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众包配送研究综述

赵建有 李玥 田浩 陶旭秋 侯雪

赵建有, 李玥, 田浩, 陶旭秋, 侯雪. 众包配送研究综述[J]. 交通运输工程学报, 2023, 23(5): 62-84. doi: 10.19818/j.cnki.1671-1637.2023.05.004
引用本文: 赵建有, 李玥, 田浩, 陶旭秋, 侯雪. 众包配送研究综述[J]. 交通运输工程学报, 2023, 23(5): 62-84. doi: 10.19818/j.cnki.1671-1637.2023.05.004
ZHAO Jian-you, LI Yue, TIAN Hao, TAO Xu-qiu, HOU Xue. Review on research of crowdsourcing delivery[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 62-84. doi: 10.19818/j.cnki.1671-1637.2023.05.004
Citation: ZHAO Jian-you, LI Yue, TIAN Hao, TAO Xu-qiu, HOU Xue. Review on research of crowdsourcing delivery[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 62-84. doi: 10.19818/j.cnki.1671-1637.2023.05.004

众包配送研究综述

doi: 10.19818/j.cnki.1671-1637.2023.05.004
基金项目: 

国家重点研发计划 2020YFB1600400

国家自然科学基金项目 U1909204

国家自然科学基金项目 U19B2029

详细信息
    作者简介:

    赵建有(1963-),男,河南西峡人,长安大学教授,工学博士,从事物流工程研究

    通讯作者:

    李玥(1993-),女,河北唐山人,长安大学工学博士研究生

  • 中图分类号: U-9

Review on research of crowdsourcing delivery

Funds: 

National Key Research and Development Program of China 2020YFB1600400

National Natural Science Foundation of China U1909204

National Natural Science Foundation of China U19B2029

More Information
  • 摘要: 基于SCI数据库和CNKI数据库收录的1 495个文献,应用知识图谱分析软件VOSviewer对众包配送共词进行聚类分析,梳理了众包配送参与主体的影响因素、众包配送平台的运营和众包配送车辆路径问题,分析了国内外众包配送的现状,探讨了众包配送存在的问题,提出了众包配送未来的发展方向。研究结果表明:在众包参与主体方面,价格、安全和配送难度等是影响众包配送模式普及的重要因素;在平台运营方面,现有众包配送运营平台以成本最小或路径最短为目标,构建单一的任务匹配函数;在车辆路径问题方面,现有众包配送多依托已有数据库采用启发式算法求解车辆路径问题。未来众包配送研究的发展趋势主要包括:对众包参与主体影响因素进行研究,为吸引不同特征的参与主体,适应区域客户密度和经济发展水平差异,应合理调整配送价格,进一步细化场景;为提升众包配送平台服务水平,提高众包配送平台竞争力,应将安全、资源、环境与交通等因素纳入配送平台中,构建多目标任务匹配函数;为适应众包配送特性,提高众包配送系统响应速度,应构建具有优先级的多目标路径优化函数;应利用人工智能算法等工具,解决众包配送路径优化问题。

     

  • 图  1  众包配送研究发展趋势

    Figure  1.  Development trend of crowdsourcing delivery research

    图  2  众包配送研究热点

    Figure  2.  Research hotspots of crowdsourcing delivery

    图  3  客户选择众包配送的影响因素

    Figure  3.  Influencing factors of customers choosing crowdsourcing delivery

    图  4  配送员参与配送意愿的影响因素

    Figure  4.  Influencing factors of delivers' willingness to participate in delivery

    图  5  现有众包配送平台定价策略

    Figure  5.  Pricing strategies of existing crowdsourcing delivery platforms

    图  6  匹配模式关系

    Figure  6.  Relationship of matching patterns

    图  7  任务匹配目标频数

    Figure  7.  Task matching objective frequencies

    图  8  车辆路径问题研究层次

    Figure  8.  Research levels of vehicle routing problems

    图  9  车辆路径问题求解算法

    Figure  9.  Algorithms for solving vehicle routing problems

    图  10  众包配送发展阶段

    Figure  10.  Development stages of crowdsourcing delivery

    表  1  现有众包配送平台定价策略

    Table  1.   Pricing strategies of existing crowdsourcing delivery platforms

    定价方案 平台 定价规则 额外报酬 运营时间
    时间定价 Amazon Flex 每小时$18~25 2015年至今
    Shipt 每小时$15~25 服务费 2014年至今
    Deliv 每小时$13~18 零交货时收到50%工资 2012年至今
    沃尔玛 每小时$11 享受折扣商品 2013年至今
    任务定价 Doordash 每单$5~8 额外费用受配送距离、服务餐厅、高峰时段等影响,平均每单$25 2013年至今
    Instacart 基本费用+ 每单$3.99 超过$35的部分收取5%服务费与客户小费,平均每单$7~20 2012年至今
    美团、盒马、有赞等中国新零售企业 每单¥5~8 超单奖励、用户红包、补助、高峰时段奖励、节假日奖励等 2015年至今
    UberEats 每单$4~5 票价、交货率、服务费等加15%~25%,总计每单$8~12 2014年至今
    Shyp 每单$5 根据包裹尺寸增加至每单$14~17 2013年至今
    里程定价 UberRush 每公里$3.10~4.35 每增加1公里增加$1.24~2.49 2014年至今
    投标定价 Nimber 投标价格 投标价格 2016年至今
    DHL MyWays 投标价格 扣除10%交易费 2013年至今
    下载: 导出CSV

