Genetic-algorithm-based simulation optimization considering a single stochastic constraint

European Journal of Operational Research

Volumn 236, Issue 1, 2014, Pages 113-125

  Tsai, Shing Chih ^a   Fu, Sheng Yang ^a  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

Feasibility determination; Genetic algorithm; Metaheuristics; Simulation; Simulation based optimization

Indexed keywords

FEASIBILITY DETERMINATION; META HEURISTICS; NEAR-OPTIMAL SOLUTIONS; SIMULATION; SIMULATION OPTIMIZATION; SIMULATION-BASED OPTIMIZATIONS; STATISTICAL GUARANTEE; STOCHASTIC CONSTRAINTS;

GENETIC ALGORITHMS; HEURISTIC ALGORITHMS; STOCHASTIC SYSTEMS;

OPTIMIZATION;

EID: 84900604181     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2013.11.034     Document Type: Article

References (48)

1. W. Abo-Hamad, and A. Arisha Simulation-based framework to improve patient experience in an emergency department European Journal of Operational Research 224 2013 154 166
2. S. Andradóttir, and S.H. Kim Fully sequential procedures for comparing constrained systems via simulation Naval Research Logistics 57 2010 403 421
3. A. Arreola-Risa, V.M. Giménez-García, and J.L. Martínez-Parra Optimizing stochastic production-inventory systems: A heuristic based on simulation and regression analysis European Journal of Operational Research 213 2011 107 118
4. J. Atlason, M.A. Epelman, and S.G. Henderson Call center staffing with simulation and cutting plane methods Annals of Operation Research 127 2004 333 358
5. Baker, J. E. (1987). Reducing bias and inefficiency in the selection algorithm. In Proceedings of the 2nd international conference on genetic algorithms (pp. 14-21).
6. D. Batur, and S.H. Kim Finding feasible systems in the presence of constraints on multiple performance measures ACM Transactions on Modeling and Computer Simulation 20 13 2010 1 26
7. M.S. Bazaraa, H.D. Sherali, and C.M. Shetty Nonlinear programming: Theory and algorithms 2006 John Wiley and Sons New York
8. S. Bhatnagar, N. Hemachandra, and V.K. Mishra Stochastic approximation algorithms for constrained optimization via simulation ACM Transactions on Modeling and Computer Simulation 21 15 2011 1 22
9. Boesel, J. (1999). Search and selection for large-scale stochastic optimization. Doctoral dissertation. Evanston, IL: Department of IEMS, Northwestern University.
10. J. Boesel, B.L. Nelson, and N. Ishii A framework for simulation- optimization software IIE Transactions 35 2003 221 230
11. J. Boesel, B.L. Nelson, and S.H. Kim Using ranking and selection to "clean up" after simulation optimization Operations Research 51 2003 814 825
12. J. Branke, S.E. Chick, and C. Schmidt Selecting a selection procedure Management Science 53 2007 1916 1932
13. M.T. Cezik, and P. L'Ecuyer Staffing multiskill call centers via linear programming and simulation Management Science 54 2008 310 323
14. E.J. Chen A revisit of two-stage selection procedures European Journal of Operational Research 210 2011 281 286
15. C.H. Chen, M. Fu, and L.H. Lee Efficient simulation budget allocation for selecting an optimal subset Informs Journal on Computing 20 2008 579 595
16. C.A.C. Coello Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state of the art Computer Methods in Applied Mechanics and Engineering 191 2002 1245 1287
17. X. Guan, C. Song, Y.-C. Ho, and Q. Zhao Constrained ordinal optimization - A feasibility model based approach Discrete Event Dynamics Systems 16 2006 279 299
18. Hong, L. J. (2004). Discrete optimization via simulation: algorithms and error control. Doctoral dissertation. Evanston, IL: Department of IEMS, Northwestern University.
19. L.J. Hong, and B.L. Nelson Discrete optimization via simulation using COMPASS Operations Research 54 2006 115 129
20. L.J. Hong, and B.L. Nelson A brief introduction to optimization via simulation M.D. Rossetti, R.R. Hill, B. Johansson, A. Dunkin, R.G. Ingalls, Proceedings of the 2009 winter simulation conference 2009 Institute of Electrical and Electronics Engineers Piscataway, NJ 75 85
21. S.C. Horng, S.Y. Lin, L.H. Lee, and C.H. Chen Memetic algorithm for real-time combinatorial stochastic simulation optimization problems with performance analysis IEEE Transactions on Cybernetics 43 2013 1495 1509
22. S.R. Hunter, and R. Pasupathy Optimal sampling laws for stochastically constrained simulation optimization on finite sets Informs Journal on Computing 25 2013 527 542
23. Jia, Q. S. (2009). Improved constrained ordinal optimization for simulation-based constrained optimization. In The proceedings of the joint 48th IEEE conference on decision and control and 28th Chinese control conference, Shanghai, P.R. China (pp. 2315-2320).
24. A. Kabirian, and S. Ólafsson Selection of the best with stochastic constraints M.D. Rossetti, R.R. Hill, B. Johansson, A. Dunkin, R.G. Ingalls, Proceedings of the 2009 winter simulation conference 2009 Institute of Electrical and Electronics Engineers Piscataway, NJ 584 589
