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A new approach to reducing search space and increasing efficiency in simulation optimization problems via the fuzzy-DEA-BCC. (English) Zbl 1407.90382
Summary: The development of discrete-event simulation software was one of the most successful interfaces in operational research with computation. As a result, research has been focused on the development of new methods and algorithms with the purpose of increasing simulation optimization efficiency and reliability. This study aims to define optimum variation intervals for each decision variable through a proposed approach which combines the data envelopment analysis with the Fuzzy logic (Fuzzy-DEA-BCC), seeking to improve the decision-making units’ distinction in the face of uncertainty. In this study, Taguchi’s orthogonal arrays were used to generate the necessary quantity of DMUs, and the output variables were generated by the simulation. Two study objects were utilized as examples of mono- and multiobjective problems. Results confirmed the reliability and applicability of the proposed method, as it enabled a significant reduction in search space and computational demand when compared to conventional simulation optimization techniques.

90C70 Fuzzy and other nonstochastic uncertainty mathematical programming
90C08 Special problems of linear programming (transportation, multi-index, data envelopment analysis, etc.)
Full Text: DOI
[1] Fu, M. C., Optimization for simulation: theory vs. practice, INFORMS Journal on Computing, 14, 3, 192-215, (2002) · Zbl 1238.90001
[2] Hillier, F. S.; Lieberman, G. J., Introduction to Operations Research, (2010), New York, NY, USA: McGraw-Hill, New York, NY, USA · Zbl 0155.28202
[3] Fu, M. C.; Andradottir, S.; Carson, J. S.; Glover, F.; Harrell, C. R.; Ho, Y.-C.; Kelly, J. P.; Robinson, S. M., Integrating optimization and simulation: research and practice, Proceedings of the Winter Simulation Conference
[4] Banks, J.; Carson, J. S.; Nelson, B. L.; Nicol, D. M., Discrete Event Simulation, (2005), Upper Saddle River, NJ, USA: Prentice-Hall, Upper Saddle River, NJ, USA
[5] Azadeh, A.; Tabatabaee, M.; Maghsoudi, A., Design of intelligent simulation software with capability of optimization, Australian Journal of Basic and Applied Sciences, 3, 4, 4478-4483, (2009)
[6] Medaglia, A. L.; Fang, S.-C.; Nuttle, H. L. W., Fuzzy controlled simulation optimization, Fuzzy Sets and Systems, 127, 1, 65-84, (2002) · Zbl 1005.68173
[7] April, J.; Glover, F.; Kelly, J. P.; Laguna, M., Practical introduction to simulation optimization, Proceedings of the Winter Simulation Conference: Driving Innovation
[8] Banks, J., Panel session: the future of simulation, Proceedings of the Winter Simulation Conference
[9] Harrel, C. R.; Ghosh, B. K.; Bowden, R., Simulation Using ProModel, (2004), New York, NY, USA: McGraw-Hill, New York, NY, USA
[10] Kleijnen, J. P. C.; van Beers, W.; van Nieuwenhuyse, I., Constrained optimization in expensive simulation: novel approach, European Journal of Operational Research, 202, 1, 164-174, (2010) · Zbl 1189.90156
[11] Taguchi, G., System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Costs, (1987), Dearborn, Mich, USA: UNIPUB/Kraus International Publications, Dearborn, Mich, USA
[12] Banker, R. D.; Charnes, A.; Cooper, W. W., Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science, 30, 9, 1078-1092, (1984) · Zbl 0552.90055
[13] Kao, C.; Liu, S.-T., Fuzzy efficiency measures in data envelopment analysis, Fuzzy Sets and Systems, 113, 3, 427-437, (2000) · Zbl 0965.90025
[14] Ross, P. J., Taguchi Techniques for Quality Engineering, (1996), New York, NY, USA: McGraw-Hill, New York, NY, USA
[15] Charnes, A.; Cooper, W. W.; Rhodes, E., Measuring the efficiency of decision making units, European Journal of Operational Research, 2, 6, 429-444, (1978) · Zbl 0416.90080