    表  2  众包配送任务匹配模式内涵

    Table  2.   Connotations of crowdsourcing delivery task matching patterns

    匹配模式 单任务模式 多任务模式 参考文献
    时间匹配模式 [47]、[49]~[51]
    纯路径匹配模式 [5]、[22]、[25]、[34]~[35]、[47]、[52]
    公共交通工具匹配模式 [20]、[24]、[54]~[60]
    混合匹配模式 [61]~[64]
    符号说明
    配送员 起点 终点
    包裹a 起点 终点
    包裹b 起点 终点
    路径
    中继点
    负重
    空载
    下载: 导出CSV

    表  3  众包配送任务匹配模式总结

    Table  3.   Summary of crowdsourcing delivery task matching patterns

    匹配模式 运营主体与研究 运营方式 参考文献
    时间匹配模式 Amazon Flex 处理亚马逊公司1~2 h内需要完成的及时配送任务 [47]、[49]~[50]
    Spark Delivery 沃尔玛超市订单配送 [51]
    UberEats、GrubHub等 进行批量小、批次多又需要在特定时间内完成送货上门服务的食品和杂货的配送服务 [51]
    纯路径匹配模式 DHL MyWays 将愿意参与众包配送人员的路径与包裹的目的地匹配 [52]
    沃尔玛 鼓励到店消费的客户顺路配送线上下单的包裹 [34]
    沃尔玛 鼓励员工在下班路上帮助配送食物和杂货 [35]
    Devari等的研究 利用客户社交网络(熟人和朋友)进行末端配送 [22]
    Alnaggar等的研究 送货要求与旅行者预先计划的旅行相匹配 [47]
    亚特兰大大都市区的微观模拟 通过社区综合物品共享平台鼓励社区居民进行众包配送 [25]
    Wang等的研究 将快递站分区,并将快递站的包裹与乘坐公交的众包配送员匹配,进行“最后一公里”配送 [5]
    任务匹配模式 PiggyBaggy 通过自行车运输图书 [23]
    Kafle等的研究 行人和自行车众包配送员通过投标参与配送 [53]
    公共交通工具匹配模式 Miller等的研究 利用私家车的闲置空间进行众包配送 [14]、[24]
    Li等的研究 将包裹配送任务插入到出租车乘客的行程线路 [56]~[58]
    Murray等的研究 无人机参与配送 [59]
    Gatta等的研究 地铁乘客作为众包配送员进行配送 [20]
    Binetti等的研究 鼓励自行车共享系统的用户自愿承担城市配送任务 [60]
    混合匹配模式 Raviv等的研究 将自动服务点作为众包配送员的接力场所 [62]~[63]
    Akeb等的研究 将客户暂时不便接收的众包配送包裹通过邻里代收 [64]
    下载: 导出CSV

    表  4  众包配送任务匹配模型研究

    Table  4.   Research on crowdsourcing delivery task matching models

    研究范围 研究模型 研究目标 研究方法 参考文献
    按路径匹配 纯路径匹配模型 配送成本最低 构建半机会感知任务匹配模型,运用基于强化学习的参与者选择算法解决众包配送任务匹配问题 [67]
    配送成本最低 考虑中间仓库的众包配送和传统配送联合配送的配送模型 [63]
    决策时间最短 构建众包配送任务匹配建模为动态调度波模型,通过带有预定路线的最佳先验政策算法求解 [68]
    公共交通工具匹配模型 配送时间最短 构建了两阶段的决策模型,第1阶段是离线出租车轨迹挖掘,第2阶段是在线包裹路由寻找和出租车调度 [71]
    按时间和路径混合匹配 任务匹配模型 配送成本最低 构建了考虑需求异致性的分支价格模型,并采用启发式算法求解模型 [69]
    考虑时间窗的综合成本最低 构建混合整数线性规划模型,通过紧急搜索算法求解 [53]
    综合考虑交付成本最低和服务质量最优 提出滚动地平线调度方法求解基于现状的任务匹配和基于预测的任务匹配问题 [70]
    按时间匹配 时间匹配模型 研究服务领域、服务质量、交付能力规划、交付成本及各要素之间的相互作用 将动态定价和服务覆盖率规划作为操纵需求杠杆,建立众包配送任务匹配模型 [65]
    配送路径最短 考虑众包配送员停车意愿,建立众包与传统配送联合的动态任务匹配模型,通过滚动地平线方法反复求解 [66]
    下载: 导出CSV
  • [1] ROUGÈS J F, MONTREUIL B. Crowdsourcing delivery: new interconnected business models to reinvent delivery[C]// IPIC. 1st International Physical Internet Conference. Atlanta: IPIC, 2014: 1-19.
    [2] 房殿军, 黄一丁, 蒋红琰, 等. 智能物流数据治理与技术研究[J]. 信息技术与网络安全, 2022, 41(5): 9-16.

