众包物流配送车辆调度模型及优化

doi:10.12068/j.issn.1005-3026.2021.08.021

东北大学学报（自然科学版） ›› 2021, Vol. 42 ›› Issue (8): 1210-1216.DOI: 10.12068/j.issn.1005-3026.2021.08.021

• 管理科学 • 上一篇

众包物流配送车辆调度模型及优化

杜子超¹，卢福强²，王素欣^1,3，王雷震^1,3

(1. 东北大学信息科学与工程学院，辽宁沈阳110819; 2. 燕山大学经济管理学院，河北秦皇岛066004; 3. 东北大学秦皇岛分校控制工程学院，河北秦皇岛066004)

修回日期:2020-12-23 接受日期:2020-12-23 发布日期:2021-09-02
通讯作者: 杜子超
作者简介:杜子超(1996-)，男，内蒙古呼和浩特人，东北大学硕士研究生；王雷震(1965-)，男，河北行唐人，东北大学教授.
基金资助:
国家重点研发计划项目(2020YFB1712802)；国家自然科学基金资助项目(71401027)；河北省自然科学基金资助项目(G2016501086)；河北省高等学校人文社会科学研究项目(SQ202002).

Vehicle Scheduling Model and Optimization of Crowdsourcing Logistics Distribution

DU Zi-chao¹， LU Fu-qiang²， WANG Su-xin^1,3， WANG Lei-zhen^1,3

1. School of Information Science & Engineering， Northeastern University， Shenyang 110819， China; 2. School of Economics and Management， Yanshan University， Qinhuangdao 066004， China； 3. School of Control Engineering， Northeastern University at Qinhuangdao， Qinhuangdao 066004， China.

Revised:2020-12-23 Accepted:2020-12-23 Published:2021-09-02
Contact: LU Fu-qiang
About author:-
Supported by:
-

摘要/Abstract

摘要： 针对众包抢单模式和众包派单模式的配送特点，建立众包配送车辆调度模型，将两种配送模式有机结合，优势互补，并根据模型特点采用蚁群-量子粒子群混合优化算法进行求解.以深圳清湖冷链配送为例，从配送距离和成本等角度，分别与传统配送模式、抢单配送模型和派单配送模型进行比较，实验充分证明了众包配送模型的有效性;同时，将蚁群-量子粒子群混合算法与蚁群、粒子群等算法优化结果进行比较，证明了蚁群-量子粒子群混合算法的有效性.

关键词: 众包车辆调度优化;抢单模式;派单模式;蚁群算法;量子粒子群算法

Abstract: Considering the distribution characteristics of crowdsourcing order grabbing/dispatching modes， a crowdsourcing distribution vehicle scheduling model is established， in which two distribution modes are combined， complementing each other. According to the characteristics of the model， a hybrid algorithm of ant colony coupled with quantum particle swarm optimization is proposed to solve the model. Taking Qinghu cold chain distribution in Shenzhen as an example， the crowdsourcing distribution model is compared with the traditional model and order grabbing/dispatching models from the perspective of distribution distance and cost. The experiment fully proves the effectiveness of the crowdsourcing distribution model. The optimization results of hybrid algorithm proposed is compared with that of the ant colony algorithm and particle swarm optimization algorithm， thus verifying the effectiveness of the proposed algorithm.

Key words: optimization of crowdsourcing vehicle scheduling; order grabbing mode; order dispatching mode; ant colony algorithm; quantum particle swarm algorithm

中图分类号:

N945.1

杜子超，卢福强，王素欣，王雷震. 众包物流配送车辆调度模型及优化[J]. 东北大学学报（自然科学版）, 2021, 42(8): 1210-1216.

DU Zi-chao， LU Fu-qiang， WANG Su-xin， WANG Lei-zhen. Vehicle Scheduling Model and Optimization of Crowdsourcing Logistics Distribution[J]. Journal of Northeastern University(Natural Science), 2021, 42(8): 1210-1216.

