一种“效率-公平-运力”多维权衡的需求可拆分配送方法

doi:10.16097/j.cnki.1009-6744.2022.05.029

交通运输系统工程与信息 ›› 2022, Vol. 22 ›› Issue (5): 285-292.DOI: 10.16097/j.cnki.1009-6744.2022.05.029

一种“效率-公平-运力”多维权衡的需求可拆分配送方法

王建伟¹ ，刘旭旭¹ ，付鑫^{* 1} ，杨洋^{2a, 2b} ，崔梦妍¹

1. 长安大学，运输工程学院，西安 710064；2. 北京航空航天大学，a. 交通科学与工程学院， b. 车路协同与安全控制北京市重点实验室，北京 100191

收稿日期:2022-06-06 修回日期:2022-06-30 接受日期:2022-07-11 出版日期:2022-10-25 发布日期:2022-10-22
作者简介:王建伟(1965- )，男，黑龙江哈尔滨人，教授，博士。
基金资助:
国家重点研发计划

A Multi-dimensional "Efficiency-Fairness-Capacity" Balancing Method for Demand Splitting Distribution

WANG Jian-wei¹ , LIU Xu-xu¹ , FU Xin^{* 1}, YANG Yang^{2a, 2b} , CUI Meng-yan¹

1. School of Transportation Engineering, Chang'an University, Xi'an 710064, China; 2a. School of Transportation Science and Engineering; 2b. Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beihang University, Beijing 100191, China

Received:2022-06-06 Revised:2022-06-30 Accepted:2022-07-11 Online:2022-10-25 Published:2022-10-22
Supported by:
National Key Research and Development Program of China(2020YFC1512000)

摘要/Abstract

摘要： 针对自然灾害及重大社会公共事件等各类突发事件的配送问题，本文以公路运输为研究场景，将配送时间最短、加权时间攀比值最小和使用车辆数最少为多维目标，在引入需求可拆分这一限制条件的基础上，构建“效率-公平-运力”多维权衡的需求可拆分应急物资配送模型。针对该问题设计改进的蚁群算法求解模型。从选择拆分点、信息素更新和引入变邻域搜索算子这3个方面改进了算法，并实现当解持续不变时，初始化信息素，以增加随机性。结果表明，与传统求解算法相比，改进算法的稳定性更高(平均偏差率降低7.00%)，寻优性更好(优化率提高7.41%)。通过分析考虑三目标、双目标和决策者具有明显偏好的多重场景下的求解结果得知：效率、公平、运力这3个子目标相互悖反，增加运力投入可以显著提高配送方案的效率与公平；当运力不变时，效率与公平之间近似呈同比例反比关系。研究结论可为救灾目标不确定条件下多因素考量的应急物资配送决策生成与优化问题提供方法改进与可量化决策支撑。

关键词: 公路运输, 应急物资配送, 蚁群算法, 需求可拆分, 效率-公平-运力兼顾型决策

Abstract: This paper focuses on the emergency material distribution in special cases such as in natural disasters and major social and public events. A multi-dimensional method of "efficiency-fairness-capacity" is proposed for the distribution of emergency materials using highway transportation based on the restriction that demand can be split, and the objectives of the shortest distribution time, the smallest weighted time climbing value and the least number of distribution vehicles. An improved Ant Colony algorithm is designed to solve the model. The algorithm is improved in three aspects: choosing splitting points, pheromone updating and introducing variable neighborhood search operators, and implementing the initialization of pheromones when the solution is continuously constant to increase the randomness. The results show that the improved algorithm has higher stability (7.00% lower average deviation rate) and better optimality finding (7.41% higher optimization rate) compared to the traditional algorithm. From the results under multiple scenarios considering tri-objective, bi-objective, and decision makers with obvious preferences, it was found that the three sub-objectives of efficiency, equity, and capacity are paradoxical to each other. Increasing the distribution capacity can significantly improve the efficiency and equity. When the capacity is constant, the efficiency and equity are negatively correlated in the same proportion. The study results provide methodological references and support for the emergency material distribution with multi-factors and uncertain disaster relief objectives.

Key words: highway transportation, emergency material distribution, ant colony algorithm, demand splitting; efficiency-equity-capacity balanced decision making

中图分类号:

U491

王建伟, 刘旭旭, 付鑫, 杨洋, 崔梦妍. 一种“效率-公平-运力”多维权衡的需求可拆分配送方法[J]. 交通运输系统工程与信息, 2022, 22(5): 285-292.

