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;2.国防高科技大学系统工程学院;3.湖南师范大学商学院;4.国防科学技术大学系统工程学院

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通讯作者:

中图分类号:

TP273

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


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

School of Logistics and Transportation, Central South University of Forestry and Technology

Fund Project:

This work was supported by the National Natural Science Foundation, China (No. 61773120, No.71771028)。

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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。本文将道路拥堵状况反映在车辆在路段上的行驶速度上,在考虑客户满意度的基础上,构建了最小化总成本的冷链车辆路径优化数学模型。为了求解该问题,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,设计了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、禁忌搜索改进的蚁群算法和本文所提出来的知识型蚁群算法进行了对比分析,验证了本文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. In this paper, the road congestion is reflected in the speed of vehicles on the road, and the mathematical model of cold chain vehicle routing optimization is built to minimize the total cost on the basis of customer satisfaction. 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, tabu search improved ant colony algorithm and the knowledge-based ant colony algorithm proposed in this paper are compared and analyzed, and the effectiveness of the model and knowledge-based ant colony algorithm proposed in this paper is verified.

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历史
  • 收稿日期:2021-01-26
  • 最后修改日期:2021-05-18
  • 录用日期:2021-06-03
  • 在线发布日期: 2021-07-01
  • 出版日期: