Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem.

*(English)*Zbl 1149.90355Summary: Simulation optimization has received considerable attention from both simulation researchers and practitioners. In this study, we develop a solution framework which integrates multi-objective evolutionary algorithm (MOEA) with multi-objective computing budget allocation (MOCBA) method for the multi-objective simulation optimization problem. We apply it on a multi-objective aircraft spare parts allocation problem to find a set of non-dominated solutions. The problem has three features: huge search space, multi-objective, and high variability. To address these difficulties, the solution framework employs simulation to estimate the performance, MOEA to search for the more promising designs, and MOCBA algorithm to identify the non-dominated designs and efficiently allocate the simulation budget. Some computational experiments are carried out to test the effectiveness and performance of the proposed solution framework.

##### MSC:

90B50 | Management decision making, including multiple objectives |

90B05 | Inventory, storage, reservoirs |

90C29 | Multi-objective and goal programming |

##### Keywords:

multi-objective simulation optimization; evolutionary computing; multi-objective computing budget allocation; Pareto optimality; spare parts inventory problem
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\textit{L. H. Lee} et al., Eur. J. Oper. Res. 189, No. 2, 476--491 (2008; Zbl 1149.90355)

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##### References:

[1] | Ahmed, M.A.; Alkhamis, T.M., Simulation-based optimization using simulated annealing with ranking and selection, Computers and operations research, 29, 387-402, (2002) · Zbl 0994.90102 |

[2] | Alkhamis, T.M.; Ahmed, M.A., Simulation-based optimization using simulated annealing with confidence interval, Proceedings of the 2004 winter simulation conference, 514-519, (2004) |

[3] | Alkhamis, T.M.; Ahmed, M.A.; Tuan, V.K., Simulated annealing for discrete optimization with estimation, European journal of operational research, 116, 530-544, (1999) · Zbl 1009.90076 |

[4] | Alrefaei, M.H.; Alawneh, A.J., Selecting the best stochastic system for large scale problems in DEDS, Mathematics and computers in simulation, 64, 237-245, (2004) · Zbl 1051.93062 |

[5] | Alrefaei, M.H.; Andradóttir, S., Discrete stochastic optimization via a modification of the stochastic ruler method, Proceedings of the 1996 winter simulation conference, 406-411, (1996) |

[6] | Alrefaei, M.H.; Andradóttir, S., Accelerating the convergence of the stochastic ruler method for discrete stochastic optimization, Proceedings of the 1997 winter simulation conference, 352-357, (1997) |

[7] | Andradóttir, S., Accelerating the convergence of random search methods for discrete stochastic optimization, ACM transactions on modeling and computer simulation, 9, 4, 349-380, (1999) · Zbl 1391.65019 |

[8] | Baesler, F.F.; Sepúlveda, J.A., Multi-response simulation optimization using stochastic genetic search within a goal programming framework, Proceedings of the 2000 winter simulation conference, 788-794, (2000) |

[9] | Batchoun, P.; Ferland, J.A.; Cléroux, R., Allotment of aircraft spare parts using genetic algorithms, Pesquisa operacional, 23, 1, 141-159, (2003) |

[10] | Boesel, J.; Nelson, B.L.; Ishii, N., A framework for simulation-optimization software, IIE transactions, 35, 221-229, (2003) |

[11] | Butler, J.; Morrice, D.J.; Mullarkey, P.W., A multiple attribute utility theory approach to ranking and selection, Management science, 47, 6, 800-816, (2001) · Zbl 1232.91214 |

[12] | Chen, H.C.; Dai, L.; Chen, C.H.; Yücesan, E., New development of optimal computing budget allocation for discrete event simulation, In Proceedings of the 1997 winter simulation conference, 334-341, (1997) |

[13] | Chen, C.H.; Lin, J.W.; Yücesan, E.; Chick, S.E., Simulation budget allocation for further enhancing the efficiency of ordinal optimization, Discrete event dynamic systems: theory and applications, 10, 251-270, (2000) · Zbl 0970.90014 |

[14] | Chick, S.E., Selecting the best system: A decision theoretic approach, In Proceedings of the 1997 winter simulation conference, 326-333, (1997) |

[15] | Chick, S.E.; Inoue, K., New two-stage and sequential procedures for selecting the best simulated system, Operations research, 49, 5, 732-743, (2001) |

[16] | Diaz, A.; Fu, M., Models for multi-echelon repairable item inventory systems with limited repair capacity, European journal of operational research, 97, 480-492, (1997) · Zbl 0927.90003 |