    FANG Dian-jun, HUANG Yi-ding, JIANG Hong-yan, et al. Research on data governance and technology of intelligent logistics[J]. Cyber Security and Data Governance, 2022, 41(5): 9-16. (in Chinese)
    [3] GUO Yu-han, YU Jun-yu, ALLAOUI H, et al. Lateral collaboration with cost-sharing in sustainable supply chain optimisation: a combinatorial framework[J]. Transportation Research Part E: Logistics and Transportation Review, 2022, 157: 102593. doi: 10.1016/j.tre.2021.102593
    [4] DE BLASIO B. Improving the efficiency of truck deliveries in NYC[R]. New York: New York City Department of Transportation, 2019.
    [5] WANG Yuan, ZHANG Dong-xiang, LIU Qing, 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. doi: 10.1016/j.tre.2016.06.002
    [6] GDOWSKA K, VIANA A, PEDROSO J P. Stochastic last-mile delivery with crowdshipping[J]. Transportation Research Procedia, 2018, 30: 90-100. doi: 10.1016/j.trpro.2018.09.011
    [7] BORANGIU T, TRENTESAUX D, THOMAS A, et al. Service Orientation in Holonic and Multi-Agent Manufacturing[M]. Berlin: Spring, 2016.
    [8] SINDLINGER T S. Crowdsourcing: why the power of the crowd is driving the future of business[J]. American Journal of Health-System Pharmacy, 2010, 67(18): 1565-1566. doi: 10.2146/ajhp100029
    [9] PUNEL A, STATHOPOULOS A. Modeling the acceptability of crowdsourced goods deliveries: role of context and experience effects[J]. Transportation Research Part E: Logistics and Transportation Review, 2017, 105: 18-38. doi: 10.1016/j.tre.2017.06.007
    [10] MEHMANN J, FREHE V, TEUTEBERG F. Crowd logistics— a literature review and maturity model[C]//HICL. Proceedings of the Hamburg International Conference of Logistics. Hamburg: HICL, 2015: 117-145.
    [11] JALLER M, OTERO-PALENCIA C, PAHWA A. Automation, electrification, and shared mobility in urban freight: opportunities and challenges[J]. Transportation Research Procedia, 2020, 46: 13-20. doi: 10.1016/j.trpro.2020.03.158
    [12] RAI H B, VERLINDE S, MERCKX J, et al. Crowd logistics: an opportunity for more sustainable urban freight transport?[J]. European Transport Research Review, 2017, DOI: 10.1007/s12544-017-0256-6.
    [13] POURRAHMANI E, JALLER M. Crowdshipping in last mile deliveries: operational challenges and research opportunities[J]. Socio-Economic Planning Sciences, 2021, 78: 101063. doi: 10.1016/j.seps.2021.101063
    [14] SEGHEZZI A, MANGIARACINA R, TUMINO A, et al. 'Pony express crowdsourcing'logistics for last-mile delivery in B2C e-commerce: an economic analysis[J]. International Journal of Logistics Research and Applications, 2021, 24(5): 456-472. doi: 10.1080/13675567.2020.1766428
    [15] GATTA V, MARCUCCI E, NIGRO M, et al. Public transport-based crowdshipping for sustainable city logistics: assessing economic and environmental impacts[J]. Sustainability, 2019, DOI: 10.3390/su11010145.
    [16] MACHARIS C, KIN B. The 4 A's of sustainable city distribution: innovative solutions and challenges ahead[J]. International Journal of Sustainable Transportation, 2017, 11(2): 59-71. doi: 10.1080/15568318.2016.1196404
    [17] LEE H L, CHEN Y, GILLAI B, et al. Technological disruption and innovation in last-mile delivery[R]. Palo Alto: Stanford Graduate School of Business, 2016.
    [18] MLADENOW A, BAUER C, STRAUSS C. "Crowd logistics": the contribution of social crowds in logistics activities[J]. International Journal of Web Information Systems, 2016, 12(3): 379-396. doi: 10.1108/IJWIS-04-2016-0020
    [19] DAI Hong-yan, LIU Peng, LIU Yang. Capacity planning for O2O on-demand delivery systems with crowd-sourcing[J]. SSRN Electronic Journal, 2017, 38(1): 1-25.
    [20] GATTA V, MARCUCCI E, NIGRO M, et al. Sustainable urban freight transport adopting public transport-based crowdshipping for B2C deliveries[J]. European Transport Research Review, 2019, 11(1): 1-14. doi: 10.1186/s12544-018-0328-2
    [21] ARSLAN A M, AGATZ N, KROON L, et al. Crowdsourced delivery: a dynamic pickup and delivery problem with ad-hoc drivers[J]. Social Science Electronic Publishing, 2016, DOI: 10.2139/ssrn.2726731.
    [22] DEVARI A, NIKOLAEV A G, HE Qing. 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. doi: 10.1016/j.tre.2017.06.011
    [23] PALOHEIMO H, LETTENMEIER M, WARIS H. Transport reduction by crowdsourced deliveries—a library case in Finland[J]. Journal of Cleaner Production, 2016, 132: 240-251. doi: 10.1016/j.jclepro.2015.04.103
    [24] MILLER J, NIE Y M, STATHOPOULOS A. Crowdsourced urban package delivery: modeling traveler willingness to work as crowdshippers[J]. Transportation Research Record: Journal of the Transportation Research Board, 2017, 2610(1): 67-75. doi: 10.3141/2610-08
    [25] BEHREND M, MEISEL F. The integration of item-sharing and crowdshipping: can collaborative consumption be pushed by delivering through the crowd?[J]. Transportation Research Part B: Methodological, 2018, 111: 227-243. doi: 10.1016/j.trb.2018.02.017
    [26] 刘雅儒. 众包配送模式及其发展趋势研究[J]. 物流工程与管理, 2016, 38(4): 32-33, 40. doi: 10.3969/j.issn.1674-4993.2016.04.014

    LIU Ya-ru. Study on model of crowdsourcing express and its development tendency[J]. Logistics Engineering and Management, 2016, 38(4): 32-33, 40. (in Chinese) doi: 10.3969/j.issn.1674-4993.2016.04.014
    [27] 郑璇池. 基于激励机制与路径规划的快递众包配送任务调度研究[D]. 哈尔滨: 哈尔滨工业大学, 2021.

