2019
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1

Integrated biobjective project selection and scheduling using Bayesian networks: A riskbased approach
http://scientiairanica.sharif.edu/article_21387.html
10.24200/sci.2019.21387
1
This paper presents a novel formulation of the integrated biobjective 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.
0

3695
3711


A.
Namazian
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, P.O. Box 141556619, Iran.
Iran
a.namazian@ut.ac.ir


S.
Haji Yakhchali
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, P.O. Box 141556619, Iran.
Iran


M.
Rabbani
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, P.O. Box 141556619, Iran.
Iran
mrabani@ut.ac.ir
Project selection and scheduling
Risk analysis
Bayesian Networks
multiobjective programming
Genetic Algorithm
[1. Tuli, B., Arindam, S., Bijan, S., and Kumar, S.S. Introduction to softset theoretic solution of project selection problem", Benchmarking: An International Journal, 23(7), pp. 16431657 (2016). 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. Tahri, H. Mathematical optimization methods: application in project portfolio management", Procedia  Social and Behavioral Sciences, 210, pp. 339347 (2015). 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. 3542 (2016). 5. Badri, M.A., Davis, D., and Davis, D. A comprehensive 01 goal programming model for project selection", International Journal of Project Management, 19(4), pp. 243252 (2001). 6. Arratia M., N.M., L_opez I., F., Schae_er, S.E., and CruzReyes, L. Static R&D project portfolio selection in public organizations", Decision Support Systems, 84, pp. 5363 (2016). 7. Tavana, M., Keramatpour, M., SantosArteaga, 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. 84328444 (2015). 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. 3953 (2016). 9. Kellenbrink, C. and Helber, S. Scheduling resourceconstrained projects with a exible project structure", European Journal of Operational Research, 246(2), pp. 379391 (2015). 10. Ji, X. and Yao, K. Uncertain project scheduling problem with resource constraints", Journal of Intelligent Manufacturing, 28(3), pp. 575580 (2017). 11. To_ghian, A.A. and Naderi, B. Modeling and solving the project selection and scheduling", Computers & Industrial Engineering, 83, pp. 3038 (2015). 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. 7999 (2004). 13. Ghorbani, S. and Rabbani, M. A new multiobjective algorithm for a project selection problem", Advances in Engineering Software, 40(1), pp. 914 (2009). 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. 869894 (2007). 15. Xiao, J., Ao, X.T., and Tang, Y. Solving software project scheduling problems with ant colony optimization", Computers & Operations Research, 40(1), pp. 3346 (2013). 16. Wang, W.X., Wang, X., Ge, X.L., and Deng, L. Multiobjective optimization model for multiproject scheduling on critical chain", Advances in Engineering Software, 68, pp. 3339 (2014). 17. P_erez, _A., Quintanilla, S., Lino, P., and Valls, V. A multiobjective approach for a project scheduling problem with due dates and temporal constraints infeasibilities", International Journal of Production Research, 52(13), pp. 39503965 (2014). 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. 83102 (2014). 19. Artigues, C., Leus, R., and Talla Nobibon, F. Robust optimization for resourceconstrained project scheduling with uncertain activity durations", Flexible Services and Manufacturing Journal, 25(1), pp. 175205 (2013). 20. Suresh, M., Dutta, P., and Jain, K. Resource constrained multiproject scheduling problem with resource transfer times", AsiaPaci_c Journal of Operational Research, 32(06), p. 1550048 (2015). 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). 3710 A. Namazian et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 3695{3711 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. 494505 (2007). 23. ShiMing, H., IChu, C., ShingHan, L., and Ming Tong, L. Assessing risk in ERP projects: identify and prioritize the factors", Industrial Management & Data Systems, 104(8), pp. 681688 (2004). 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. 602614 (2013). 25. Rodr__guez, A., Ortega, F., and Concepci_on, R. A method for the evaluation of risk in IT projects", Expert Systems with Applications, 45, pp. 273285 (2016). 26. Zavadskas, E.K., Turskis, Z., and Tamo_saitiene, J. Risk assessment of construction projects", Journal of Civil Engineering and Management, 16(1), pp. 3346 (2010). 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. 589600 (2007). 28. Cheng, M. and Lu, Y. Developing a risk assessment method for complex pipe jacking construction projects", Automation in Construction, 58, pp. 4859 (2015). 29. Jamshidi, A., Rahimi, S.A., Aitkadi, 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. 15 (2015). 30. ChingChow, Y., WenTsaan, L., MingYi, 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. 298322 (2006). 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. 6777 (2014). 32. Hyun, K.C., Min, S., Choi, H., Park, J., and Lee, I.M. Risk analysis using faulttree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels", Tunnelling and Underground Space Technology, 49, pp. 121129 (2015). 33. Liang, W., Hu, J., Zhang, L., Guo, C., and Lin, W. Assessing and classifying risk of pipeline thirdparty interference based on fault tree and SOM", Engineering Applications of Arti_cial Intelligence, 25(3), pp. 594608 (2012). 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. 332353 (2013). 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. 198210 (2012). 36. Sadeghi, N., Fayek, A.R., and Pedrycz, W. Fuzzy Monte Carlo simulation and risk assessment in construction", ComputerAided Civil and Infrastructure Engineering, 25(4), pp. 238252 (2010). 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. 98799890 (2009). 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. 439449 (2013). 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. 3950 (2009). 40. Leu, S.S. and Chang, C.M. Bayesiannetworkbased safety risk assessment for steel construction projects", Accident Analysis & Prevention, 54, pp. 122133 (2013). 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. 86100 (2012). 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. 13581366 (2010). 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. 199205 (2016). 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. 479495 (2016). 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. 12331245 (2014). 46. Tripathy, B.B. and Biswal, M.P. A zeroone goal programming approach for project selection", Journal of Information and Optimization Sciences, 28(4), pp. 619626 (2007).##]
1

An architectural solution for virtual computer integrated manufacturing systems using ISO standards
http://scientiairanica.sharif.edu/article_20799.html
10.24200/sci.2018.20799
1
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.
0

3712
3727


J.
Delaram
Department of Industrial Engineering, Advanced Manufacturing Laboratory, Sharif University of Technology, Tehran, Iran.
Iran
jalal.delaram@ie.sharif.edu


