Spare Parts Supply Optimization and Decision-making Based on Cross-efficiency Multi-objective Sorting Evolutionary Algorithm

doi:10.3969/j.issn.1000-1093.2020.11.022

Abstract

Abstract: A multi-objective optimization model is established with the constraints of spare parts satisfaction, lead time and inventory capacity for the shortest total supply time, the lowest risk and the minimum cost. The proposed model is used to optimize the spare parts supply and make an optimal supply scheme. The set of non-dominant solutions are obtained by using the proposed secondary goal cross-efficiency sort multi-objective evolutionary algorithm (SGCES-MOEA). During optimization process, the improved data envelopment analysis (DEA) is used to calculate the cross-efficiencies of the non-dominant solutions. On the one hand, the algorithm is guided to converge to the optimal efficiency individuals. On the other hand, the non-dominant solutions are sorted to select an optimal one. The example shows that 13 non-dominate spare parts supply schemes are obtained by using SGCES-MOEA algorithm, and the scheme with cross-efficiency of 0.927 8 is determined as the optimal scheme. The new algorithm is superior to the multi-objective evolutionary algorithm without efficiency sorting strategy and that with self-evaluation efficiency sorting strategy.

Key words: sparepartssupply, multi-objectiveoptimization, evolutionarycomputation, dataenvelop-mentanalysis, cross-efficiency, sorting

CLC Number:

WANG Yadong， SHI Quan， YOU Zhifeng， WANG Fang， XIA Wei. Spare Parts Supply Optimization and Decision-making Based on Cross-efficiency Multi-objective Sorting Evolutionary Algorithm[J]. Acta Armamentarii, 2020, 41(11): 2338-2346.

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