    ZHENG Xuan-chi. Research on the scheduling of express crowdsourcing distribution task based on incentive mechanism and path planning[D]. Harbin: Harbin Institute of Technology, 2021. (in Chinese)
    [28] RAYLE L, DAI D, CHAN N, et al. Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco[J]. Transport Policy, 2016, 45: 168-178. doi: 10.1016/j.tranpol.2015.10.004
    [29] PUNEL A, ERMAGUN A, STATHOPOULOS A. Studying determinants of crowd-shipping use[J]. Travel Behaviour and Society, 2018, 12: 30-40. doi: 10.1016/j.tbs.2018.03.005
    [30] RAI H B, VERLINDE S, MACHARIS C. Shipping outside the box. Environmental impact and stakeholder analysis of a crowd logistics platform in Belgium[J]. Journal of Cleaner Production, 2018, 202: 806-816. doi: 10.1016/j.jclepro.2018.08.210
    [31] 冯鑫, 陈旎珊. 基于众包物流配送模式的生产配送协同调度多目标优化[J]. 系统工程, 2022, 40(5): 94-103.

    FENG Xin, CHEN Ni-shan. Bi-objective optimization problem of integrated production and transportation scheduling with crowdsource deliveries[J]. System Engineering, 2022, 40(5): 94-103. (in Chinese)
    [32] BLIXKHAN S. Creating a user manual for healthy crowd engagement: a review of mark hedges and Stuart Dunn's academic crowdsourcing in the humanities: crowds, communities and co-production[J]. DHQ: Digital Humanities Quarterly, http://www.digitalhumanities.org/dhq/vol/13/4/000435/000435.html.
    [33] YILDIZ B, SAVELSBERGH M. Provably high-quality solutions for the meal delivery routing problem[J]. Transportation Science, 2019, 53(5): 1372-1388. doi: 10.1287/trsc.2018.0887
    [34] FAULIN J, GRASMAN S E, JUAN A A, et al. Sustainable Transportation and Smart Logistics: Decision-Making Models and Solutions[M]. Amsterdam: Elsevier, 2019.
    [35] TAYLOR T A. On-demand service platforms[J]. Manufacturing and Service Operations Management, 2018, 20(4): 704-720. doi: 10.1287/msom.2017.0678
    [36] FISCHER J, SERSLI S, NELSON T, et al. Spatial variation in bicycling risk based on crowdsourced safety data[J]. Canadian Geographies-Le Géographe Canadien, 2022, 66(3): 556-568. doi: 10.1111/cag.12756
    [37] MARCUCCI E, LE PIRA M, CARROCCI C S, et al. Connected shared mobility for passengers and freight: investigating the potential of crowdshipping in urban areas[C]// IEEE. 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). New York: IEEE, 2017: 839-843.
    [38] LE T V, UKKUSURI S V. Crowd-shipping services for last mile delivery: analysis from survey data in two countries[C]// TRB. 97th Annual Meeting Transportation Research Board. Washington DC: TRB, 2018, DOI: 10.48550/arXiv.1810.02856.
    [39] BINETTI M, CAGGIANI L, CAMPOREALE R, et al. A sustainable crowdsourced delivery system to foster free-floating bike-sharing[J]. Sustainability, 2019, 11(10): 2772. doi: 10.3390/su11102772
    [40] ERMAGUN A, PUNEL A, STATHOPOULOS A. Shipment status prediction in online crowd-sourced shipping platforms[J]. Sustainable Cities and Society, 2020, 53: 101950. doi: 10.1016/j.scs.2019.101950
    [41] ERMAGUN A, STATHOPOULOS A. To bid or not to bid: an empirical study of the supply determinants of crowd-shipping[J]. Transportation Research, 2018, 116: 468-483.
    [42] TANIGUCHI E, THOMPSON R G. City Logistics 3: Towards Sustainable and Liveable Cities[M]. London: ISTE Ltd., 2018.
    [43] TANIGUCHI E, THOMPSON R G, QURESHI A G. Modelling city logistics using recent innovative technologies[J]. Transportation Research Procedia, 2020, 46: 3-12. doi: 10.1016/j.trpro.2020.03.157
    [44] LIANG Xiao-ping, YANG Hua-long, WU Qiong. Online crowdsourced delivery for urban parcels using private cars under time-dependent travel times[J]. Computers and Industrial Engineering, 2022, 174: 108807. doi: 10.1016/j.cie.2022.108807
    [45] ELCART Z, MILLER K, TAN S, et al. Using public transportation to facilitate last mile package delivery[R]. Austin: Texas Department of Transportation, 2016.
    [46] LE T V, STATHOPOULOS A, VAN WOENSEL T, et al. Supply, demand, operations, and management of crowd-shipping services: a review and empirical evidence[J]. Transportation Research Part C: Emerging Technologies, 2019, 103: 83-103. doi: 10.1016/j.trc.2019.03.023
    [47] ALNAGGAR A, GZARA F, BOOKBINDER J H. Crowdsourced delivery: a review of platforms and academic literature[J]. Omega: the International Journal of Management Science, 2021, 98: 102139. doi: 10.1016/j.omega.2019.102139
    [48] 王强, 植赐佳, 徐佳. 基于声誉系统的软件众测任务分配机制[J]. 南京理工大学学报, 2022, 46(5): 561-570. doi: 10.14177/j.cnki.32-1397n.2022.46.05.007