O.
Fatahi Valilai
Department of Industrial Engineering, Advanced Manufacturing Laboratory, Sharif University of Technology, Tehran, Iran.
Iran
fvalilai@sharif.edu
CIM (Computer Integrated Manufacturing)
VCIM (Virtual Computer Integrated Manufacturing)
Manufacturing System Architecture
Axiomatic Design (AD) Theory
ISO standards
[1. Valilai, O.F. and Houshmand, M. A collaborative and integrated platform to support distributed manufacturing system using a serviceoriented approach based on cloud computing paradigm", Rob. and Com. Integ. Man., 29(1), pp. 110127 (2013). 2. Koren, Y., The Global Manufacturing Revolution: ProductProcessBusiness Integration and Recon_gurable Systems, John Wiley & Sons, UK (2010). 3. Zhang, W. and Xie, S. Agent technology for collaborative process planning: a review", The Int. J. of Adv. Man. Tech., 32(3), pp. 315325 (2007). 4. Nagalingam, S.V. and Lin, G.C. CIMstill the solution for manufacturing industry", Rob. and Com. Integ. Man., 24(3), pp. 332344 (2008). 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. 473487 (2012). 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. 10951117 (2010). 7. Wang, X.V. and Xu, X.W. An interoperable solution for cloud manufacturing", Rob. and Com. Integ. Man., 29(4), pp. 232247 (2013). 8. Li, Q. Applications integration in a hybrid cloud computing environment: modelling and platform", Ent. Info. Sys., 7(3), pp. 237271 (2013). 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. 10311054 (2014). 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. Zhou, N. Development of an agent based VCIM resource scheduling process for small and medium enterprises", Int. Ass. of Eng., 25(2), pp. 3150 (2010). 12. Nagalingam, S.V. and Lin, G. Latest developments in CIM", Rob. and Com. Integ. Man., 15(6), pp. 423430 (1999). 13. Wang, D., Nagalingam, S., and Lin, G. Development of an agentbased virtual CIM architecture for small to medium manufacturers", Rob. and Com. Integ. Man., 23, pp. 116 (2007). 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. 131154 (2011). 15. Delaram, J. and Valilai, O.F. Development of a novel solution to enable integration and interoperability for cloud manufacturing", Procedia CIRP, 52, pp. 611 (2016). 16. Scheer, A.W., CIM Computer Integrated Manufacturing: Computer Steered Industry, Springer Publishing Company (2012). 17. Rehg, J.A. and Kraebber, H.W., ComputerIntegrated Manufacturing, Prentice Hall (2005). 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. 256265 (1997). 19. Delaram, J. and Valilai, O.F. A novel solution for manufacturing interoperability ful_llment using interoperability service providers", Procedia CIRP, 63, pp. 774779 (2017). 20. Zhou, N. Inside virtual CIM", Intel. Cont. and Com. Eng., 11(5), pp. 163175 (2011). 21. Mitchell Jr, F., CIM Systems: An Introduction to ComputerIntegrated Manufacturing, PrenticeHall (1991) . J. Delaram and O. Fatahi Valilai/Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 3712{3727 3725 22. Groover, M.P., Automation, Production Systems, and ComputerIntegrated Manufacturing, Prentice Hall (2007). 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. 70507061 (2012). 24. Nagalingam, S.V. and Lin, G. A uni_ed approach towards CIM justi_cation", Com. Integ. Man. Sys., 10(2), pp. 133145 (1997). 25. Zhou, N. Virtual CIM", Intel. Cont. and Com. Eng., 21(9), pp. 3140 (2015). 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. 22162237 (2014). 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. 36933708 (2002). 28. Nagalingam, S., Lin, G., and Wang, D. Resource scheduling for a virtual CIM system", Adv. Man., 10(2), pp. 269294 (2007). 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. 161164 (2007). 30. Browne, J., The Extended EnterpriseManufacturing and the Value Chain, Springer (1995). 31. Jagdev, H. and Browne, J. The extended enterprisea context for manufacturing", Pro. Plan. and Cont., 9(3), pp. 216229 (1998). 32. Martinez, M.T. Virtual enterpriseorganisation, evolution and control", Int. J. of Pro. Eco., 74(1), pp. 225238 (2001). 33. Park, K.H. and Favrel, J. Virtual enterpriseinformation system and networking solution", Com. and Ind. Eng., 37(1), pp. 441444 (1999). 34. Liu, N., Li, X., and Shen, W. Multigranularity resource virtualization and sharing strategies in cloud manufacturing", J. of Net. and Com. App., 46, pp. 7282 (2014). 35. Morariu, O., Borangiu, T., and Raileanu, S. vMES: Virtualization aware manufacturing execution system", Comp. in Ind., 67(2), pp. 2737 (2015). 36. Buyya, R., Vecchiola, C., and Selvi, S.T. Virtualization, in mastering cloud computing", Comp. in Ind., 67(2), pp. 71109 (2013). 37. Van Geenhuizen, M. and Nijkamp, P. Knowledge virtualization and local connectedness among young globalized hightech companies", Tech. Fore. and Soc. Chan., 79(7), pp. 11791191 (2012). 38. CamarinhaMatos, L.M. and Afsarmanesh, H. A comprehensive modeling framework for collaborative networked organizations", J. of Intel. Man., 18(5), pp. 529542 (2007). 39. Brown, E.A. Reinventing government research and development: A status report on management initiatives and reinvention e_orts at the Army Research Laboratory", J. of Net. and Com. App., 62(1), pp. 78111 (1998). 40. Vassiliou, M. The virtual research laboratory: taxonomy and analysis. in aerospace", Com. and Ind. Eng., 37(1), pp. 441444 (1999). 41. Narula, R. R&D collaboration by SMEs: new opportunities and limitations in the face of globalisation", Techno., 24(2), pp. 153161 (2004). 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. 3444 (2006). 43. Browne, J. and Zhang, J. Extended and virtual enterprisessimilarities and di_erences", Int. J. of Ag. Man. Sys., 1(1), pp. 3036 (1999). 44. Zhang, J., Chan, F., and Li, P. Agentand CORBAbased application integration platform for an agile manufacturing environment", The Int. J. of Adv. Man. Tech., 21(6), pp. 460468 (2003). 45. Huang, B. A framework for virtual enterprise control with the holonic manufacturing paradigm", Com. In Ind., 49(3), pp. 299310 (2002). 46. Odrey, N.G. and Mej__, G. A recon_gurable multiagent system architecture for error recovery in production systems", Rob. and Com. Integ. Man., 19(1), pp. 3543 (2003). 47. HernandezMatias, J. An integrated modelling framework to support manufacturing system diagnosis for continuous improvement", Rob. and Com. Iinteg. Man., 24(2), pp. 187199 (2008). 48. Lin, C.P. and Jeng, M. An expanded SEMATECH CIM framework for heterogeneous applications integration", Man. and Cyber., 36(1), pp. 7690 (2006). 49. Nahm, Y.E. and Ishikawa, H. A hybrid multiagent system architecture for enterprise integration using computer networks", Rob. and Com. Integ. Man., 21(3), pp. 217234 (2005). 50. Williamson, A. and Deasley, P. Systems thinking and computerintegrated manufacturing", Sys. Prac Tech., 7(1), pp. 923 (1994). 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. 111123 (1997). 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. 96114 (2018). 53. Suh, N.P., The Principles of Design, Oxford University Press, UK (1990). 54. Kulak, O. and Kahraman, C. Fuzzy multiattribute selection among transportation companies using axiomatic design and analytic hierarchy process", Info. Sci., 170(2), pp. 191210 (2005). 3726 J. Delaram and O. Fatahi Valilai/Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 3712{3727 55. Carnevalli, J.A., Miguel, P.A.C., and Calarge, F.A. Axiomatic design application for minimising the dif _culties of QFD usage", Int. J. of Pro. Eco., 125(1), pp. 112 (2010). 56. Peck, J., Nightingale, D., and Kim, S.G. Axiomatic approach for e_cient healthcare system design and optimization", CIRP Ann. Man. Tech., 59(1), pp. 469 472 (2010). 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. 4972 (2012). 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. 13771396 (2000). 59. Valilai, O.F. and Houshmand, M. A manufacturing ontology model to enable data integration services in cloud manufacturing using axiomatic design theory", in CloudBased Design and Manufacturing (CBDM): A ServiceOriented Product Development Paradigm for the 21st Century, Springer (2014). 60. Suh, N.P., Axiomatic Design: Advances and Applications, Oxford University Press, UK (2001). 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. 243255 (1991). 62. Rechtin, E., Systems Architecting: Creating and Building Complex Systems, PrenticeHall (2001). 63. Albano, L.D. and Suh, N.P. Axiomatic design and concurrent engineering", Com. Aid. Des., 26(7), pp. 499504 (1994). 64. Bae, S., Lee, J.M., and Chu, C.N. Axiomatic design of automotive suspension systems", CIRP Ann. Man. Tech., 51(1), pp. 115118 (2002). 65. Suh, N.P. Designingin of quality through axiomatic design", Rel, Trans., 44(2), pp. 256264 (1995). 66. Gon_calvesCoelho, A.M. and Mourao, A.J. Axiomatic design as support for decisionmaking in a design for manufacturing context: A case study", Int. J. of Pro. Eco., 109(1), pp. 8189 (2007). 67. Cebi, S. and Kahraman, C. Indicator design for p passenger car using fuzzy axiomatic design principles", Exp. Sys. with App., 37(9), pp. 64706481 (2010). 68. Suh, N.P. Axiomatic design of mechanical systems", J. of Mech. Des., 117(B), pp. 210 (1995). 69. Gebala, D.A. and Suh, N.P. An application of axiomatic design", Res. in Eng. Des., 3(3), pp. 149162 (1992). 70. Togay, C., Dogru, A.H., and Tanik, J.U. Systematic componentoriented development with axiomatic design", J. of Sys. and Soft., 81(11), pp. 18031815 (2008). 71. Ferrer, I. Methodology for capturing and formalizing DFM Knowledge", Rob. and Com. Integ. Man., 26(5), pp. 420429 (2010). 72. Cebi, S., Celik, M., and Kahraman, C. Structuring ship design project approval mechanism towards installation of operatorsystem interfaces via fuzzy axiomatic design principles", Info. Sci., 180(6), pp. 886895 (2010). 73. Heo, G. and Lee, S.K. Design evaluation of emergency core cooling systems using axiomatic design", Nuc. Eng. and Des., 237(1), pp. 3846 (2007). 74. Thielman, J. Evaluation and optimization of General Atomics' GTMHR reactor cavity cooling system using an axiomatic design approach", Nuc. Eng. and Des., 235(13), pp. 13891402 (2005). 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. Hirani, H. and Suh, N.P. Bearing design using multiobjective genetic algorithm and axiomatic design approaches", Tri. Int., 38(5), pp. 481491 (2005). 77. Janthong, N., Brissaud, D., and Butdee, S. Combining axiomatic design and casebased reasoning in an innovative design methodology of mechatronics products", CIRP J. of Man. Sci. and Tech., 2(4), pp. 226239 (2010). 78. http://www.iso.org/iso/iso catalogue/catalogue tc/ catalogue detail.htm?csnumber=39926. 79. http://www.iso.org/iso/iso catalogue/catalogue tc/ catalogue detail.htm?csnumber=29556. 80. http://www.iso.org/iso/iso catalogue/catalogue tc/ catalogue detail.htm?csnumber=24020. 81. http://www.iso.org/iso/iso catalogue/catalogue tc/ catalogue detail.htm?csnumber=28777. 82. http://www.iso.org/iso/iso catalogue/catalogue tc/ catalogue detail.htm?csnumber=50417. 83. http://www.iso.org/iso/catalogue detail.htm? csnumber=46559. 84. http://www.iso.org/iso/iso catalogue/catalogue tc/ catalogue detail.htm?csnumber=30418. 85. http://www.iso.org/iso/iso catalogue/catalogue tc/ catalogue detail.htm?csnumber=31583. 86. http://www.iso.org/iso/catalogue detail.htm? csnumber=57308. 87. http://www.iso.org/iso/catalogue detail.htm? csnumber=54497.##]
1

A tabu search algorithm for a multiperiod bank branch location problem: A case study in a Turkish bank
http://scientiairanica.sharif.edu/article_20493.html
10.24200/sci.2018.20493
1
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 multiperiod 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 fouryear strategic planning.
0

3728
3746


A.
Basar
Department of Industrial Engineering, Istanbul Technical University, Macka Campus 34357, Istanbul, Turkey.
Turkey


Ö.
Kabak
Department of Industrial Engineering, Istanbul Technical University, Macka Campus 34357, Istanbul, Turkey.
Turkey
kabak@itu.edu.tr


Y. I.
Topcu
Department of Industrial Engineering, Istanbul Technical University, Macka Campus 34357, Istanbul, Turkey.
Turkey
Integer programming
decision support system
Tabu search
case study
banking
location
[1. DemirgucKunt, A. and Maksimovic, V. Law, _nance, and _rm growth", J. Financ., 53, pp. 21072137 (1998). 2. Levine, R. and Zervos, S. Stock markets, banks, and economic growth", Am. Econ. Rev., 88, pp. 537558 (1998). 3. Turkish Banks Association, Banking and Sector Information (2017). http://www.tbb.org.tr/tr/bankavesektor bilgileri/ba nkabilgileri/subeler/65, accessed 15 March 2017. 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. 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. 2554 (2014). 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. 814826 (2008). 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. 117 (2002). 8. Cook, W.D., Seiford, L.M., and Zhu, J. Models for performance benchmarking: Measuring the e_ect of ebusiness activities on banking performance", Omega, 32, pp. 313322 (2004). 9. Camanho, A.S. and Dyson, R.G. Cost e_ciency measurement with price uncertainty: A DEA application to bank branch assessments", Eur. J. Oper. Res, 161, pp. 432446 (2005). 10. Portela, M.C.A.S. and Thanassoulis, E. Comparative e_ciency analysis of Portuguese bank branches", Eur. J. Oper. Res., 177, pp.12751288 (2007). 11. Paradi, J.C. and Zhu, H. A survey on bank branch e_ciency and performance research with data envelopment analysis", Omega, 41(1), pp. 6179 (2013). 12. Paradi, J.C., Min, E., and Yang, X. Evaluating Canadian bank branch operational e_ciency from sta_ allocation: A DEA approach", Management and Organizational Studies, 2(1), pp. 5265 (2015). 13. LaPlante, A.E. and Paradi, J.C. Evaluation of bank branch growth potential using data envelopment analysis", Omega, 52, pp. 3341 (2015). 14. Basar, A., Catay, B., and Unluyurt, T. A taxonomy for emergency service station location problem", Optim. Lett., 6(6), pp. 11471160 (2012). 15. Arabani, A.B. and Farahani, R.Z. Facility location dynamics: An overview of classi_cations and applications", Comput. Ind. Eng., 62, pp. 408420 (2012). 16. Miller, T.C., Friesz, T.L., Tobin, R.L., and Kwon, C. Reaction function based dynamic location modelling in StackelbergNashCournot competition", Netw. Spat. Econ., 7(1), pp. 7797 (2007). 17. Hale, T.S. and Moberg, C.R. Location science research: A review", Ann. Oper. Res., 123, pp. 2135 (2003). 18. Klose, A. and Drexl, A. Facility location models for distribution system design", Eur. J. Oper. Res., 162(1), pp. 429 (2005). 19. Melo, M.T., Nickel, S., and SaldanhadaGama, F. Facility location and supply chain managementA review", Eur. J. Oper. Res., 196(2), pp. 401412 (2009). 20. Wesolowky, G.O. and Truscott, W.G. The multiperiod locationallocation problem with relocation of facilities", Manage. Sci., 22, pp. 5765 (1975). 3744 A. Basar et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 3728{3746 21. Schilling, D.A. Dynamic location modeling for public sector facilities: A multi criteria approach", Decision Sci., 11, pp. 714724 (1980). 22. Gunawardane, G. Dynamic versions of set covering type public facility location problems", Eur. J. Oper. Res., 10(2), pp. 190195 (1982). 23. Galvao, R.D. and Gonzalez, S.E. A Lagrangean heuristic for the pkmedian dynamic location problem", Eur. J. Oper. Res., 58, pp. 250262 (1992). 24. Drezner, Z. Dynamic facility location: The progressive pmedian problem", Location Science, 3, pp. 17 (1995). 25. Chardaire, P., Sutter, A., and Costa, M.C. Solving the dynamic facility location problem", Networks, 28, pp. 117124 (1996). 26. Hormozi, A.M. and Khumawala, B.M. An improved algorithm for solving a multiperiod facility location problem", IIE Trans., 28(2), pp. 105114 (1996). 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. 597609 (1997). 28. Antunes, A. and Peeters, D. A dynamic optimization model for school network planning", Socio. Econ. Plan. Sci., 34(2), pp. 101120 (2000). 29. Antunes, A. and Peeters, D. On solving complex multiperiod location models using simulated annealing", Eur. J. Oper. Res., 130(1), pp. 190201 (2001). 30. Canel, C., Khumawala, B.M., Law, J., and Loh, A. An algorithm for the capacitated, multicommodity multiperiod facility location problem", Comput. Oper. Res., 28(5), pp. 411427 (2001). 31. Dias, J., Captivo, M.E., and Cl__maco, J. E_cient primaldual heuristic for a dynamic location Problem", Comput. Oper. Res., 34, pp. 18001823 (2007). 32. AlbaredaSambola, M., Fernandez, E., Hinojosa, Y., and Puerto, J. The multiperiod incremental service facility location problem", Comput. Oper. Res., 36(5), pp. 13561375 (2009). 33. Basar, A., Catay, B., and Unluyurt, T. A multiperiod double coverage approach for locating the emergency medical service stations in Istanbul", J. Oper. Res. Soc., 62(4), pp. 627637 (2011). 34. TorresSoto, J.E. and Uster, H. Dynamicdemand capacitated facility location problems with and without relocation", Int. J. Prod. Res., 49(13), pp. 39794005 (2011). 35. Sha, Y. and Huang, J. The multiperiod locationallocation problem of engineering emergency blood supply systems", Systems Engineering Procedia, 5, pp. 2128 (2012). 36. Ghaderi, A. and Jabalameli, M.S. Modeling the budgetconstrained dynamic uncapacitated facility locationnetwork design problem and solving it via two e_cient heuristics: A case study of health care", Math. Comput. Model., 57(3), pp. 382400 (2013). 37. Zarandi, M.H.F., Davari, S., and Sisakht, S.A.H. The largescale dynamic maximal covering location problem", Math. Comput. Model., 57(3), pp. 710719 (2013). 38. Miskovic, S., Stanimirovic, Z., and Grujicic, I. An e_ cient variable neighborhood search for solving a robust dynamic facility location problem in emergency service network", Electronic Notes in Discrete Mathematics, 47, pp. 261268 (2015). 39. Megiddo, N. Dynamic location problems", Ann. Oper. Res., 6(10), pp. 311319 (1986). 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. Da Gama, F.S. and Captivo, M.E. A heuristic approach for the discrete dynamic location problem", Location Science, 6, pp. 211223 (1998). 42. Clawson, C.J. Fitting branch locations, performance standards, and marketing strategies to local conditions", J. Marketing, 38, pp. 814 (1974). 43. Boufounou, P.V. Evaluating bank branch location and performance: A case study", Eur. J. Oper. Res., 87, pp. 389402 (1995). 44. Ravallion, M. and Wodon, Q. Banking on the poor? Branch location and nonfarm rural development in Bangladesh", Rev. Dev. Econ., 4, pp. 121139 (2000). 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. 3738 (2013). 46. Aggelopoulos, E. and Georgopoulos, A. Bank branch e_ciency under environmental change: a bootstrap DEA on monthly pro_t and loss accounting statements of Greek retail branches", Eur. J. Oper. Res., 261(3), pp. 11701188 (2017). 47. Cvetkoska, V. and Savi_c, G. E_ciency of bank branches: empirical evidence from a twophase research approach", Economic ResearchEkonomska Istra _zivanja, 30(1), pp. 318333 (2017). 48. Quaranta, A.G., Ra_oni, A., and Visani, F. A multidimensional approach to measuring bank branch e_ciency", Eur. J. Oper. Res., 266(2), pp. 746760 (2018). 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. 5986 (2017). 50. Min, H. A model based decision support system for locating banks", Inform. Manage., 17, pp. 207215 (1989). 51. Cinar, N. A decision support model for bank branch location selection", World Academy of Science, Engineering & Technology, 60, pp. 126131 (2009). A. Basar et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 3728{3746 3745 52. Rahgan, S.H. and Mirzazadeh, A. A new method in the location problem using fuzzy evidential reasoning", Engineering and Technology, 4(22), pp. 46364645 (2012). 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. 301330 (2001). 54. Gorener, A., Dincer, H., and Hacioglu, U. Application of multiobjective optimization on the basis of ratio analysis (MOORA) method for bank branch location selection", International Journal of Finance & Banking Studies, 2(2), pp. 4152 (2016). 55. Min, H., and Melachrinoudis, E. The threehierarchical locationallocation of banking facilities with risk and uncertainty", Int. T. Oper. Res., 8, pp. 381401 (2001). 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. 549565 (2002). 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. 20472069 (2003). 58. Monteiro, M. and Fontes, D. Locating and sizing bankbranches by opening, closing or maintaining facilities", Operat. Res. Proceed., pp. 303308 (2005). 59. Zhang, L. and Rushton, G. Optimizing the size and locations of facilities in competitive multisite service systems", Comput. Oper. Res., 35, pp. 327338 (2008). 60. Alexandris, G. and Giannikos, I. A new model for maximal coverage exploiting GIS capabilities", Eur. J. Oper. Res., 202, pp. 328338 (2010). 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. 654658 (2010). 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. 323337 (2004). 63. Badri, M.A. A combined AHPGP model for quality control systems", Int. J. Prod. Econ., 72, pp. 2740 (2001). 64. Saaty, T.L., The Analytic Hierarchy Process, New York, McGrawHill, Inc. (1980). 65. Ahsan, M.K. and Bartlema, J. Monitoring healthcare performance by analytic hierarchy process: A developingcountry perspective", Int. T. Oper. R., 11, pp. 465478 (2004). 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. 171187 (2002). 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. 14311444 (2007). 68. Fernandez, I. and Ruiz, M.C. Descriptive model and evaluation system to locate sustainable industrial areas", J. Clean. Prod., 17(1), pp. 87100 (2009). 69. Marianov, V., ReVelle, C.S., Facility Location, Berlin, Springer (1995). 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. 671677 (2012). 71. Naama, B., Bouzeboudja, H., and Allali, A. Application of tabu search and genetic algorithm in minimize losses in power system. Using the Bcoe_cient method", Energy. Proced., 36, pp. 687693 (2013). 72. Ros lon, J. and Zawistowski, J. Construction projects' indicators improvement using selected metaheuristic algorithms", Procedia Engineer., 153, pp. 595598 (2016). 73. Hamta, N., Fatemi Ghomi, S.M.T., Tavakkoli Moghaddam, R., and Jolai, F. A hybrid metaheuristic for balancing and scheduling assembly lines with sequenceindependent setup times by considering deterioration tasks and learning e_ect", Scientia Iranica, 21(3), pp. 963979 (2014). 74. Imanipour, N. and Zegordi S.H.A.D. A heuristic approach based on tabu search for early/tardy exible job shop problems", Scientia Iranica, 13(1), pp. 113 (2006). 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(14), pp. 328 (1993). 76. Zhang, G., Habenicht, W. and Spie_, W.E.L. Improving the structure of deep frozen and chilled food chain with tabu search procedure", J. Food Eng., 60(1), pp. 6779 (2003). 77. Grabowski, J. and Wodecki, M. A very fast tabu search algorithm for the permutation ow shop problem with makespan criterion", Comput. Oper. Res., 31, pp. 18911909 (2004).##]
1

A robust bilevel programming model for designing a closedloop supply chain considering government's collection policy
http://scientiairanica.sharif.edu/article_20609.html
10.24200/sci.2018.20609
1
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 bilevel 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 MinMax regret scenario based robust optimization approach. The results show the necessity of using robust bilevel programming in closed loop supply chain network design under the governmental legislative decisions as a leaderfollower configuration.
0

3747
3764


A.
Hassanpour
Department of Industrial Engineering, Faculty of Engineering and Technology, Alzahra University, Tehran, P.O. Box 199389373,
Iran.
Iran


J.
Bagherinejad
Department of Industrial Engineering, Faculty of Engineering and Technology, Alzahra University, Tehran, P.O. Box 199389373,
Iran.
Iran
jbagheri@alzahra.ac.ir


M.
Bashiri
Department of Industrial Engineering, Faculty of Engineering and Technology, Shahed University, Tehran, P.O. Box 319118651,
Iran.
Iran
bashiri@shahed.ac.ir
Bilevel Programming
Closedloop supply chain
Government regulations
Genetic Algorithm
robust optimization
Scenario
1

Multiobjective mathematical modeling of an integrated train makeup and routing problem in an Iranian railway company
http://scientiairanica.sharif.edu/article_20782.html
10.24200/sci.2018.20782
1
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 multiobjective 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 yarddemand 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.
0

3765
3779


R.
AlikhaniKooshkak
School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
Iran


R.
TavakkoliMoghaddam
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Postal Code: 1439957131, Iran; c.LCFC, Metz, France.
Iran


A.
Jamili
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Postal Code: 1439957131, Iran.
Iran


S.
Ebrahimnejad
Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.
Iran
ibrahimnejad@kiau.ac.ir
Train makeup and routing problem
Optimization with multiple objectives
Lp norm
Goaloriented optimization (GP)
Simulated annealing
[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. 351362 (1994). 2. Huddleston, J.R. and Pangotra, p.p. Regional and local economic impacts of transportation investments", Transportation Quarterly, 44(4), pp. 579594 (1990). 3. Yaghini, M., Momeni, M., and Sarmadi, M. A hybrid solution method for fuzzy train formation planning", Applied Soft Computing, 31, pp. 257265 (2015). 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. 402416 (2014). 5. Assad, A.A. Modelling of rail networks: Toward a routing/makeup model", Transportation Research Part B: Methodological, 14(1), pp. 101114 (1980). 6. Crainic, T., Ferland, J.A., and Rousseau, J.M. A tactical planning model for rail freight transportation", Transportation Science, 18(2), pp. 165184 (1984). 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. 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. Huntley, C.L., Brown, D.E., Sappington, D.E., and Markowicz, B.P. Freight routing and scheduling at CSX transportation", Interfaces, 25(3), pp. 5871 (1995). 10. Bagheri, M., Saccomanno, F., and Fu, L. Modeling hazardous materials risks for di_erent train makeup plans", Transportation Research Part E: Logistics and Transportation Review, 48(5), pp. 907918 (2012). 11. Sha_a, M.A., Sadjadi, S.J., and Jamili, A. Robust train formation planning", Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 224(2), pp. 7590 (2010). 12. Sun, Y., Cao, C., and Wu, C. Multiobjective optimization of train routing problem combined with train scheduling on a highspeed railway network", Transportation Research Part C: Emerging Technologies, 44, pp. 120 (2014). 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. 19471950 (2015). 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 tra_c", Transportation Science, 50(2), pp. 408 423 (2016). 15. Boysen, N., Emde, S., and Fliedner, M. The basic train makeup problem in shunting yards", OR Spectrum, 38(1), pp. 207233 (2016). 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. 167194 (2017). 17. Bahrami, F., Safari, H., TavakkoliMoghaddam, R., and Modarres Yazdi, M. On modeling doortodoor parcel delivery services in Iran", Iranian Journal of Management Studies, 9(4), pp. 883906 (2017). R. AlikhaniKooshkak et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 3765{3779 3779 18. GallardoBobadilla, R. and Doucette, J. A linear programming model for optimization of the railway blocking problem", Proceeding of the American Railway Engineering and MaintenanceofWay Association Annual Conference (AREMA 2014), Chicago, IL, September 28  October 1 (2014). 19. Yaghini, M., Momeni, M., and Sarmadi, M. An improved local branching approach for train formation planning", Applied Mathematical Modelling, 37(4), pp. 23002307 (2013). 20. Jamili, A. A mathematical model and a hybrid algorithm for robust periodic singletrack trainscheduling problem", International Journal of Civil Engineering, 15(1), pp. 6375 (2017). 21. TavakkoliMoghaddam, 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. 443454 (2008). 22. AlikhaniKooshkak, R., TavakkoliMoghaddam, R., Jamili, A., and Ebrahimnejad, S. Solving a multiobjective train makeup model with locomotive limitation by a _rey 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. Hwang, C.L. and Masud, A.S.M., Multiple Objective Decision MakingMethods and Applications: A stateof theArt Survey, (164). Springer Science & Business Media (2012). 24. Coello Coello, C.A. and Christiansen, A.D. MOSES: A multiobjective optimization tool for engineering design", Engineering Optimization, 31(3), pp. 337368 (1999). 25. Evans, G.W. An overview of techniques for solving multiobjective mathematical programs", Management Science, 30(11), pp. 12681282 (1984). 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. Saltelli, A., Chan, K., and Scott, E.M. (Eds.), Sensitivity Analysis, 1, New York: Wiley (2000). 28. Kirkpatrick, S., Gelatt, C.D., and Vecchi, M.P. Optimization by simulated annealing", Science, 220(4598), pp. 671680 (1983).##]
1

Simulationbased optimization of a stochastic supply chain considering supplier disruption: Agentbased modeling and reinforcement learning
http://scientiairanica.sharif.edu/article_20789.html
10.24200/sci.2018.20789
1
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 multiperiod 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 agentbased simulation approach has been developed to simulate the supply chain and transactions between retailers and unreliable suppliers. In addition, a Qlearning approach (as a method of reinforcement learning) has been developed to optimize the simulation procedure. Furthermore, different configurations for simulation procedure are analyzed. The Rnetlogo package is used to implement the algorithm. Also a numerical example has been solved using the proposed simulationoptimization 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 Qleaning approach.
0

3780
3795


A.
Aghaie
Department of Industrial Engineering, K.N. Toosi University of Technology, Pardis Street, Mollasadra Street, Vanaq Square,
Tehran, 1999143344, Iran
Iran


