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中国管理科学 ›› 2021, Vol. 29 ›› Issue (5): 157-165.doi: 10.16381/j.cnki.issn1003-207x.2018.1472

• 论文 • 上一篇    下一篇

多油品供给受限下多油库被动配送车辆路径问题研究

徐小峰, 林姿汝, 周鹏   

  1. 中国石油大学(华东)经济管理学院, 山东 青岛 266580
  • 收稿日期:2018-10-18 修回日期:2018-12-28 出版日期:2021-05-20 发布日期:2021-05-26
  • 通讯作者: 徐小峰(1982-),男(汉族),山东莱阳人,中国石油大学(华东)经济管理学院,教授,博士生导师,管理学博士,研究方向:运筹优化、物流与供应链管理,E-mail:xuxiaofeng@upc.edu.cn. E-mail:xuxiaofeng@upc.edu.cn.
  • 基金资助:
    国家自然科学基金资助项目(71871222);中央高校基本科研业务费专项资金资助项目(17CX04023B);中国石油大学(华东)港澳台合作交流基金资助项目(UPCGAT2018002)

Research on Vehicle Routing Problem of Multiple Oil Depot Passive Distribution under Multi Oil Supply Constraints

XU Xiao-feng, LIN Zi-ru, ZHOU Peng   

  1. School of Economics and Management, China University of Petroleum, Qingdao 266580, China
  • Received:2018-10-18 Revised:2018-12-28 Online:2021-05-20 Published:2021-05-26

摘要: 成品油供给不足将导致加油站油品订单无法完全满足,如何安排有限油品的合理配送对保障能源供给安全至关重要。为此,本文考虑有限供给下不同客户配送的优先次序,开展配送计划、车辆调度和路径优化等油品配送网络规划活动,对多油品供给受限情况下多油库被动配送车辆路径问题(Multiple Depot Vehicle Routing Problem,MDVRP)进行深入研究。首先,文章构建了考虑需求优先等级和配送成本的多油品多油库车辆路径规划多目标优化模型。其次,采用多目标粒子群优化算法(Multi-Objective Particle Swarm Optimization,MOPSO)对模型进行求解,以实现车辆高效调度和油品配送路径优化。最后,基于CNPC在青岛市部分油库和加油站点的数据信息,构建油品配送网络进行实证检验。算例结果显示,配送车辆路径经过优化后,生成Pareto非劣解集,配送成本显著降低,配送满足率明显提高,这也进一步验证了该模型及相关算法的可行性和有效性。

关键词: MDVRP, 资源受限, 成品油配送, 多目标优化, MOPSO

Abstract: The insufficient supply of refined oil will cause that the oil gas stationcan't fully meet the orders. How to arrange the reasonable distribution of limited oil is essential to ensure the safety of energy supply. To this end, the distribution priority of different customers under limited supply is considered, distribution planning, vehicle scheduling, path optimization and other oil distribution network planning activities are carried out, in-depth study on the Multiple Depot Vehicle Routing Problem (MDVRP) with multi-oil supply constraints is conducted. Firstly, a multi-objective optimization model of vehicle routing for multi-oil products and multi-oil depots is constructed, which considers the priority of demand and the cost of distribution. Secondly, the Multi-Objective Particle Swarm Optimization (MOPSO) is used to solve the model to achieve efficient vehicle scheduling and oil distribution routing optimization. Finally, based on the data information of CNPC in some oil depots and fueling stations in Qingdao, an oil distribution network is constructed for empirical testing. The results of the example show that the Pareto optimal set is generated after the optimization of vehicle routing, the distribution cost is significantly reduced, and the delivery satisfaction rate is significantly improved, which further verifies the feasibility and effectiveness of the model and related algorithms.The model and algorithm can be further extended to various supply and demand situations, which is helpful to solve the distribution problem of refined oil products with different priority of gas stations.

Key words: MDVRP, resource constrained, refined oil distribution, multi-objective optimization, MOPSO

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