计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

两级Memetic应急物资协同决策配送算法

刘捷,赵蕾   

  1. (南昌大学 信息工程学院,南昌 330031)
  • 收稿日期:2015-11-16 出版日期:2016-11-15 发布日期:2016-11-15
  • 作者简介:刘捷(1978—),男,副教授、硕士,主研方向为智能算法;赵蕾,硕士研究生。

Two-stage Memetic Algorithm for Collaborative Decision Making of Emergency Resources Distribution

LIU Jie,ZHAO Lei   

  1. (School of Information Engineering,Nanchang University,Nanchang 330031,China)
  • Received:2015-11-16 Online:2016-11-15 Published:2016-11-15

摘要: 针对应急救援中次生灾害对物资配送的影响,提出应急物资协同决策配送算法。以最小化最后完成配送任务的时间为优化目标,考虑道路损毁、泥石流等次生灾害问题,设计基于随机策略的两级应急物资优化配送模型。为解决该优化模型中存在的多极值问题,结合差分进化与Q强化学习理论,构建由一级单目标和二级多目标车辆路径优化问题组成的Memetic配送算法。实验结果表明,与Multi start和Branch cut算法相比,该算法具有更好的收敛速度和收敛精度。

关键词: 差分进化, Q强化学习, 车辆路径优化, 协同决策, 应急物资配送

Abstract: Aiming at the influence of secondary disasters on resource distribution in emergency rescue,this paper proposes an algorithm for collaborative decision making of emergency resources distribution.Firstly,it takes minimizing the time to complete the distribution tasks as the optimization objection and constructs the two-layer optimization model for emergency resource distribution based on random strategy,which considers secondary disasters including damaged roads and mudslides.Meanwhile,in order to solve the multi-extreme value problem of the optimization model,the Memetic distribution algorithm constructed by the Single Objective Vehicle Routing Problem(SVRP) and the Two-layer Multi Objective Vehicle Routing Problem(MVRP) is built based on the Differential Evolution(DE) and Q reinforcement learning theory.Experimental results show that the proposed algorithm has higher convergence speed and convergence precision than Multi start and Branch cut algorithms.

Key words: Differential Evolution(DE), Q reinforcement learning, vehicle routing optimization, collaborative decision making, emergency resource distribution

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