M.
Hajian Heidary
Department of Industrial Engineering, K.N. Toosi University of Technology, Pardis Street, Mollasadra Street, Vanaq Square,
Tehran, 1999143344, Iran
Iran
Supply chain management
simulation based optimization
reinforcement learning
demand uncertainty
supplier disruption
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Agentbased modeling of the immune system: NetLogo, a promising framework", BioMed Research International, 2, pp. 16 (2014). 7. Humann, J. and Madni, A.M. Integrated agentbased modeling and optimization in complex systems analysis", Procedia Computer Science, 28, pp. 818827 (2014). 8. Macal, C.M. Everything you need to know about agentbased modelling and simulation", Journal of Simulation, 10, pp. 144156 (2016). 9. Avci, M.G. and Selim, H. A multiobjective, simulationbased optimization framework for supply A. Aghaie and M. Hajian Heidary/Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 3780{3795 3795 chains with premium freights", Expert Systems with Applications, 67, pp. 95106 (2017). 10. Sutton, R.S. and Barto, A.G., Reinforcement Learning: An Introduction, MIT press, Cambridge (1998). 11. Gosavi, A. Reinforcement learning for longrun average cost", European Journal of Operational Research, 155, pp. 654674 (2004). 12. Merzifonluoglu, Y. and Feng, Y. Newsvendor problem with multiple unreliable suppliers", International Journal of Production Research, 52(1), pp. 221242 (2014). 13. Merzifonluoglu, Y. Impact of risk aversion and backup supplier on sourcing decisions of a _rm", International Journal of Production Research, 53(22), pp. 69376961 (2015). 14. Merzifonluoglu, Y. Integrated demand and procurement portfolio management with spot market volatility and option contracts", European Journal of Operational Research, 258(1), pp. 181192 (2017). 15. Bouakiz, M. and Sobel, M.J. Inventory control with an exponential utility criterion", Operations Research, 40(3), pp. 603608 (1992). 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. 502517 (2010). 17. Densing, M. Dispatch planning using newsvendor dual problems and occupation times: application to hydropower", European Journal of Operational Research, 228, pp. 321330 (2013). 18. Jalali, H. and Nieuwenhuyse, I.V. Simulation optimization in inventory replenishment: a classi_cation", IIE Transactions, 47, pp. 12171235 (2015). 19. Nikolopoulou, A. and Ierapetritou, M.G. Hybrid simulation based optimization approach for supply chain management", Computers & Chemical Engineering, 47, pp. 183193 (2012). 20. Kwon, O., Im, G.P., and Lee, K.C. MACESCM: A multiagent and casebased reasoning collaboration mechanism for supply chain management under supply and demand uncertainties", Expert Systems with Applications, 33(3), pp. 690705 (2007). 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. 949959 (2008). 22. Sun, R. and Zhao, G. Analyses about e_ciency of reinforcement learning to supply chain ordering management", IEEE 10th International Conference on Industrial Informatics, China (2012). 23. Dogan, I. and Guner, A.R. A reinforcement learning approach to competitive ordering and pricing problem", Expert Systems, 32(1), pp. 3948 (2015). 24. Jiang, C. and Sheng, Z. Casebased reinforcement learning for dynamic inventory control in a multiagent supplychain system", Expert Systems with Applications, 36(3), pp. 65206526 (2009). 25. Kim, C.O., Kwon, I.H., and Kwak, C. Multiagent based distributed inventory control model", Expert Systems with Applications, 37(7), pp. 51865191 (2010). 26. Mortazavi, A., Khamseh, A.A., and Azimi, P. Designing of an intelligent selfadaptive model for supply chain ordering management system", Engineering Applications of Arti_cial Intelligence, 37, pp. 207220 (2015). 27. Rabe, M. and Dross, F. A reinforcement learning approach for a decision support system for logistics networks", Winter Simulation Conference, USA (2015). 28. Zhou, J., Purvis, M., and Muhammad, Y. A combined modelling approach for multiagent collaborative planning in global supply chains", 8th International Symposium on Computational Intelligence and Design, China (2015). 29. Thiele, J. and Marries, R. NetLogo: introduction to the RNetLogo package", Journal of Statistical Software, 58, pp. 141 (2014). 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. 187201 (2017).##]
1

New ShewhartEWMA and ShewhartCUSUM control charts for monitoring process mean
http://scientiairanica.sharif.edu/article_20637.html
10.24200/sci.2018.4962.1011
1
In this paper, we propose new ShewhartEWMA (SEWMA) and ShewhartCUSUM (SCUSUM) control charts using the varied L ranked set sampling (VLRSS) for monitoring the process mean, namely the SEWMAVLRSS and SCUSUMVLRSS 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 SEWMAVLRSS and SCUSUMVLRSS 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.
0

3796
3818


M.
Awais
Department of Statistics, QuaidiAzam University, Islamabad, Pakistan
Pakistan
iawais3232@gmail.com


A.
Haq
Department of Statistics, QuaidiAzam University, Islamabad, Pakistan
Pakistan
aaabdulhaq@yahoo.com
Average Run Length
CUSUM
Control chart
EWMA
Perfect and imperfect rankings
Ranked set sampling
Statistical process control
[1. Page, E.S. Continuous inspection schemes", Biometrika, 41(12), pp. 100115 (1954). 2. Lucas, J.M. and Crosier, R.B. Fast initial response for CUSUM qualitycontrol schemes: Give your CUSUM a head start", Technometrics, 24(3), pp. 199205 (1982). 3. Lucas, J.M. Combined ShewhartCUSUM quality control schemes", Journal of Quality Technology, 14(2), pp. 5159 (1982). 4. Roberts, W.S. Control chart tests based on geometric moving averages", Technometrics, 1(3), pp. 239250 (1959). 5. Lucas, J.M. and Saccucci, M.S. Exponentially weighted moving average control schemes: Properties and enhancements", Technometrics, 32(1), pp. 112 (1990). 6. Knoth, S. Fast initial response features for EWMA control charts", Statistical Papers, 46(1), pp. 4764 (2005). 7. Chiu, W.C. Generally weighted moving average control charts with fast initial response features", Journal of Applied Statistics, 36(3), pp. 255275 (2009). 8. Abbas, N., Riaz, M., and Does, R.J.M.M. Mixed exponentially weighted moving average cumulative sum M. Awais and A. Haq/Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 3796{3818 3817 charts for process monitoring", Quality and Reliability Engineering International, 29(3), pp. 345356 (2013). 9. Haq, A. A new hybrid exponentially weighted moving average control chart for monitoring process mean", Quality and Reliability Engineering International, 29(7), pp. 10151025 (2013). 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. 697710 (2014). 11. McIntyre, G.A. A method for unbiased selective sampling, using ranked sets", Crop and Pasture Science, 3(4), pp. 385390 (1952). 12. Dell, T.R. and Clutter, J.L. Ranked set sampling theory with order statistics background", The International Biometric Society, 28(2), pp. 545555 (1972). 13. Stokes, S.L. Ranked set sampling with concomitant variables", Communications in Statistics Theory and Methods, 6(12), pp. 12071211 (1977). 14. Samawi, H.M., Ahmed, M.S., and AbuDayyeh, W. Estimating the population mean using extreme ranked set sampling", Biometrical Journal, 38(5), pp. 577586 (1996). 15. Muttlak, H.A. Median ranked set sampling", Journal of Applied Statistical Science, 6(4), pp. 245255 (1997). 16. Muttlak, H.A. Investigating the use of quartile ranked set samples for estimating the population mean", Applied Mathematics and Computation, 146(23), pp. 437443 (2003). 17. AlNasser, A.D. L ranked set sampling: A generalization procedure for robust visual sampling", Communications in Statistics  Simulation and Computation, 36(1), pp. 3343 (2007). 18. Haq, A., Brown, J., Moltchanova, E., and AlOmari, A.I. Varied L ranked set sampling scheme", Journal of Statistical Theory and Practice, 9(4), pp. 741767 (2015). 19. Salazar, R.D. and Sinha, A.K. Control chart x based on ranked set sampling", Comunicacion Tecnica, No. 19709 (PE/CIMAT) (1997). 20. Muttlak, H.A. and AlSabah, W. Statistical quality control based on ranked set sampling", Journal of Applied Statistics, 30(9), pp. 10551078 (2003). 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. 11851201 (2004). 22. AlOmari, 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. 745763 (2012). 23. AlSabah, W.S. Cumulative sum statistical control charts using ranked set sampling data", Pakistan Journal of Statistics, 26(2), pp. 365378 (2010). 24. Abujiya, M.R., Riaz, M., and Lee, M.H. Enhancing the performance of combined ShewhartEWMA charts", Quality and Reliability Engineering International, 29(8), pp. 10931106 (2013). 25. Abujiya, M.R., Riaz, M., and Lee, M.H. Improving the performance of combined Shewhartcumulative sum control charts", Quality and Reliability Engineering International, 29(8), pp. 11931206 (2013). 26. Awais, M. and Haq, A. An EWMA chart for monitoring process mean", Journal of Statistical Computation and Simulation, 88(5), pp. 10031025 (2018). 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. 7490 (2018). 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. 20112024 (2014). 29. Mehmood, R., Riaz, M., and Does, R.J.J.M. Control charts for location based on di_erent sampling schemes", Journal of Applied Statistics, 40(3), pp. 483 494 (2013). 30. Mehmood, R., Riaz, M., and Does, R.J.J.M. Quality quandaries: On the application of di_erent ranked set sampling schemes", Quality Engineering, 26(3), pp. 370378 (2014). 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. 13111332 (2014). 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. 15871610 (2015). 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. 16231640 (2015). 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. 15871610 (2015). 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. 13051325 (2015). 36. Haq, A., Brown, J., Moltchanova, E., and AlOmari, A.I. E_ect of measurement error on exponentially weighted moving average control charts under ranked set sampling schemes", Journal of Statistical Computation and Simulation, 85(6), pp. 12241246 (2015). 3818 M. Awais and A. Haq/Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 3796{3818 37. Abbasi, S.A. and Riaz, M. On dual use of auxiliary information for e_cient monitoring", Quality and Reliability Engineering International, 32(2), pp. 705714 (2016). 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. 627636 (2016). 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. Munir, W. and Haq, A. New cumulative sum control charts for monitoring process variability", Journal of Statistical Computation and Simulation, 87(15), pp. 28822899 (2017). 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. 317327 (1996). 42. Steiner, S.H. EWMA control charts with timevarying control limits and fast initial response", Journal of Quality Technology, 31(1), pp. 7586 (1999). 43. David, H.A. and Nagaraja, H.N., Order Statistics, 3rd Edn., John Wiley & Sons, Inc., Hoboken, New Jersey (2003). 44. Montgomery, D.C., Introduction to Statistical Quality Control, 6thEdn., John Wiley & Sons, Inc., New York (2007).##]
1