    WANG Qiang, ZHI Ci-jia, XU Jia. Task allocation mechanism for software crowdsourced testing based on reputation system[J]. Journal of Nanjing University of Science and Technology, 2022, 46(5): 561-570. (in Chinese) doi: 10.14177/j.cnki.32-1397n.2022.46.05.007
    [49] ALTENRIED M. On the last mile: logistical urbanism and the transformation of labour[J]. Work Organisation, Labour and Globalisation, 2019, 13(1): 114-129.
    [50] 梁兆佐. 电子零售平台在新冠病毒疫情下的韧性—以Amazon为例[D]. 台北: 台湾大学, 2020.

    LIANG Zhao-zuo. The resiliency of online retail platform providers under the global COVID-19 pandemic—case study of Amazon[D]. Taipei: National Taiwan University, 2020. (in Chinese)
    [51] 兰宇琳. 面向城市物流配送的多目标路径优化问题研究[D]. 广州: 华南理工大学, 2021.

    LAN Yu-lin. Research on multi-objective routing optimization problem in city logistics dispatching[D]. Guangzhou: South China University of Technology, 2021. (in Chinese)
    [52] SERAFINI S, NIGRO M, GATTA V, et al. Sustainable crowdshipping using public transport: a case study evaluation in Rome[J]. Transportation Research Procedia, 2018, 30: 101-110. doi: 10.1016/j.trpro.2018.09.012
    [53] KAFLE N, ZOU Bo, 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. doi: 10.1016/j.trb.2016.12.022
    [54] QI Wei, LI Le-fei, LIU Sheng, et al. Shared mobility for last-mile delivery: design, operational prescriptions, and environmental impact[J]. Manufacturing and Service Operations Management, 2018, 20(4): 737-751. doi: 10.1287/msom.2017.0683
    [55] GHILAS V, DEMIR E, VAN WOENSEL T. An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows and scheduled lines[J]. Computers and Operations Research, 2016, 72: 12-30. doi: 10.1016/j.cor.2016.01.018
    [56] LI Bao-xiang, KRUSHINSKY D, REIJERS H A, et al. The share-a-ride problem: people and parcels sharing taxis[J]. European Journal of Operational Research, 2014, 238(1): 31-40. doi: 10.1016/j.ejor.2014.03.003
    [57] LI Bao-xiang, KRUSHINSKY D, VAN WOENSEL T, et al. An adaptive large neighborhood search heuristic for the share-a-ride problem[J]. Computers and Operations Research, 2016, 66: 170-180. doi: 10.1016/j.cor.2015.08.008
    [58] LI Bao-xiang, KRUSHINSKY D, VAN WOENSEL T, et al. The share-a-ride problem with stochastic travel times and stochastic delivery locations[J]. Transportation Research Part C: Emerging Technologies, 2016, 67: 95-108. doi: 10.1016/j.trc.2016.01.014
    [59] MURRAY C C, CHU A G. The flying sidekick traveling salesman problem: optimization of drone-assisted parcel delivery[J]. Transportation Research Part C: Emerging Technologies, 2015, 54: 86-109. doi: 10.1016/j.trc.2015.03.005
    [60] TU Wei, ZHAO Tian-hong, ZHOU Bao-ding, et al. OCD: online crowdsourced delivery for on-demand food[J]. IEEE Internet of Things Journal, 2020, 7(8): 6842-6854. doi: 10.1109/JIOT.2019.2930984
    [61] SARIKLIS D, POWELL S. A heuristic method for the open vehicle routing problem[J]. Journal of the Operational Research Society, 2000, 51(5): 564-573. doi: 10.1057/palgrave.jors.2600924
    [62] RAVIV T, TENZER E Z. Crowd-shipping of small parcels in a physical internet[R]. Tel Aviv: Tel Aviv University, 2018.
    [63] MACRINA G, DI PUGLIA PUGLIESE L, GUERRIERO F, et al. Crowd-shipping with time windows and transshipment nodes[J]. Computers and Operations Research, 2020, 113: 104806. doi: 10.1016/j.cor.2019.104806
    [64] 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. doi: 10.1016/j.tre.2018.04.007
    [65] YILDIZ B, SAVELSBERGH M. Service and capacity planning in crowd-sourced delivery[J]. Transportation Research Part C: Emerging Technologies, 2019, 100: 177-199. doi: 10.1016/j.trc.2019.01.021
    [66] ARSLAN A M, AGATZ N, KROON L, et al. Crowdsourced delivery—a dynamic pickup and delivery problem with ad hoc drivers[J]. Transportation Science, 2019, 53(1): 222-235. doi: 10.1287/trsc.2017.0803
    [67] 谢珍真. 基于深度学习的众包计算资源分配与群体行为异构性研究[D]. 长春: 吉林大学, 2021.

