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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。Abstract: Based on 1 495 literatures collected in the SCI database and CNKI database, the knowledge graph analysis software VOSviewer was used to perform the clustering analysis of the co-occurrence terms of crowdsourcing delivery. The influencing factors related to the participants engaged in crowdsourcing delivery, the operations of crowdsourcing delivery platforms, and the routes of crowdsourcing delivery vehicles were systematically reviewed. The present situation of crowdsourcing delivery in China and abroad was analyzed, the existing problems of crowdsourcing delivery were discussed, and the future development directions of crowdsourcing delivery were put forward. Research results show that in terms of participants in crowdsourcing delivery, price, safety, and delivery difficulty are important factors affecting the popularity of crowdsourcing delivery model. In terms of platform operation, the existing crowdsourcing delivery operation platforms take the minimum cost or shortest path as the goal to build a single task matching function. In terms of vehicle routing problem, the existing crowdsourcing delivery mostly relies on the existing databases to solve the vehicle routing problem with a heuristic algorithm. The future development trend of crowdsourcing delivery research mainly lies in studying the influencing factors of crowdsourcing participants, reasonably adjusting the delivery prices, and refining the scenario to attract participants with different characteristics and adapt to regional customer density and economic development level differences. In addition, in order to improve the service level and competitiveness of crowdsourcing delivery platform, the factors, such as safety, resources, environment, and transportation, should be incorporated into the delivery platform to build a multi-objective task matching function. In order to adapt to the characteristics of crowdsourcing delivery and improve the response speed of crowdsourcing delivery system, a multi-objective path optimization function with priority should be constructed. Artificial intelligence algorithms and other tools should be used to optimize the problem of crowdsourcing delivery routes.
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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
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 
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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]
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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]
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