面向冷链物流配送路径优化的知识型蚁群算法
作者:
作者单位:

1. 中南林业科技大学 物流与交通学院,长沙 410004;2. 国防科学技术大学 系统工程学院,长沙 410000;3. 湖南师范大学 商学院,长沙 410081

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E-mail: xinglining@gmail.com.

中图分类号:

TP273

基金项目:

国家自然科学基金项目(61773120);智慧物流技术湖南省重点实验室项目(2019TP1015);北京市智能物流系统协同创新中心开放项目(BILSCIC-2019KF-25).


Knowledge based ant colony algorithm for cold chain logistics distribution path optimization
Author:
Affiliation:

1. School of Logistics and Transportation,Central South University of Forestry and Technology,Changsha 410004,China;2. School of Systems Engineering,National Defense University of Science and Technology,Changsha 410000,China;3. School of Business,Hunan Normal University,Changsha 410081, China

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    摘要:

    生鲜电商、冷链宅配的盛行使冷链物流订单呈现出“小批量、多批次、易腐坏”的特点,进一步增大了城市冷链物流配送路径优化的必要性与难度.鉴于此,同时考虑顾客满意度和道路拥堵状况,构建最小化总成本的冷链车辆路径优化数学模型.为求解该问题,将知识型精英策略下的禁忌搜索算子和动态概率选择的知识模型融入蚁群算法,设计一种新的知识型蚁群算法.通过对模拟实例和真实实例进行仿真实验,对传统蚁群算法、基于禁忌搜索改进的蚁群算法与所提出的知识型蚁群算法进行对比分析,验证了所构模型和知识型蚁群算法的有效性.

    Abstract:

    The popularity of fresh e-commerce and cold chain home delivery makes the cold chain logistics orders present the characteristics of "small batch, multi batch, perishable", which further increases the necessity and difficulty of urban cold chain logistics distribution path optimization. Considering both customer satisfaction and road congestion, this paper constructs a mathematical model of cold chain vehicle routing optimization to minimize the total cost. In order to solve this problem, a new knowledge-based ant colony algorithm is designed by integrating the tabu search operator under the knowledge-based elitist strategy and the knowledge model of dynamic probability selection into the ant colony algorithm. Through the simulation experiments of simulation examples and real examples, the traditional ant colony algorithm, the improved ant colony algorithm based on tabu search and the knowledge based ant colony algorithm proposed in this paper are compared and analyzed, and the effectiveness of the proposed model and knowledge-based ant colony algorithm is verified.

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任腾,罗天羽,李姝萱,等.面向冷链物流配送路径优化的知识型蚁群算法[J].控制与决策,2022,37(3):545-554

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  • 在线发布日期: 2022-01-25
  • 出版日期: 2022-03-20