A Malmquist productivity index with the directional distance function and uncertain data
http://scientiairanica.sharif.edu/article_20698.html
10.24200/sci.2018.5259.1173
1
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 realworld problems is considered by using the best and worstcase 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.
0

3819
3834


N.
Aghayi
Department of Mathematics, Ardabil Branch, Islamic Azad University, Ardabil, Iran
Iran
nazila.aghayi@gmail.com


M.
Tavana
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
United States
tavana@lasalle.edu


B.
Maleki
Department of Mathematics, Ardabil Branch, Islamic Azad University, Ardabil, Iran
Iran
hodamaleki62@yahoo.com
Data envelopment analysis
Malmquist productivity index
Interval approach
directional distance function
undesirable outputs
[1. Farrell, M.J. The measurement of productive ef _ciency", Journal of the Royal Statistical Society, 120(3), pp. 253281 (1957). 2. Charnes, A., Cooper, W.W., and Rhodes, E. Measuring the e_ciency of decision making units", European Journal of Operational Research, 2(6), pp. 429444 (1978). 3. Banker, R.D., Charnes, A., and Cooper, W.W. Some models for estimating technical and scale ine_ciencies in data envelopment analysis", Management Science, 30(9), pp. 10781092 (1984). 4. Pittman, R.W. Multilateral productivity comparisons with undesirable outputs", Economic Journal, 93(372), pp. 883891 (1983). 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. 7386 (1982). 6. Ardabili, J.S., Aghayi, N., and Monzali, A.L. New ef _ciency using undesirable factors of data envelopment analysis", Modeling & Optimization, 9(2), pp. 249255 (2007). 7. Malmquist, S. Index numbers and indi_erence surfaces", Trabajos de Estatistica, 4(2), pp. 209 242 (1953). 8. Fare, R., Grosskopf, S., and Logan, J. The relative e_ciency of Illinois electric utilities", Resources and Energy, 5, pp. 349367 (1983). 9. Soyster, A.L. Convex programming with set inclusive constraints and applications to inexact linear programming", Operational Research, 21, pp. 11541157 (1972). 10. Seiford, L.M. and Zhu, J. Modeling undesirable factors ine_ciency valuation", European Journal of Operational Research, 142(1), pp. 1620 (2002). 11. Chambers, R.G., Chung, Y., and Fare, R. Bene_t and distance function", Journal of Economic Theory, 70(2), pp. 407419 (1996). 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. 229240 (1997). 13. Shepherd, R.W., Theory of Cost and Production Functions, Princeton, NJ, USA: Princeton University press (1970). 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. 517530 (2015). 15. Iftikhar, Y., Wang, Z., Zhang, B., and Wang, B. Energy and CO2 emissions e_ciency of major economies: A network DEA approach", Energy, 147, pp. 197207 (2018). 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. 4957 (2015). 17. Barnab_e, W. Disaggregation of the cost Malmquist productivity index with joint and outputspeci_c inputs", Omega, 75, pp. 112 (2018). 18. Sueyoshi, T., Goto, M., and Wang, D. Malmquist index measurement for sustainability enhancement in Chinese municipalities and provinces", Energy Economics, 67, pp. 554571 (2017). 19. Sueyoshi, T. and Goto, M. DEA environmental assessment in time horizon: Radial approach Malmquist index measurement on petroleum companies", Energy Economics, 51, pp. 329345 (2015). 20. Fuentes, R. and LilloBanuls, A. Smoothed bootstrap Malmquist index based on DEA model to compute productivity of tax o_ces", Expert Systems with Applications, 42, pp. 24422450 (2015). 21. Yu, C., Shi, L., Wang, Y., Chang, Y., and Cheng, B. The ecoe_ciency of pulp and paper industry in China: an assessment based on slacksbased measure and MalquistLuenberger index", Journal of Cleaner Production, 127, pp. 511521 (2016). 22. Kao, C. Measurement and decomposition of the Malmquist productivity index for parallel production systems", Omega, 67, pp. 5459 (2016). 23. Maroto, A. and Zo_o, J. Accessibility gains and road transport infrastructure in Spain: A productivity approach based on the Malmquist index", Journal of Transport Geography, 52, pp. 143152 (2016). 24. Emrouznejad, A., RostamyMalkhalifeh, M., Hatami Marbini, A., Tavana, M., and Aghayi, N. An overall pro_t Malmquist productivity index with fuzzy and interval data", Mathematical and Computer Modelling, 54(1112), pp. 28272838 (2011). 25. Wanke, P., Barros, C.P., and Emrouznejad, A. Assessing productive e_ciency of banks using integrated FuzzyDEA and bootstrapping a case of Mozambican banks", European Journal of Operational Research, 249(1), pp. 378389 (2016). 26. Mashayekhi, Z. and Omrani, H. An integrated multi objective MarkowitzDEA cross e_ciency model with fuzzy returns for portfolio selection problem", Operation Research, 38, pp. 19 (2016). 27. Aghayi, N. Cost e_ciency measurement with fuzzy data in DEA", Journal of Intelligent and Fuzzy Systems, 32, pp. 409420 (2017). 28. Toloo, M., Aghayi, N., and RostamyMalkhalifeh, M. Measuring overall pro_t e_ciency with interval data", Applied Mathematics and Computation, 201(12), pp. 640649 (2008). 3834 N. Aghayi et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 3819{3834 29. HatamiMarbini, A., Emrouznejad, A., and Agrell, P. Interval data without sign restrictions in DEA", Applied Mathematical Modelling, 38(78), pp. 2028 2036 (2014). 30. Salehpour, S. and Aghayi, N. The most revenue e_ ciency with price uncertainty", International Journal of Data Envelopment Analysis, 3, pp. 575592 (2015). 31. Kouvelis, P. and Yu, G., Robust Discrete Optimization and Its Applications, Kluwer Academic publishers Norwell, MA (1997). 32. BenTall, A. and Nemirovski, A. Robust convex optimization", Mathematical Operation Research, 23, pp. 769805 (1998). 33. ElGhaoui, L. and Lebret, H. Robust solutions to leastsquares problems to uncertain data matrices", Sima Journal on Matrix Analysis and Applications, 18, pp. 10351064 (1997). 34. Bertsimas, D. and Sim, M. The price of the robustness", Operation Research, 52, pp. 3553 (2004). 35. ZahediSeresht, M., Jahanshahloo, G.R., and Jablonsky, J. A robust data envelopment analysis model with di_erent scenarios", Applied Mathematical Modelling, 52, pp. 306319 (2017). 36. Youse_, S., Soltani, R., Saen, R.F., and Pishvaee, M.S. A robust fuzzy possibilistic programming for a new network GPDEA model to evaluate sustainable supply chains", Journal of Cleaner Production, 166, pp. 537549 (2017). 37. ChungCheng, L. Robust data envelopment analyses approaches for evaluating algorithmic performance", Computers and Industrial Engineering, 81, pp. 7889 (2015). 38. Mardani, M. and Salarpour, M. Measuring technical e_ciency of potato production in Iran using robust data envelopment analysis", Information Processing in Agriculture, 2(1), pp. 614 (2015). 39. Aghayi, N., Tavana, M., and Raayatpanah, M.A. Robust e_ciency measurement with common set of weights under varying degrees of conservatism and data uncertainty", European Journal of Industrial Engineering, 10(30), pp. 385405 (2016). 40. Aghayi, N. and Maleki, B. E_ciency measurement of DMUs with undesirable outputs under uncertainty based on the directional distance function: Application on Bank Industry", Energy, 112, pp. 376387 (2016). 41. Ray, C. and Desli, E. Productivity growth, technical progress, and e_ciency change in industrialized countries: comment", The American Economic Review, 87, pp. 10331039 (1997).##]
1

