A Crowdsourcing Matching and Pricing Strategy in Urban Distribution System

The vigorous development of O2O e-commerce promote the appearance of many small orders, increasing the stresses on logistics operators to carry out city distribution. However, a crowdsourcing joined to release these stresses is a new try and become more popular. This paper focuses on matching of the crowds and tasks from crowdsourcing platform for city distribution. To address exploring the impact of time, space and efficiency on the task matching in crowdsourcing platform, a bi-objective matching and differentiated pricing model creates to achieve the highest efficiency and the lowest total cost in urban distribution system. For solving the model, a two-dimensional and multi-stage roulette algorithm has been designed, with combining modeling and simulation method. The proposed method takes full use of economic development of the region where the task is located and the space-time efficient distance and space-time reachable distance. To illustrate the effectiveness and validity of the proposed method, a sample test is conducted with the actual operating data of a company in the PRD region, and the results show that 719 tasks out of 746 matching pairs are executed, the task matching rate is 89.4%, the completion rate is 86.1% and the total task price 39140 RMBs. Compared with the original matching situation, the total price increases at 3.90%, while the task completion rate is improved at 37.7%, which greatly enhance the efficiency of crowdsourcing platform member matching. The matching of the participants and tasks of the city distribution crowdsourcing platform, which combines with the measurement of differential pricing and crowds’ credibility, can be applied in this problem successfully.