Abstract
In order to optimize the spare parts supply network, a multi-objective optimization model is established with the objectives of the shortest supply time, the lowest risk, and the minimum supply cost. A decomposition-based multi-objective evolutionary algorithm with differential evolution strategy is introduced to solve the multi-objective model. A series of non-dominated solutions, that is, representing the optimal spare parts supply schemes are obtained. In order to comprehensively measure the performance of these solutions, suitable quantitative metrics are selected, and the secondary goal-based cross-efficiency Data Envelopment Analysis (DEA) model has been used to evaluate the efficiency of the obtained optimal schemes. The improved DEA model overcomes the problems that the efficient units cannot be sorted and the optimal weight is not unique in traditional DEA model. Finally, the self-evaluation efficiency and cross-evaluation efficiency of each scheme are obtained, and the optimal supply scheme is found based on their cross-evaluation efficiency.
Highlights
The optimization problem of spare parts supply needs to consider many factors, such as time, risk, cost, and so on [1]
This paper solves the problem of spare parts supply network optimization and evaluation, and a multi-objective optimization model considering time, risk and cost is developed
The differential evolution strategy is introduced into the MOEA/D algorithm, and the non-dominated solutions of the model are obtained by using the proposed multi-objective evolution algorithm
Summary
The optimization problem of spare parts supply needs to consider many factors, such as time, risk, cost, and so on [1]. Wei et al studied the wartime spare parts scheduling model under the condition of insufficient resources. They took the earliest supply start time and the least transfer line as objectives, and the multi-objective model was transformed into single-objective optimization by the weighting method [2]. In order to minimize the supply costs, Qin et al established an optimization model of emergency resource allocation considering the constraints of the number of emergency resources, reserve capacity, and location. Fazli et al established a three-objective optimization model of emergency supply network in order to minimize the total supply cost and transportation time while maximizing the supply reliability [6]. Zhang et al established a multi-objective three-stage stochastic programming model with the objectives of minimum lead time
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