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Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem
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AI
DOI：10.1016/j.ejor.2007.05.036.png)
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Abstract
En 中文
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. (C) 2007 Elsevier B.V. All rights reserved.
Keywords:
multi-objective simulation optimization
evolutionary computing
multi-objective computing budget allocation
Pareto optimality
spare parts inventory problem
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W