HartleyRoss type unbiased estimators of population mean using two auxiliary variables
http://scientiairanica.sharif.edu/article_20726.html
10.24200/sci.2018.5648.1397
1
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 HartleyRoss 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.
0

3835
3845


M.
Javed
a. Department of Mathematics, Institute of Statistics, Zhejiang University, Hangzhou 310027, China.
b. Department of Statistics, Government College University, Faisalabad, Pakistan
China
mariajaved@gcuf.edu.pk


M.
Irfan
a. Department of Mathematics, Institute of Statistics, Zhejiang University, Hangzhou 310027, China.
b. Department of Statistics, Government College University, Faisalabad, Pakistan
China
mirfan@zju.edu.cn


T.
Pang
Department of Mathematics, Institute of Statistics, Zhejiang University, Hangzhou 310027, China.
China
txpang@zju.edu.cn
Auxiliary variable
HartleyRoss type Estimator
Unbiased
Variance
[1. AbuDayyeh, W.A., Ahmed, M.S., Ahmed, R.A., and Muttlak, H.A. Some estimators of _nite population mean using auxiliary information", Applied Mathematics and Computation, 139, pp. 287298 (2003). 2. Kadilar, C. and Cingi, H. A new estimator using two auxiliary variables", Applied Mathematics and Computation, 162, pp. 901908 (2005). 3. Singh, H.P. and Tailor, R. Estimation of _nite population mean using known correlation coe_cient between auxiliary characters", Statistica, 65, pp. 407 418 (2005). 4. Lu, J. and Yan, Z. A class of ratio estimators of a _nite population mean using two auxiliary variables", PLOS ONE, 9(2), pp. 16 (2014). 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. 110 (2014). 6. Vishwakarma., G.K. and Kumar, M. A general family of dual to ratiocumproduct estimators of population mean in simple random sampling", Chilean Journal of Statistics, 6(2), pp. 6979 (2015). 7. Sharma, P. and Singh, R. A class of exponential ratio estimators of _nite population mean using two auxiliary variables", Pakistan Journal of Statistics and Operational Research, 11(2), pp. 221229 (2015). 8. Yasmeen, U., Amin, N.M., and Hanif, M. Exponential ratio and product type estimators of _nite population mean", Journal of Statistics and Management Systems, 19(1), pp. 5571 (2016). 9. Lu, J. E_cient estimator of a _nite 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. Muneer, S., Shabbir, J., and Khalil, A. Estimation of _nite population mean in simple random sampling and strati_ed random sampling using two auxiliary variables", Communications in Statistics Theory and Methods, 46(5), pp. 21812192 (2017). 11. Shabbir, J. and Gupta, S. Estimation of _nite population mean in simple and strati_ed random sampling using two auxiliary variables", Communications in Statistics Theory and Methods, 46(20), pp. 10135 10148 (2017). 12. Hartley, H.O. and Ross, A. Unbiased ratio estimators", Nature, 174, pp. 270272 (1954). 13. Robson, D.S. Application of multivariate polykays to the theory of unbiased ratio type estimation", Journal of American Statistical Association, 50, pp. 12251226 (1957). 14. Murthy, M.N. and Nanjamma, N.S. Almost unbiased estimator based on interpenetrating subsample estimates", Sankhya, 21, pp. 381392 (1959). M. Javed et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 3835{3845 3845 15. Biradar, R.S. and Singh, H.P. A note on almost unbiased ratiocumproduct estimator", Metron, 40(1 2), pp. 249255 (1992). 16. Biradar, R.S. and Singh, H.P. On a class of almost unbiased ratio estimators", Biomedical Journal, 34(8), pp. 937944 (1992). 17. Biradar, R.S. and Singh, H.P. A class of unbiased ratio estimators", Journal of Indian Society Agricultural Statistics, 47(3), pp. 230239 (1995). 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. 293298 (1994). 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. 547565 (2014). 20. Cekim, H.O. and Kadilar, C. New unbiased estimators with the help of HartleyRoss type estimators", Pakistan Journal of Statistics, 32(4), pp. 247260 (2016). 21. Khan, L., Shabbir, J., and Gupta, S. Unbiased ratio estimators of the mean in strati_ed ranked set sampling", Hacettepe Journal of Mathematics and Statistics, 46(6), pp. 11511158 (2017). 22. Singh, R. and Mangat, N.S., Elements of Survey Sampling, Norwell, MA: Kluwer Academic Publishers (1996). 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. 15521574 (2014). 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. Shabbir, J. and Gupta, S. On estimating _nite population mean in simple and strati_ed random sampling", Communications in Statistics Theory and Methods, 40(2), pp. 199212 (2011). 26. Kadilar, C. and Cingi, H., A New Ratio Estimator Using Correlation Coe_cient, InterStat, pp. 111 (2006). 27. Upadhyaya, L.N. and Singh, H.P. Use of transformed auxiliary variable in estimating the _nite population mean", Biometrical Journal, 41(5), pp. 627636 (1999). 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. 181191 (2007). 29. Koyuncu, N. and Kadilar, C. E_cient estimators for the population mean", Hacettepe Journal of Mathematics and Statistics, 38(2), pp. 217225 (2009). 30. Singh, V.K. and Singh, R. Performance of an estimator for estimating population mean using simple and strati_ed random sampling", SOP Transactions on Statistics and Analysis, 1(1), pp. 18 (2014). 31. Grover, L.K. and Kaur, P. An improved estimator of the _nite population mean in simple random sampling", Model Assisted Statistics and Application, 6(1), pp. 4755 (2011). 32. Koyuncu, N. and Kadilar, C. Family of estimators of population mean using two auxiliary variables in strati_ed sampling", Communications in Statistics Theory and Methods, 38, pp. 23982417 (2009).##]
1

An intelligent model for predicting the dayahead deregulated market clearing price: A hybrid NNPSOGA approach
http://scientiairanica.sharif.edu/article_20615.html
10.24200/sci.2018.50910.1909
1
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 KMeans 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 realworld electricity market for the one month of the year 20102013 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.
0

3846
3856


B.
Ostadi
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115111, Iran
Iran
bostadi@modares.ac.ir


O.
Motamedi Sedeh
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115111, Iran
Iran
omid.motamedi@modares.ac.ir


A.
Husseinzadeh Kashan
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115111, Iran
Iran
a.kashan@modares.ac.ir


M.R.
AminNaseri
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115111, Iran
Iran
amin_nas@modares.ac.ir
Neural network
Genetic Algorithm
particle swarm optimization
market clearing price
Pay as a bid
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