WANG Jian-wei, LIU Xu-xu, FU Xin, YANG Yang, CUI Meng-yan. A Multi-dimensional "Efficiency-Fairness-Capacity" Balancing Method for Demand Splitting Distribution[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 285-292.

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参考文献

[1] 杨枫, 种大双. 应急车辆动态路径选择的两阶段优化模型[J]. 交通运输系统工程与信息, 2022, 22(3): 84- 92. [YANG F, ZHONG D S. Emergency vehicle dynamic path selection based on two-stage objective optimization model[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 84-92.]

[2] 段晓红, 吴家新, 周芷晴. 基于层次蝙蝠算法的应急车辆调度与交通疏散协同决策[J]. 交通运输系统工程与信息, 2020, 20(2): 157-165. [DUAN X H, WU J X, ZHOU Z Q, et al. Collaborative decision-making of emergency vehicle scheduling and traffic evacuation based on bi-level bat algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(2): 157-165.]

[3] COOK R A, LODREE E J. Dispatching policies for lastmile distribution with stochastic supply and demand[J]. Transportation Research Part E: Logistics & Transportation Review, 2017, 106: 353-371.

[4] 曲冲冲, 王晶, 黄钧, 等. 考虑时效与公平性的震后应急物资动态配送优化研究[J]. 中国管理科学, 2018, 26 (6): 178-187. [QU C C, WANG J, HUANG J, et al. Research on dynamic distribution optimization of postearthquake emergency materials considering timeliness and fairness[J]. Chinese Journal of Management Science, 2018, 26(6): 178-187.]

[5] JIANG Y P, YUAN Y F Emergency logistics in a largescale disaster context: Achievements and challenges[J]. International Journal of Environmental Research & Public Health, 2019, 16(5): 779.

[6] 吴坷, 宋英华, 吕伟. 医疗应急物资车辆配送优化调度时间窗模型研究[J]. 中国安全生产科学技术, 2022, 18 (1): 11-16. [WU K, SONG Y H, LV W, et al. Research on time window model for optimal scheduling on vehicle distribution of medical emergency materials[J]. Journal of Safety Science and Technology, 2022, 18(1): 11-16.]

[7] 揭婉晨, 侍颖, 杨珺, 等. 需求可拆分电动汽车车辆路径问题及其改进分支定价算法研究[J]. 管理学报, 2020, 17(12): 1873-1880. [JIE W C, SI Y, YANG J, et al. Split delivery routing problem of electric vehicles its modified branch- and- price algorithm[J]. Chinese Journal of Management, 2020, 17(12): 1873-1880.]

[8] ZHANG L, LU J, YANG Z. Dynamic optimization ofemergency resource scheduling in a large-scale maritime oil spill accident[J]. Computers & Industrial Engineering, 2020, 152(1): 107028.

[9] BORTFELDT A, YI J. The split delivery vehicle routing problem with three-dimensional loading constraints[J]. European Journal of Operational Research, 2020, 282(2): 545-558.

[10] 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. [REN T, LUO T Y, LI S X, et al. Knowledge based ant colony algorithm for cold chain logistics distribution path optimization[J]. Control and Decision, 2022, 37(3): 545-554.]

[11] 范厚明, 刘鹏程, 吴嘉鑫, 等. 集货需求随机的同时配集货VRP及混合变邻域搜索算法[J]. 系统工程理论与实践, 2019, 39(10): 2646- 2659. [FAN H M, LIU P C, WU J X, et al. Hybrid genetic algorithm with variable neighborhood descent for the vehicle routing problem with simultaneous stochastic pickup and deterministic delivery[J]. Systems Engineering-Theory & Practice, 2019, 39(10): 2646-2659.]

[12] 朱莉, 曹杰, 顾珺, 等. 公平缓解灾民创伤下的应急物资动态调配研究[J]. 系统工程理论与实践, 2020, 40 (9): 2427-2437. [ZHU L, CAO J, GU J, et al. Dynamic emergency supply distribution considering faire mitigation of victim suffering[J]. Systems EngineeringTheory & Practice, 2020, 40(9): 2427-2437.]

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一种“效率-公平-运力”多维权衡的需求可拆分配送方法

A Multi-dimensional "Efficiency-Fairness-Capacity" Balancing Method for Demand Splitting Distribution

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