Abstract: Crowdsourcing logistics uses big data technology to match the needs of both the employers and the receivers, thus saving logistics costs. At the same time, the service demand of crowdsourcing logistics has random fluctuation. When the logistics demand increases sharply, the service platform will face shortage of receivers. In addition, due to the characteristics of independent selection of the logistics supply for receivers, there will be fierce competition among the various crowdsourcing logistics service platforms. The service platforms compete not only in the logistics demand market, but also in the supply market of the receivers, such as the plunder of high-quality receivers by JD crowdsourcing and Meituan crowdsourcing. In the actual operating environment of crowdsourcing logistics market with high demand and competitive supply from its receivers, determining the optimal service dynamic competition strategy for the crowdsourcing logistics platform can effectively regulate the supply capacity of the receiver and meet the logistics order demand of the platform, which is of great significance for the operation management and optimization of the crowdsourcing logistics service platform.
In view of the surge of crowdsourcing logistics demand, crowdsourcing logistics service platform is facing the fierce competition environment of supply shortage of receivers. Considering the situation that two service platforms compete with one receiver through commission and big data technology level, a crowdsourcing logistics service quality control’s differential game model based on big data technology level under supply competition caseis constructed. The changes of quality control level, profits and service quality of all parties under three situationsare analyzed, and the influences of choosing the strategy to improve big data technology on the service platform and the receiver are discussed. The results show that the higher the quality sensitivity coefficient, the higher the optimal quality control level of the service platforms and the receiver and the optimal big data technology level. The service platform is willing to invest more funds in the research and development of big data technology to improve the big data technology level, thereby promoting the improvement of quality control level. The higher the delay cost per unit demand, the lower the service platforms’optimal quality control level. The higher the delay cost per unit demand, the higher the cost of not being able to meet demand in a timely manner due to insufficient supply from the receiver, and the lower the willingness of the service platform to improve quality control. The profit of the service platform is positively correlated with the commission sensitivity coefficient and negatively correlated with the commission competition coefficient. The higher the initial crowdsourcing logistics service quality, the higher the profits of service platform and receiver. The cost of paying the same level of the service platform quality control is optimized, thereby increasing the enthusiasm of the service platform for quality control efforts. The service platform adopting big data technology strategy improves the quality control level itself, but it does not affect the quality control level of the competitive platform. The platform’s own profit is increased only when the proportion coefficient of big data technology cost optimization meets certain condition. As for the receiver, in the case of supply competition, the quality control level remains unchanged no matter whether the service platform adopts big data technology strategy or not.
Improving the level of big data technology can improve the crowdsourcing logistics service quality and bring a satisfied consumption experience to consumers. To a certain extent, the improvement of big data technology level can promote the development of crowdsourcing logistics platforms and receivers. In the event that there are fewer receivers who cannot meet all crowdsourcing logistics needs in a timely manner, the service platform can appropriately increase the commissions to win over more receivers and meet the crowdsourcing logistics market with strong demand. During the delivery process, measures such as increasing the supervision of the service quality of the receiver, and inviting the recipient to conduct a satisfaction evaluation on the receiver after delivery are taken to ensure the crowdsourcing logistics quality. Future research can also apply other suitable mathematical models to provide diversified and diverse management suggestions for improving the crowdsourcing logistics services quality.

Key words: differential game, dynamic competition, service quality, big data technology, crowdsourcing logistics

摘要： 物流需求激增会导致众包平台面临接包方供应短缺的竞争环境,针对这种情形,考虑大数据技术建立了众包物流服务质量控制模型并展开了数值分析。研究发现:最优大数据技术水平、服务平台和接包方的最优质量控制水平随质量敏感系数的减少而减少,服务平台的最优质量控制水平随单位需求延误成本的减少而增大;众包物流平台采用大数据技术策略,会提高自身的质量控制水平,但不会使竞争平台的质量控制水平提升或者降低,当大数据技术成本优化比例系数满足特定条件时,会导致更多的平台利润;接包方质量控制水平不随其服务的平台是否应用大数据技术策略而改变。

关键词: 微分博弈, 动态竞争, 服务质量, 大数据技术, 众包物流