系统管理学报

系统管理学报 ›› 2023, Vol. 32 ›› Issue (2): 215-232.DOI: 10.3969/j.issn.1005-2542.2023.02.001

• 决策科学与运营管理 •    下一篇

多中心共同配送开闭混合式的车辆路径优化问题

王勇，罗思妤，周雪，刘永，许茂增   

  1. 重庆交通大学经济与管理学院，重庆 400074
  • 出版日期:2023-03-28 发布日期:2023-03-24
  • 作者简介:王勇（1983-），男，博士，教授。研究方向为智能运输与物流配送。
  • 基金资助:

    国家自然科学基金资助项目（71871035）；重庆市教委科学技术重点项目（KJZD-K202000702）；重庆市自然科学基金面上项目（CSTB2022NSCQ-MSX0535）；重庆市研究生导师团队建设项目（JDDSTD2019008）；重庆市留学创新项目（cx2021038）；巴渝学者青年项目（YS2021058）

Open-Closed Hybrid Vehicle Routing Optimization of Multi-Center Joint Distribution

WANG Yong，LUO Siyu，ZHOU Xue，LIU Yong，XU Maozeng   

  1. School of Economics and Management，Chongqing Jiaotong University，Chongqing 400074，China
  • Online:2023-03-28 Published:2023-03-24

PDF

摘要：

针对多中心共同配送开闭混合式车辆路径优化研究在资源集成共享和合作收益分配机制设计结合方面存在的不足，提出研究多中心共同配送开闭混合式的车辆路径优化问题。首先，构建了包含运输成本、惩罚成本、租赁成本和配送成本等物流运营总成本最小的优化模型。其次，根据模型特征设计了考虑客户点地理位置和时间窗约束的三维K-means聚类算法，进而提出了遗传-粒子群混合优化算法求解模型。该混合算法设计了遗传算法和粒子群算法间的选择性赋予机制，提高了种群的多样性和获取优化解的收敛性，并增强了混合算法的局部和全局搜索能力。再次，应用成本差值分配方法进行多中心共同配送的收益分配优化研究，进而应用严格单调路径原则研究了联盟合作序列选择问题，并进行了多中心共同配送的联盟稳定性检验研究。最后，通过算法比较分析和实例数据对所提模型和算法进行了验证研究，并比较分析了不同配送模式下多中心共同配送优化方案各指标的差异，进而验证了所提方法的有效性和适用性。研究成果可为多级多中心共同配送的网络优化问题研究提供方法参考和决策支持。

关键词:

多中心共同配送, 开闭混合式车辆路径, 智能算法, 合作联盟, 资源集成共享

Abstract:

In order to overcome the shortcomings of the resource integration and sharing and the design of a cooperative profit distribution mechanism in the open-closed hybrid vehicle routing optimization of multi-center joint distribution, an open-closed hybrid vehicle routing problem of multi-center joint distribution is proposed. First, an optimization model is proposed to minimize the total logistics operating cost, including transportation cost, penalty cost, vehicle rental cost, and distribution cost. Then, according to the characteristics of the model, a three-dimensional K-means clustering algorithm is designed to consider the geographical locations and time window constraints of customers, and a genetic algorithm-particle swarm hybrid optimization is proposed to solve the model. The proposed algorithm designs a selective granting mechanism between the genetic algorithm and particle swarm algorithm to improve the diversity of the population and the convergence of the obtained optimal solutions, and enhance the local and global search capabilities. Next, a cost gap allocation method is employed to study the profit allocation optimization of the multi-center joint distribution. Afterwards, the strict monotonic path principle is used to study the selection of alliance cooperation sequences, and then the stability of multi-center joint distribution alliance is discussed. Finally, the proposed model and algorithm are validated via the comparison and analysis of algorithms and a real-world case study, the differences of the indicators in the multi-center joint distribution optimization schemes with different distribution modes are compared and analyzed, and the effectiveness and applicability of the proposed method are further verified. This paper can provide a method reference and decision support for the study of the multi-echelon multi-center joint distribution network optimization problem.

Key words:

multi-center joint distribution, open-closed hybrid vehicle routing, intelligent algorithm, cooperative alliance, resource integration and sharing

中图分类号: