. "Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market", Energy Conversion and Management, 47, pp. 2128- 2142 (2006).
22
22. Anbazhagan, S. and Kumarappan, N. "Day-ahead deregulated electricity market price classification using neural network input featured by DCT", International Journal of Electrical Power & Energy Systems, 37, pp. 103-109 (2012).
23
23. Yousefi, G.R., Kaviri, S.M., Latify, M.A., and Rahmati, I. "Electricity industry restructuring in Iran", Energy Policy, 108, pp. 212-226 (2017).
24
24. Deputy of Power and Energy, Electricity and Energy Planning Office of Ministry of Energy, 2015 Energy Balanced Sheet, Tehran, Iran, Ministry of Energy, pp. 165-195 (2015).
25
25. Baklacioglu, T. "Modeling the fuel flow-rate of transport aircraft during flight phases using genetic algorithm-optimized neural networks", Aerospace Science and Technology, 49, pp. 52-62 (2016).
26
26. Arora, P., Deepali, V., and Varshney, S. "Analysis of K-means and K-medoids algorithm for big data", Procedia Computer Science, 78, pp. 507-512 (2016).
27