Dynamic multi-objective optimization model and algorithm for logistics network

被引：0
|
作者：
Wang Y. [1 ]
Shi Q. [1 ]
Song W. [1 ]
Hu Q. [1 ]
机构：
[1] Department of Equipment Command and Management, Army Engineering University of PLA (Shijiazhuang), Shijiazhuang
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2020年 / 26卷 / 04期
关键词：
Differential evolution algorithm; Dynamic optimization; Logistics engineering; Logistics network; Multi-objective optimization; Self-adaptive;
D O I：
10.13196/j.cims.2020.04.027
中图分类号：
学科分类号：
摘要：
To solve the problem of dynamic logistics network optimization, a three-echelon multi-period logistics network model was constructed. In order to ensure the benefit and efficiency of the logistics network simultaneously, a multi-objective optimization model was established with the objectives of minimizing the total cost and the shortest supply time. A Dynamic Self-adaptive Multi-objective Differential Evolution Algorithm(DSMODEA)was proposed to solve the model, which was a meta-heuristic intelligent optimization algorithm, and selected the optimal solution by comparing the Pareto dominance relation as well as the crowding distance of individuals. The differential evolution strategy was used in the iterative process. At the same time, the proposed environmental change detection operator, environmental change response strategy and adaptive strategy ensured that the algorithm could solve the dynamic optimization problem very well. Numerical examples showed that the DSMODEA could obtain the optimal feasible solutions for each period of the logistics network, and the response strategy and adaptive strategy improved the performance of the algorithm greatly. © 2020, Editorial Department of CIMS. All right reserved.
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页码：1142 / 1150
页数：8
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