引用本文:葛显龙,薛桂琴.基于场景动态度的两级配送路径问题[J].控制与决策,2019,34(6):1195-1202
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基于场景动态度的两级配送路径问题
葛显龙1,2,薛桂琴1
(1. 重庆交通大学经济与管理学院,重庆400074;2. 重庆交通大学智能物流网络重庆市重点实验室,重庆400074)
摘要:
针对城市配送过程中出现的交通限行和需求不确定性等问题,将配送周期划分为初始配送阶段和动态补货阶段,路径中包含枢纽型物流中心、配送型物流中心和客户,研究其共同构成的两级车辆配送路径优化问题.考虑到问题的动态性,提出前摄性需求配额策略及响应性补货策略,构建基于场景动态度的两级动态车辆路径问题数学模型.设计融合扫描算子的禁忌搜索算法,完成车辆初始阶段的配送路径优化;根据场景动态度,设计修复/更新性动态客户的响应策略,快速响应动态需求.最后,通过仿真算例验证模型和算法的有效性,实验结果表明,所提出的设计策略能够有效降低动态客户对低动态度应用场景初始路径的干扰,并简化高动态度场景下的路径优化复杂度.
关键词:  动态车辆路径问题  前摄性调度  禁忌搜索算法  交通限行  需求不确定性  动态度
DOI:10.13195/j.kzyjc.2017.1532
分类号:TP273
基金项目:国家自然科学基金项目(71502021, 71602015);教育部人文社会科学基金项目(14YJC630038, 15XJC6300 07);博士后科学基金特别项目(2016T90862);重庆市基础与前沿研究项目(cstc2016jcyjA0160);重庆市教委人文社会科学研究项目(17SKG073);重庆市科学技术研究项目(KJ1500702).
Two-echelon distribution routing problem based on scenes dynamic degree
GE Xian-long1,2,XUE Gui-qin1
(1. School of Economics and Management, Chongqing Jiaotong University,Chongqing400074,China)
Abstract:
Aiming at the problems such as traffic limitation and demand uncertainty in the process of city distribution, this paper divides the distribution process into initial stage and dynamic replenishment stage, and studies the optimization of the two-echelon dynamic vehicle routing of the hub logistics center, the distribution logistics center and customers. Taking into account the dynamic events, this paper proposes a proactive demand quota strategy and a responsive replenishment strategy, and establishes the mathematical model of the two-echelon dynamic vehicle routing problem in dynamic circumstance. A tabu search algorithm based on a fusion scan operator is designed to optimize the delivery route in the initial stage. Meanwhile, according to the scene dynamic degree, this paper raises a repair/update dynamic customer response strategy to respond quickly to the dynamic demand. Finally, a simulation example is given to illustrate, the effectiveness of the proposed model and algorithm. The experimental results demonstrate that the proposed scheduling strategy can effectively reduce the dynamic client interference on the initial path of low-dynamism scenario and simplify the path optimization complexity of high-dynamism scenarios.
Key words:  dynamic vehicle routing problem  proactive scheduling  tabu search algorithm  traffic restriction  demand uncertainty  dynamic degree

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