Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services
Simulation is a powerful tool for modeling complex systems with intricate relationships between various entities and resources. Simulation optimization refers to methods that search the design space (i.e., the set of all feasible system configurations) to find a system configuration (also called a design point) that gives the best performance. Since simulation is often time consuming, sampling as few design points from the design space as possible is desired. However, in the case of multiple objectives, traditional simulation optimization methods are ineffective to uncover the efficient frontier. We propose a framework for multi-objective simulation optimization that combines the power of genetic algorithm (GA), which can effectively search very large design spaces, with data envelopment analysis (DEA) used to evaluate the simulation results and guide the search process. In our framework, we use a design point's relative efficiency score from DEA as its fitness value in the selection operation of GA. We apply our algorithm to determine optimal resource levels in surgical services. Our numerical experiments show that our algorithm effectively furthers the frontier and identifies efficient design points.
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Volume (Year): 41 (2013)
Issue (Month): 5 ()
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- Vadde, Srikanth & Zeid, Abe & Kamarthi, Sagar V., 2011. "Pricing decisions in a multi-criteria setting for product recovery facilities," Omega, Elsevier, vol. 39(2), pages 186-193, April.
- Lovell, C. A. Knox & Pastor, Jesus T., 1999. "Radial DEA models without inputs or without outputs," European Journal of Operational Research, Elsevier, vol. 118(1), pages 46-51, October.
- Chang, Shyr-Juh & Hsiao, Hsing-Chin & Huang, Li-Hua & Chang, Hsihui, 2011. "Taiwan quality indicator project and hospital productivity growth," Omega, Elsevier, vol. 39(1), pages 14-22, January.
- Lee, Loo Hay & Chew, Ek Peng & Teng, Suyan & Chen, Yankai, 2008. "Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem," European Journal of Operational Research, Elsevier, vol. 189(2), pages 476-491, September.
- Francesca Guerriero & Rosita Guido, 2011. "Operational research in the management of the operating theatre: a survey," Health Care Management Science, Springer, vol. 14(1), pages 89-114, March.
- Whittaker, Gerald & Confesor Jr., Remegio & Griffith, Stephen M. & Färe, Rolf & Grosskopf, Shawna & Steiner, Jeffrey J. & Mueller-Warrant, George W. & Banowetz, Gary M., 2009. "A hybrid genetic algorithm for multiobjective problems with activity analysis-based local search," European Journal of Operational Research, Elsevier, vol. 193(1), pages 195-203, February.
- Yun, Y. B. & Nakayama, H. & Arakawa, M., 2004. "Multiple criteria decision making with generalized DEA and an aspiration level method," European Journal of Operational Research, Elsevier, vol. 158(3), pages 697-706, November.
- White, T. P. & Toland, R. & Jackson, J. A. & Kloeber, J. M., 1996. "Simulation and optimization of a new waste remediation process," Omega, Elsevier, vol. 24(6), pages 705-714, December.
- Timo Kuosmanen & Mika Kortelainen, 2012. "Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints," Journal of Productivity Analysis, Springer, vol. 38(1), pages 11-28, August.
- Caramia, M. & Guerriero, F., 2009. "A heuristic approach to long-haul freight transportation with multiple objective functions," Omega, Elsevier, vol. 37(3), pages 600-614, June.
- Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
- Calvete, Herminia I. & Galé, Carmen, 2011. "On linear bilevel problems with multiple objectives at the lower level," Omega, Elsevier, vol. 39(1), pages 33-40, January.
- Golany, B & Roll, Y, 1989. "An application procedure for DEA," Omega, Elsevier, vol. 17(3), pages 237-250.
- Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
- Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
- Dyson, R. G. & Allen, R. & Camanho, A. S. & Podinovski, V. V. & Sarrico, C. S. & Shale, E. A., 2001. "Pitfalls and protocols in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 245-259, July.
- Yun, Y. B. & Nakayama, H. & Tanino, T. & Arakawa, M., 2001. "Generation of efficient frontiers in multi-objective optimization problems by generalized data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 129(3), pages 586-595, March.
- Brian Denton & James Viapiano & Andrea Vogl, 2007. "Optimization of surgery sequencing and scheduling decisions under uncertainty," Health Care Management Science, Springer, vol. 10(1), pages 13-24, February.
- Diewert, W E, 1980. "Capital and the Theory of Productivity Measurement," American Economic Review, American Economic Association, vol. 70(2), pages 260-67, May.
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