    XIE Zhen-zhen. Research on resource allocation mechanism and heterogeneity problem in crowd-sourced computing based on deep learning[D]. Changchun: Jilin University, 2021. (in Chinese)
    [68] KLAPP M A, ERERA A L, TORIELLO A. The one-dimensional dynamic dispatch waves problem[J]. Transportation Science, 2018, 52(2): 402-415. doi: 10.1287/trsc.2016.0682
    [69] TORRES F, GENDREAU M, REI W. Crowdshipping: an open VRP variant with stochastic destinations[J]. Transportation Research Part C: Emerging Technologies, 2022, 140: 103677. doi: 10.1016/j.trc.2022.103677
    [70] DAYARIAN I, SAVELSBERGH M. Crowdshipping and same-day delivery: employing in-store customers to deliver online orders[J]. Production and Operations Management, 2020, 29(9): 2153-2174. doi: 10.1111/poms.13219
    [71] CHEN Chao, PAN Shen-le. Service Orientation in Holonic and Multi-Agent Manufacturing[M]. Berlin: Springer, 2016.
    [72] DANTZIG G B, RAMSER J H. The truck dispatching problem[J]. Management Science, 1959, 6(1): 80-91 doi: 10.1287/mnsc.6.1.80
    [73] ARCHETTI C, SAVELSBERGH M, SPERANZA M G. The vehicle routing problem with occasional drivers[J]. European Journal of Operational Research, 2016, 254(2): 472-480. doi: 10.1016/j.ejor.2016.03.049
    [74] 周鲜成, 吕阳, 贺彩虹, 等. 考虑时变速度的多车场绿色车辆路径模型及优化算法[J]. 控制与决策, 2022, 37(2): 473-482.

    ZHOU Xian-cheng, LYU Yang, HE Cai-hong, et al. Multi-depot green vehicle routing model and its optimization algorithm with time-varying speed[J]. Control and Decision, 2022, 37(2): 473-482. (in Chinese)
    [75] KALAKANTI A K, VERMA S, PAUL T, et al. RL solver pro: reinforcement learning for solving vehicle routing problem[C]//IEEE. 2019 1st International Conference on Artificial Intelligence and Data Sciences (AIDAS). New York: IEEE, 2020: 94-99.
    [76] 胡蓉, 陈文博, 钱斌, 等. 学习型蚁群算法求解绿色多车场车辆路径问题[J]. 系统仿真学报, 2021, 33(9): 2095-2108. https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ202109012.htm

    HU Rong, CHEN Wen-bo, QIAN Bin, et al. Learning ant colony algorithm for green multi-depot vehicle routing problem[J]. Journal of System Simulation, 2021, 33(9): 2095-2108. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ202109012.htm
    [77] 徐东洋, 李昆鹏, 郑飘, 等. 多车场多车型多品类供需未匹配与可任意拆分取送货车辆路径问题优化[J]. 管理学报, 2020, 17(7): 1086-1095. doi: 10.3969/j.issn.1672-884x.2020.07.016

    XU Dong-yang, LI Kun-peng, ZHENG Piao, et al. The optimization research of multi-category unpaired supply-demand and arbitrary split pickup and delivery vehicle routing problem with multi-depot and multi-type trucks[J]. Chinese Journal of Management, 2020, 17(7): 1086-1095. (in Chinese) doi: 10.3969/j.issn.1672-884x.2020.07.016
    [78] 范双南, 陈纪铭, 高为民, 等. 基于改进智能水滴算法的动态车辆配送路径优化[J]. 系统仿真学报, 2020, 32(9): 1808-1817. https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ202009020.htm

    FAN Shuang-nan, CHEN Ji-ming, GAO Wei-min, et al. Dynamic vehicle distribution path optimization based on improved intelligent water drop algorithm[J]. Journal of Systems Simulation, 2020, 32(9): 1808-1817. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ202009020.htm
    [79] BEN TICHA H, ABSI N, FEILLRT D, et al. A branch- and-price algorithm for the vehicle routing problem with time windows on a road network[J]. Networks, 2019, 73(4): 401-417. doi: 10.1002/net.21852
    [80] KIM G, ONG Y S, CHEONG T, et al. Solving the dynamic vehicle routing problem under traffic congestion[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(8): 2367-2380. doi: 10.1109/TITS.2016.2521779
    [81] KUO Y Y, WANG Chi-chang. A variable neighborhood search for the multi-depot vehicle routing problem with loading cost[J]. Expert Systems with Applications, 2012, 39(8): 6949-6954. doi: 10.1016/j.eswa.2012.01.024
    [82] LETCHFORD A N, LYSGAARD J, EGLESE R W. A branch-and-cut algorithm for the capacitated open vehicle routing problem[J]. Journal of the Operational Research Society, 2007, 58(12): 1642-1351. doi: 10.1057/palgrave.jors.2602345
    [83] BARKAOUI M. A co-evolutionary approach using information about future requests for dynamic vehicle routing problem with soft time windows[J]. Memetic Computing, 2018, 10: 307-319. doi: 10.1007/s12293-017-0231-8
    [84] SUN Liang, PAN Quan-ke, JING Xue-lei, et al. A light-robust-optimization model and an effective memetic algorithm for an open vehicle routing problem under uncertain travel times[J]. Memetic Computing, 2021, 13: 149-167.
    [85] GUPTA A, HENG C K, ONG Y S, et al. A generic framework for multi-criteria decision support in eco-friendly urban logistics systems[J]. Expert Systems with Applications, 2017, 71: 288-300.
    [86] ZHANG Ke, FANG He, ZHANG Zheng-chao, et al. Multi-vehicle routing problems with soft time windows: a multi-agent reinforcement learning approach[J]. Transportation Research Part C: Emerging Technologies, 2020, 121: 102861.
    [87] WANG Jia-hai, WENG Tai-yao, ZHANG Qing-fu. A two-stage multiobjective evolutionary algorithm for multiobjective multidepot vehicle routing problem with time windows[J]. IEEE Transactions on Cybernetics, 2019, 49(7): 2467-2478.
    [88] 陈诚, 刘燕萍, 林秋婷, 等. 考虑车速时空动态性的城市配送车辆路径问题[J]. 工业工程与管理, 2021, 26(3): 56-62. https://www.cnki.com.cn/Article/CJFDTOTAL-GYGC202103008.htm

