众包配送研究综述

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。

Review on research of crowdsourcing delivery

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.