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.
Hu Q, Boylan JE, Chen H, et al. OR in spare parts management: A review. European Journal of Operational Research. 2018;266(2): 395-414.
Wei G, Yang Y. Dispatching model of continuous consumption resources in wartime under insufficient supply. Systems Engineering and Electronics. 2012;34(1): 102-106.
Liu X, Zhu Y-G, Wang W-P. Optimizing wartime multi-phase spares support. Systems Engineering and Electronics. 2006;44(8): 238-241.
Zhang S, Guo J, Zhong F, et al. Multi-objective Material Provision Mission Planning under Battlefield Fuzzy Environment. Mathematics in Practice and Theory. 2015;45(13): 90-95.
Qin J, Ye Y, Shen C, et al. Optimization method for emergency resource layout for transportation network considering service reliability. Journal of Railway Science and Engineering. 2018;15(2): 506-514.
Fazli Khalaf M, Khalilpourazari S, Mohammadi M. Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design. Annals of Operations Research. 2019;283: 1079-1109. Available from: doi:10.1007/s10479-017-2729-3
Zhang J, Liu H, Yu G, et al. A three-stage and multi-objective stochastic programming model to improve the sustainable rescue ability by considering secondary disasters in emergency logistics. Computers & Industrial Engineering. 2019;135: 1145-1154. Available from: doi:10.1016/j.cie.2019.02.003
Mohammadi R, Ghomi SMTF, Jolai F. Prepositioning emergency earthquake response supplies: A new multi-objective particle swarm optimization algorithm. Applied Mathematical Modelling. 2016;40(9-10): 5183-5199.
Su Z, Zhang G, Liu Y, et al. Multiple emergency resource allocation for concurrent incidents in natural disasters. International Journal of Disaster Risk Reduction. 2016;17: 199-212.
Mardle S, Miettinen KM. Nonlinear Multiobjective Optimization. Journal of the Operational Research Society. 2000;51(2): 246.
Zhang Q, Li H. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation. 2008;11(6): 712-731.
Messac A, Ismail-Yahaya A, Mattson CA. The normalized normal constraint method for generating the Pareto frontier. Structural and Multidisciplinary Optimization. 2003;25(2): 86-98.
Zhang L, Bi X, Wang Y. The ε Constrained Multi-objective Decomposition Optimization Algorithm Based on Re-matching Strategy. Acta Electronica Sinica. 2018;423(05): 11-19.
Panda A, Pani S. A Symbiotic Organisms Search algorithm with adaptive penalty function to solve multi-objective constrained optimization problems. Applied Soft Computing. 2016;46: 344-360.
Coello CAC. Constraint-Handling Techniques used with Evolutionary Algorithms. Conference Companion on Genetic & Evolutionary Computation Conference: Late Breaking Papers, ACM, Philadelphia; 2012. p. 2445-2466.
Khezrimotlagh D, Zhu J, Cook WD, et al. Data Envelopment Analysis and Big Data. European Journal of Operational Research. 2019;274: 1047-1054. Available from: doi:10.1016/j.ejor.2018.10.044
Chen J. Research on the improvement of the Model and Evaluation method of Super-efficient data Envelopment Analysis. Zhejiang University, China; 2011.
Doyle J, Green R. Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses. Journal of the Operational Research Society. 1994;45(5): 567-578.
Liang L, Wu J, Cook WD, et al. Alternative secondary goals in DEA cross-efficiency evaluation. International Journal of Production Economics. 2008;113(2): 1025-1030.
Wang YM, Chin KS. Some alternative models for DEA cross-efficiency evaluation. International Journal of Production Economics. 2010;128(1): 332-338.
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