    CHEN Cheng, LIU Yan-ping, LIN Qiu-ting, et al. On time and space dependent vehicle routing problem in urban delivery[J]. Industrial Engineering and Management, 2021, 26(3): 56-62. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GYGC202103008.htm
    [89] 符卓, 刘文, 邱萌. 带软时间窗的需求依订单拆分车辆路径问题及其禁忌搜索算法[J]. 中国管理科学, 2017, 25(5): 78-86. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGK201705010.htm

    FU Zhuo, LIU Wen, QIU Meng. A tabu search algorithm for the vehicle routing problem with soft time windows and split deliveries by order[J]. Chinese Journal of Management Science, 2017, 25, (5): 78-86. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGK201705010.htm
    [90] 高振迪, 计明军, 孔灵睿, 等. 多商品分批次取送货的模糊需求车辆路径问题[J]. 运筹与管理, 2022, 31(11): 59-64.

    GAO Zhen-di, JI Ming-jun, KONG Ling-rui, et al. Multi-commodity vehicle routing problem with split pickup and delivery and fuzzy demand[J]. Operations Research and Management Science, 2022, 31(11): 59-64. (in Chinese)
    [91] LI Hong-ye, WANG Lei, HEI Xing-hong, et al. A decomposition-based chemical reaction optimization for multi-objective vehicle routing problem for simultaneous delivery and pickup with time w indows[J]. Memetic Computing, 2018, 10: 103-120.
    [92] TEYMOURIAN E, KAYVANFAR V, KOMAKI G M, et al. Enhanced intelligent water drops and cuckoo search algorithms for solving the capacitated vehicle routing problem[J]. Information Sciences, 2016, 334/335: 354-378.
    [93] PELLETIER S, JABALI O, LAPORTE G. The electric vehicle routing problem with energy consumption uncertainty[J]. Transportation Research Part B: Methodological, 2019, 126: 225-255.
    [94] WANG Zheng, SHEU J B. Vehicle routing problem with drones[J]. Transportation Research Part B: Methodological, 2019, 122: 350-364.
    [95] SCHERMER D, MOEINI M, WENDT O. A matheuristic for the vehicle routing problem with drones and its variants[J]. Transportation Research Part C: Emerging Technologies, 2019, 106: 166-204.
    [96] 王勇, 张杰, 刘永, 等. 基于资源共享和温度控制的生鲜商品多中心车辆路径优化问题[J]. 中国管理科学, 2022, 30(11): 272-285. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGK202211026.htm

    WANG Yong, ZHANG Jie, LIU Yong, et al. Optimization of fresh goods multi-center vehicle routing problem based on resource sharing and temperature control[J]. Chinese Journal of Management Science, 2022, 30(11): 272-285. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGK202211026.htm
    [97] WANG Yong, WANG Zheng, HU Xiang-pei, et al. Truck-drone hybrid routing problem with time-dependent road travel time[J]. Transportation Research Part C: Emerging Technologies, 2022, 144: 103901.
    [98] WANG Yong, YUAN Ying-ying, GUAN Xiang-yang, et al. Collaborative two-echelon multicenter vehicle routing optimization based on state-space-time network representation[J]. Journal of Cleaner Production, 2020, 258: 120590.
    [99] 范厚明, 徐振林, 李阳, 等. 开放式多中心需求可拆分VRP及混沌遗传模拟退火算法[J]. 运筹与管理, 2022, 31(1): 92-98.

