计算机集成制造系统 ›› 2020, Vol. 26 ›› Issue (第4): 1142-1150.DOI: 10.13196/j.cims.2020.04.027

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动态物流网络多目标优化模型及求解算法

王亚东,石全,宋卫星,胡起伟   

  1. 陆军工程大学石家庄校区装备指挥与管理系
  • 出版日期:2020-04-30 发布日期:2020-04-30

Dynamic multi-objective optimization model and algorithm for logistics network

  • Online:2020-04-30 Published:2020-04-30

摘要: 针对动态物流网络优化问题,构建了多阶段三级物流网络模型。为了同时保证物流网络的效益和效率,以产品供应总成本最小和供应总时间最短为目标建立多目标优化模型。针对该模型多目标、多约束且存在时变参数的特点,提出了动态自适应多目标差分进化算法(DSMODEA)对模型进行求解。DSMODEA算法为元启发式智能优化算法,通过比较种群个体的Pareto支配关系和拥挤度距离来判断个体优劣,采用差分进化策略不断迭代收敛。同时,提出了环境变化检测算子、环境变化响应策略和自适应策略以保证算法能很好地求解动态优化问题。算例表明,DSMODEA算法能够求得各阶段物流网络的最佳可行供应方案,且所采用的响应策略和自适应飞行策略大大提高了算法的性能。

关键词: 物流工程, 物流网络, 多目标优化, 动态优化, 自适应, 差分进化算法

Abstract: 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.

Key words: logistics engineering, logistics network, multi-objective optimization, dynamic optimization, self-adaptive, differential evolution algorithm

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