Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12–14, 2024, Ningbo, China

Research Article

Research on Task Allocation and Route Joint Optimization under Crowdsourcing Logistics

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  • @INPROCEEDINGS{10.4108/eai.12-1-2024.2347290,
        author={Pingfan  Zhang},
        title={Research on Task Allocation and Route Joint Optimization under Crowdsourcing Logistics},
        proceedings={Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12--14, 2024, Ningbo, China},
        publisher={EAI},
        proceedings_a={BDEDM},
        year={2024},
        month={6},
        keywords={combination of order grabbing and order dispatching mode; crowdsourcing logistics; path optimization; genetic algorithm},
        doi={10.4108/eai.12-1-2024.2347290}
    }
    
  • Pingfan Zhang
    Year: 2024
    Research on Task Allocation and Route Joint Optimization under Crowdsourcing Logistics
    BDEDM
    EAI
    DOI: 10.4108/eai.12-1-2024.2347290
Pingfan Zhang1,*
  • 1: Shanghai Maritime University
*Contact email: zhangpf10121122@163.com

Abstract

This paper studies the crowdsourcing logistics problem of the combination of snatch and dispatch. First of all, based on the existing crowdsourcing tasks, this paper analyzes the task allocation mode of crowdsourcing logistics and the characteristics of crowdsourcing logistics subjects. Then, in view of the crowdsourcing task and the one-time arrival of the crowdsourcing couriers, the order grabbing process of the crowdsourcing logistics platform and the order scheduling and path planning process of the crowdsourcing logistics platform pushing the task are considered, and the model with the distribution cost and overtime cost as the optimization objective is constructed. The random rush rate is designed to control the number of orders. Then genetic algorithm is used to plan the delivery path of the crowdsourcing deliverer. Finally, the effectiveness of the model and algorithm is verified by numerical experiments, which provides reference and decision support for task allocation and route optimization of crowdsourcing logistics platform.