参考文献

[1]Wang Y，Zhang D X，Liu Q，et al.Towards enhancing the last-mile delivery:an effective crowd-tasking model with scalable solutions［J］.Transportation Research Part E:Logistics and Transportation Review，2016，93:279-293.
[2]Dupljanin D，Mirkovic M，Dumnic S，et al.Urban crowdsourced last mile delivery:mode of transport effects on fleet performance［J］.International Journal of Simulation Modelling，2019，18(3):441-452.
[3]Akeb H，Moncef B，Durand B.Building a collaborative solution in dense urban city settings to enhance parcel delivery:an effective crowd model in Paris［J］.Transportation Research Part E:Logistics and Transportation Review，2018，119:223-233.
[4]王文杰，陈颖，蒋帅杰.考虑平台竞争的众包物流社会配送服务最优定价策略［J］.运筹与管理，2020，29(10):11-20.(Wang Wen-jie，Chen Ying，Jiang Shuai-jie.Optimal design of a service system with free experience service under a dedicated service discipline［J］.Operations Research and Management Science，2020，29(10):11-20.)
[5]刘悦秋，李军，潘旭.基于大众参与度的众包物流定价策略研究［J］.西南交通大学学报(社会科学版)，2019，20(1):107-115.(Liu Yue-qiu，Li Jun，Pan Xu.Research on pricing strategy of crowdsourcing distribution based on public participation［J］.Journal of Southwest Jiaotong University(Social Sciences)，2019，20(1):107-115.)
[6]Castillo V E，Bell J E，Rose W J，et al.Crowdsourcing last mile delivery:strategic implications and future research directions［J］.Journal of Business Logistics，2018，39(1):7-25.
[7]Li L X，Wang X，Rezaei J.A Bayesian best-worst method-based multicriteria competence analysis of crowdsourcing delivery personnel［J］.Complexity，2020(6):1-17.
[8]Devari A，Nikolaev A G，He Q.Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers［J］.Transportation Research Part E:Logistics and Transportation Review，2017，105:105-122.
[9]Sampaio A，Savelsbergh M，Veelenturf L P，et al.Delivery systems with crowd-sourced drivers:a pickup and delivery problem with transfers［J］.Networks，2020，76(2):1-24.
[10]Rechavi A，Toch E.Crowd logistics:understanding auction-based pricing and couriers’ strategies in crowdsourcing package delivery［J/OL］.Journal of Intelligent Transportation Systems，2020 ［2020-11-29］.https://doi.org/10.1080/15472450.2020.1797503.
[11]Kafle N，Zou B，Lin J.Design and modeling of a crowdsource-enabled system for urban parcel relay and delivery［J］.Transportation Research Part B:Methodological，2017，99:62-82.
[12]Alnaggar A，Gzara F，Bookbinder J H.Crowdsourced delivery:a review of platforms and academic literature［J］.Omega，2021，98:102139.
[13]Meng S，Kang J S，Chi K，et al.Gearbox fault diagnosis through quantum particle swarm optimization algorithm and kernel extreme learning machine［J］.Journal of Vibroengineering，2020，22(6):1399-1414.
[14]Zhang Z J，Wang W L，Xia R F，et al.Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm［J/OL］.BMC Bioinformatics，2020 ［2020-11-18］.https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03833-7.
[15]Su X Y，Wei S W.Sliding mode control design for active suspension systems using quantum particle swarm optimisation［J/OL］.International Journal of Vehicle Design，2020，81(1/2)［2020-11-18］.http://dx.doi.org/10.1504/IJVD.2019.10033091.
[16]Zhao X G，Liang J，Meng J，et al.An improved quantum particle swarm optimization algorithm for environmental economic dispatch［J］.Expert Systems with Applications，2020，152:113370.
[17]Gan W Y，Zhu D Q，Ji D X.QPSO-model predictive control-based approach to dynamic trajectory tracking control for unmanned underwater vehicles［J］.Ocean Engineering，2018，158:208-220.
[18]Liu R C，Li J X，Fan J.A dynamic multiple populations particle swarm optimization algorithm based on decomposition and prediction［J］.Applied Soft Computing，2018，73:434-459.
[19]Zhang D G，Wang J X，Fan H R，et al.New method of traffic flow forecasting based on quantum particle swarm optimization strategy for intelligent transportation system［J］.International Journal of Communication Systems，2020，34(1):1-20.

众包物流配送车辆调度模型及优化

Vehicle Scheduling Model and Optimization of Crowdsourcing Logistics Distribution

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