    FAN Hou-ming, XU Zhen-lin, LI Yang, et al. Chaos genetic simulated annealing algorithm for the open multi-depot split delivery vehicle routing problem[J]. Operations Research and Management Science, 2022, 31(1): 92-98. (in Chinese)
    [100] FENG Liang, ZHOU Lei, GUPTA A, et al. Solving generalized vehicle routing problem with occasional drivers via evolutionary multitasking[J]. IEEE Transactions on Cybernetics, 2021, 51(6): 3171-3184.
    [101] DAHLE L, ANDERSSON H, CHRISTIANSEN M, et al. The pickup and delivery problem with time windows and occasional drivers[J]. Computers and Operations Research, 2019, 109: 122-133.
    [102] YANG Ding-tong. Planning and operation of a crowdsourced package delivery system: models, algorithms and applications[D]. Irvine: University of California, 2021.
    [103] BRAEKERS K, RAMAEKERS K, VAN NIEUWENHUYSE I. The vehicle routing problem: state of the art classification and review[J]. Computers and Industrial Engineering, 2016, 99: 300-313.
    [104] 谭志龙, 王征, 薛桂琴, 等. 基于社会化库存的多回程物流配送问题的拉格朗日松弛算法[J]. 计算机集成制造系统, 2021, 27(3): 965-972. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ202103027.htm

    TAN Zhi-long, WANG Zheng, XUE Gui-qin, et al. Improved Lagrangian relaxation algorithm based on socialized inventory for multi-trip distribution problem[J]. Computer Integrated Manufacturing Systems, 2021, 27(3): 965-972. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ202103027.htm
    [105] AWD H, ELSHAER R. A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants[J]. Computers and Industrial Engineering, 2019, 140: 106242.
    [106] 庞燕, 罗华丽, 夏扬坤. 基于禁忌搜索算法的废弃家具回收车辆路径优化[J]. 计算机集成制造系统, 2020, 26(5): 1425-1433. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ202005026.htm

    PANG Yan, LUO Hua-li, XIA Yang-kun. Waste furniture recycling vehicle routing optimization based on tabu search algorithm[J]. Computer Integrated Manufacturing Systems, 2020, 26(5): 1425-1433. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ202005026.htm
    [107] PEI Jun, MLADENOVIĆ N, UROŠEVIĆ D, et al. Solving the traveling repairman problem with profits: a novel variable neighborhood search approach[J]. Information Sciences, 2020, 507: 108-123.
    [108] AZI N, GENDREAU M, POTVIN J Y. An adaptive large neighborhood search for a vehicle routing problem with multiple routes[J]. Computers and Operations Research, 2014, 41: 167-173.
    [109] SACRAMENTO D, PISINGER D, ROPKE S. An adaptive large neighborhood search metaheuristic for the vehicle routing problem with drones[J]. Transportation Research Part C: Emerging Technologies, 2019, 102: 289-315.
    [110] 崔俊云, 陈迪, 袁野, 等. 空间众包中在线路径规划算法[J]. 清华大学学报(自然科学版), 2020, 60(8): 672-682. https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB202008007.htm

    CUI Jun-yun, CHEN Di, YUAN Ye, et al. Online rout planning algorithm in spatial crowdsourcing[J]. Journal of Tsinghua University (Science and Technology), 2020, 60(8): 672-682. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB202008007.htm
    [111] 杜子超, 卢福强, 王素欣, 等. 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. https://www.cnki.com.cn/Article/CJFDTOTAL-DBDX202108021.htm

    DU Zi-chao, LU Fu-qiang, WANG Su-xin, et al. Vehicle scheduling model and optimization of crowdsourcing logistics distribution[J]. Journal of Northeastern University (Natural Science), 2021, 42(8): 1210-1216. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DBDX202108021.htm
    [112] 邓丽娟, 张纪会. 混合蚁群算法求解双目标时间窗VRP[J]. 复杂系统与复杂性科学, 2020, 17(4): 73-84.

    DENG Li-juan, ZHANG Ji-hui. A hybrid ant colony optimization for bi-objective VRP with time windows[J]. Complex Systems and Complexity Science, 2020, 17(4): 73-84. (in Chinese)
    [113] 熊国文, 张敏, 许文鑫. 基于众包模式的两级开闭混合车辆路径优化[J]. 浙江大学学报(工学版), 2021, 55(12): 2397-2408. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC202112021.htm

    XIONG Guo-wen, ZHANG Min, XU Wen-xin. Vehicle routing optimization of two-echelon opening and closing hybrid based on crowdsourcing mode[J]. Journal of Zhejiang University (Engineering Science), 2021, 55(12): 2397-2408. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC202112021.htm
    [114] 雷坤, 郭鹏, 王祺欣, 等. 基于end-to-end深度强化学习的多车场车辆路径优化[J]. 计算机应用研究, 2022, 39(10): 3013-3019.

    LEI Kun, GUO Peng, WANG Qi-xin, et al. End-to-end deep reinforcement learning framework for multi-depot vehicle routing problem[J]. Application Research of Computers, 2022, 39(10): 3013-3019. (in Chinese)
    [115] 纪苗苗, 吴志彬. 考虑工人路径的多智能体强化学习空间众包任务分配方法[J]. 控制与决策, http://kzyjc.alljournals.cn/kzyjc/article/abstract/2022-1319.

    JI Miao-miao, WU Zhi-bin. A multi-agent reinforcement learning algorithm for spatial crowdsourcing task assignments considering workers' path[J]. Control and Decision, http://kzyjc.alljournals.cn/kzyjc/article/abstract/2022-1319. (in Chinese)
    [116] TONG Yong-xin, YUAN Ye, CHENG Yu-rong, et al. Survey on spatiotemporal crowdsourced data management techniques[J]. Journal of Software, 2016, 28(1): 35-58.
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  • 收稿日期:  2023-04-15
  • 网络出版日期:  2023-11-17
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