[17] | Fonseca, C.M.; Fleming, P.J., Genetic algorithms for multiobjective optimization: formulation, discussion and generalization, Genetic algorithms: Proceedings of the fifth international conference, (1993), Morgan Kaufmann San Mateo, CA, pp. 416-423 |

[18] | Fonseca, C.M.; Fleming, P.J., An overview of evolutionary algorithms in multiobjective optimization, Evolutionary computation, 3, 1, 1-16, (1995) |

[19] | Gong, W.B.; Ho, Y.C.; Zhai, W.G., Stochastic comparison algorithm for discrete optimization with estimation, SIAM journal on control and optimization, 10, 384-404, (1999) · Zbl 0957.60075 |

[20] | Hanne, T.; Nickel, S., A multiobjective evolutionary algorithm for scheduling and inspection planning in software development projects, European journal of operational research, 167, 663-678, (2005) · Zbl 1077.90060 |

[21] | Hedlund, H.E.; Mollaghasemi, M., A genetic algorithm and an indifference-zone ranking and selection framework for simulation optimization, Proceedings of the 2001 winter simulation conference, 417-421, (2001) |

[22] | Kennedy, W.J.; Patterson, J.W.; Fredendall, L.D., An overview of recent literature on spare parts inventories, International journal of production economics, 76, 201-215, (2002) |

[23] | Kim, J.; Shin, K.; Yu, H., Optimal algorithm to determine the spare inventory level for a repairable-item inventory system, Computers and operations research, 23, 3, 289-297, (1996) · Zbl 0855.90052 |

[24] | Lee, L.H.; Chew, E.P.; Teng, S.Y.; Goldsman, D., Optimal computing budget allocation for multi-objective simulation models, Proceedings of the 2004 winter simulation conference, 586-594, (2004) |

[25] | Lee, L.H., Chew, E.P., Teng, S.Y., Goldsman, D., submitted for publication. Finding the non-dominated Pareto set for multi-objective simulation models. Submitted to IIE Transactions. |

[26] | Lee, L.H.; Lee, C.; Tan, Y.P., A multi-objective genetic algorithm for robust flight scheduling using simulation, European journal of operational research, 177, 3, 1948-1968, (2007) · Zbl 1102.90323 |

[27] | Morrice, D.J.; Butler, J.; Mullarkey, P.W., An approach to ranking and selection for multiple performance measures, Proceedings of the 1998 winter simulation conference, 719-725, (1998) |

[28] | Nelson, B.L.; Swann, J.; Goldsman, D.; Song, W.M., Simple procedures for selecting the best simulated system when the number of alternatives is large, Operations research, 49, 950-963, (2001) |

[29] | Ólafsson, S., Iterative ranking-and-selection for large-scale optimization, Proceedings of the 1999 winter simulation conference, 479-485, (1999) |

[30] | Papadopoulos, H., A field service support system using a queuing network model and the priority mva algorithm, Omega, 24, 2, 195-203, (1996) |

[31] | Rinott, Y., On two-stage selection procedures and related probability-inequalities, Communications in statistics, A7, 8, 799-811, (1978) · Zbl 0392.62020 |

[32] | Rosen, S.L.; Harmonosky, C.M., An improved simulated annealing simulation optimization method for discrete parameter stochastic systems, Computers and operations research, 32, 343-358, (2005) · Zbl 1073.90026 |

[33] | Sherbrooke, C.C., METRIC: A multi-echelon technique for recoverable item control, Operations research, 16, 122-141, (1967) |

[34] | Swisher, J.R.; Jacobson, S.H., Evaluating the design of a family practice healthcare clinic using discrete-event simulation, Health care management science, 5, 2, 75-88, (2002) |

[35] | Swisher, J.R.; Jacobson, S.H.; Yücesan, E., Simulation optimization using ranking, selection, and multiple comparison procedures: A survey, ACM transactions on modeling and computer simulation, 13, 2, 134-154, (2003) · Zbl 1390.65025 |

[36] | Tan, K.C.; Khor, E.F.; Lee, T.H.; Sathikannan, R., An evolutionary algorithm with advanced goal and priority specification for multi-objective optimization, Journal of artificial intelligence research, 18, 183-215, (2003) · Zbl 1056.68125 |

[37] | Teleb, R.; Azadivar, F., A methodology for solving multi-objective simulation-optimization problems, European journal of operational research, 72, 135-145, (1994) · Zbl 0798.90090 |

[38] | Yan, D.; Mukai, H., Stochastic discrete optimization, SIAM journal on control and optimization, 30, 594-612, (1992) · Zbl 0764.90066 |

[39] | Yang, S.M., Shao, D.G., Luo, Y.J., 2005. A novel evolution strategy for multi-objective optimization problem. Applied Mathematics and Computation. |

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