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Review on research of crowdsourcing delivery(PDF)


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Review on research of crowdsourcing delivery
ZHAO Jian-you LI Yue TIAN Hao TAO Xu-qiu HOU Xue
(School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China)
logistics engineering crowdsourcing delivery freight mode task matching vehicle routing problem graph analysis
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.4 tabs, 10 figs, 116 refs.


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Last Update: 2023-11-10