Modeling and Solving the Allocation Problem of Spatiotemporal Crowdsourced Logistics Tasks in Social Manufacturing

Aiming at the allocation problem of crowdsourced logistics tasks in social manufacturing, a bipartite graph is constructed from the location of crowdsourced workers, and the temporal and spatial attributes of the tasks to represent the crowdsourced task allocation model according to its characteristics of spatiotemporal discretization and subject to full allocation. Considering workers’ acceptable distance, transportation cost, and tasks balance, a crowdsourced task allocation model with the overall maximum benefit to workers as the optimization objective is established. First, the dynamic allocation problem is converted to a stationary one using a dynamic programming algorithm to determine the acceptable distance of the workers under the optimal allocation quality, and then the improved beluga whale optimization (IBWO) algorithm is utilized to solve the problem. The Sobol sequence initialization method is used to improve the population diversity and initial solution quality, and the cyclone foraging strategy is used in the development phase to enhance the global search capability and convergence performance of the algorithm. Experiments were conducted on the dataset downloaded from the website (http://dataju.cn). The results show that the overall benefits of the workers improved by 150.12, 181.96, and 260.64, which validates the rationality of the model and the feasibility and practicality of the method.