[16] Cooper, W. W.; Sieford, L. M.; Tone, K., Data Envelopment Analysis: A Comprehensive Text with Models, Application, References and DEA-Solver Software, (2007), New York, NY, USA: Springer Science + Business Media, New York, NY, USA · Zbl 1111.90001
[17] Cook, W. D.; Seiford, L. M., Data envelopment analysis (DEA)—thirty years on, European Journal of Operational Research, 192, 1, 1-17, (2009) · Zbl 1180.90151
[18] Weng, S.-J.; Tsai, B.-S.; Wang, L.-M.; Chang, C.-Y.; Gotcher, D., Using simulation and data envelopment analysis in optimal healthcare efficiency allocations, Proceedings of the Winter Simulation Conference (WSC ’11)
[19] Andersen, P.; Petersen, N. C., A procedure for ranking efficient units in data envelopment analysis, Management Science, 39, 1261-1264, (1993) · Zbl 0800.90096
[20] Xue, M.; Harker, P. T., Note: ranking DMUs with infeasible super-efficiency DEA models, Management Science, 48, 5, 705-710, (2002) · Zbl 1232.90306
[21] Hatami-Marbini, A.; Emrouznejad, A.; Tavana, M., A taxonomy and review of the fuzzy data envelopment analysis literature: two decades in the making, European Journal of Operational Research, 214, 3, 457-472, (2011) · Zbl 1219.90199
[22] Wen, M.; Qin, Z.; Kang, R., Sensitivity and stability analysis in fuzzy data envelopment analysis, Fuzzy Optimization and Decision Making, 10, 1, 1-10, (2011) · Zbl 1304.90114
[23] Lertworasirikul, S.; Fang, S.-C.; Joines, J. A.; Nuttle, H. L. W., Fuzzy data envelopment analysis (DEA): a possibility approach, Fuzzy Sets and Systems, 139, 2, 379-394, (2003) · Zbl 1047.90080
[24] Liang, G.-S.; Wang, M.-J. J., Evaluating human reliability using fuzzy relation, Microelectronics Reliability, 33, 1, 63-80, (1993)
[25] Aouni, B.; Martel, J. M.; Hassaine, A., Fuzzy goal programming model: an overview of the current state-of-the art, Journal of Multi-Criteria Decision Analysis, 16, 149-161, (2009) · Zbl 1205.90306
[26] Kaufmann, A., Introduction to the Theory of Fuzzy Subsets, xvi+416, (1975), New York, NY, USA: Academic Press, New York, NY, USA
[27] Zadeh, L. A., Outline of new approach to the analysis of complex systems and decision processes, IEEE Transactions on Systems, Man and Cybernetics, 3, 1, 28-44, (1973) · Zbl 0273.93002
[28] Zadeh, L. A., Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, 1, 1, 3-28, (1978) · Zbl 0377.04002
[29] Yager, R. R., A characterization of the extension principle, Fuzzy Sets and Systems, 18, 3, 205-217, (1986) · Zbl 0628.04005
[30] Zimmermann, H.-J., Fuzzy Set Theory—and Its Applications, (1991), Boston, Mass, USA: Kluwer-Nijhoff Publishing, Boston, Mass, USA · Zbl 0719.04002
[31] Kao, C.; Lin, P.-H., Efficiency of parallel production systems with fuzzy data, Fuzzy Sets and Systems, 198, 83-98, (2012) · Zbl 1251.90108
[32] Wang, R.-C.; Liang, T.-F., Application of fuzzy multi-objective linear programming to aggregate production planning, Computers and Industrial Engineering, 46, 1, 17-41, (2004)
[34] SimRunner User Guide, (2006), Orem, Utah, USA: ProModel Corporation, Orem, Utah, USA
[35] Nageshwaraniyer, S. S.; Son, Y. J.; Dessureault, S., Simulation-based optimal planning for material handling networks in mining, Simulation: Transactions of the Society for Modeling and Simulation International, 89, 330-345, (2013)
[36] Kim, W. K.; Yoon, K. P.; Kim, Y.; Bronson, G. J., Improving system performance for stochastic activity network: a simulation approach, Computers and Industrial Engineering, 62, 1, 1-12, (2012)
[37] Ólafsson, S.; Henderson, S. G.; Nelson, B. L., Metaheuristics, Handbooks in Operations Research and Management Science, 633-654, (2006), Elsevier
[38] Bal, H.; Örkcü, H. H.; Çelebioğlu, S., Improving the discrimination power and weights dispersion in the data envelopment analysis, Computers & Operations Research, 37, 1, 99-107, (2010) · Zbl 1171.90418
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