25. S.H. Kim, and B.L. Nelson Selecting the best system S.G. Henderson, B.L. Nelson, Handbooks in operations research and management science: Simulation 2006 Elsevier Science Amsterdam 501 534
26. J.P.C. Kleijnen, W. van Beers, and I. van Nieuwenhuyse Constrained optimization in expensive simulation: Novel approach European Journal of Operational Research 202 2010 164 174
27. A.M. Law Simulation modeling and analysis 4th ed. 2007 McGraw-Hill New York
28. L.H. Lee, E.P. Chew, S. Teng, and Y. Chen Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem European Journal of Operational Research 189 2008 476 491
29. L.H. Lee, N.A. Pujowidianto, L.W. Li, C.H. Chen, and C.M. Yap Approximate simulation budget allocation for selecting the best design in the presence of stochastic constraints IEEE Transactions on Automatic Control 57 2012 2940 2945
30. D. Li, L.H. Lee, and Y.C. Ho Constraint ordinal optimization Information Sciences 148 2002 201 220
31. Y. Luo, and E. Lim Simulation-based optimization over discrete sets with noisy constraints S. Jain, R.R. Creasey, J. Himmelspach, K.P. White, M. Fu, Proceedings of the 2011 winter simulation conference 2011 Institute of Electrical and Electronics Engineers Piscataway, NJ 4013 4025
32. Z. Michalewicz Genetic algorithms + data structures = evolution algorithms 3rd ed. 1996 Springer Berlin
33. D.J. Morrice, and J. Butler Ranking and selection with multiple "targets" L.F. Perrone, F.P. Wieland, J. Liu, B.G. Lawson, D.M. Nicol, R.M. Fujimoto, Proceedings of the 2006 winter simulation conference 2006 Institute of Electrical and Electronics Engineers Piscataway, NJ 222 230
34. D. Nazzal, M. Mollaghasemi, H. Hedlund, and A. Bozorgi Using genetic algorithms and an indifference-zone ranking and selection procedure under common random numbers for simulation optimisation Journal of Simulation 6 2012 56 66
35. B.L. Nelson, J. Swann, D. Goldsman, and W. Song Simple procedures for selecting the best simulated system when the number of alternatives is large Operations Research 49 2001 950 963
36. S. Ólafsson Metaheuristics S.G. Henderson, B.L. Nelson, Handbooks in operations research and management science: Simulation 2006 Elsevier Science Amsterdam 633 654
37. C. Park, and S.H. Kim Handling stochastic constraints in discrete optimization via simulation S. Jain, R.R. Creasey, J. Himmelspach, K.P. White, M. Fu, Proceedings of the 2011 winter simulation conference 2011 Institute of Electrical and Electronics Engineers Piscataway, NJ 4217 4226
38. N.A. Pujowidianto, S.R. Hunter, R. Pasupathy, L.H. Lee, and C.H. Chen Closed-form sampling laws for stochastically constrained simulation optimization on large finite sets C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, A.M. Uhrmacher, Proceedings of the 2012 winter simulation conference 2012 Institute of Electrical and Electronics Engineers Piscataway, NJ 1 10
39. Ray, T.; Kang, T.; Chye, S. K. (2000). An evolutionary algorithm for constrained optimization. In Whitley, D.; Goldberg, D.; Cantú, E. (Eds.), Proceedings of the 2000 genetic and evolutionary computing conference, San Francisco, CA (pp. 771-777).
40. Y. Rinott On two-stage selection procedures and related probability-inequalities Communications in Statistics - Theory and Methods A7 1978 799 811
41. T.P. Runarsson, and X. Yao Stochastic ranking for constrained evolutionary optimization IEEE Transactions on Evolutionary Computation 4 2000 284 294
42. R. Szechtman, and E. Yücesan A new perspective on feasibility determination S.J. Mason, R.R. Hill, L. Mönch, O. Rose, T. Jefferson, J.W. Fowler, Proceedings of the 2008 winter simulation conference 2008 Institute of Electrical and Electronics Engineers Piscataway, NJ 273 280
43. S.C. Tsai Rapid screening procedures for zero-one optimization via simulation Informs Journal on Computing 25 2013 317 331
44. S.C. Tsai, and I.H. Chu Controlled multistage selection procedures for comparison with a standard European Journal of Operational Research 223 2012 709 721
45. S.C. Tsai, and C.H. Kuo Screening and selection procedures with control variates and correlation induction techniques Naval Research Logistics 59 2012 340 361
46. H. Vieira Junior, K.H. Kienitz, and M.C.N. Belderrain Discrete-valued, stochastic-constrained simulation optimization with COMPASS S. Jain, R.R. Creasey, J. Himmelspach, K.P. White, M. Fu, Proceedings of the 2011 winter simulation conference 2011 Institute of Electrical and Electronics Engineers Piscataway, NJ 4191 4200
47. J. Xu, B.L. Nelson, and L.J. Hong Industrial strength COMPASS: A comprehensive algorithm and software for optimization via simulation ACM Transactions on Modeling and Computer Simulation 20 2010 1 29
48. T.Y. Yu, C. Tsai, and H.T. Huang Applying simulation optimization to the asset allocation of a property-casualty insurer European Journal of Operational Research 207 2010 499 507