A Scheduling Optimization Method of Shared Bicycles Based on a Multi-objective Ant Colony Algorithm

As a crucial mode for facilitating public transportation connections and addressing the "last mile" problem, shared bicycles confront the challenge of supply and demand imbalances. To solve this issue, deploying vehicles for scheduling purposes becomes an essential step in rebalancing the shared bicycles. In order to address the issues of current shared bicycle scheduling methods including single optimization objective, limited visits to scheduling sites, and insufficient consideration of continuous scheduling connections, a multi-objective optimization model is developed in this paper to minimize both total demand dissatisfaction and scheduling costs. This model considers the situation that the demand at the scheduling site surpasses the capacity of the scheduling vehicle during peak hours. Consequently, it enables the scheduling vehicle to make multiple trips to the site and allows to conduct continuous scheduling in multiple periods of time for multiple vehicles. A multi-objective ant colony algorithm is designed to solve this model by integrating the technique of non-dominated sorting to classify the solution set into various levels of non-dominance. The solution at the highest level is then utilized to create a Pareto-optimal solution, which considers two objectives concurrently. This algorithm introduces a new ant system incorporating maximum-minimum criteria, modifies the state transition probability rule and pheromone update rule to enhance their efficacy to deal with the multi-objective optimization problem. In order to verify the feasibility of the model and algorithm, a case study is carried out. The results show that the model is confirmed to be effective in decreasing demand loss while ensuring the lower scheduling costs. Specifically, the total demand dissatisfaction degree is reduced from 26.48% to 17.86%. Comparing the results of the multi-objective ant colony algorithm and greedy algorithm under various example sizes, the multi-objective ant colony algorithm shows a clear superiority in continuous scheduling of multiple periods of time. Specifically, it is capable of organizing the driving path of each scheduling vehicle in each scheduling cycle, as well as the arrival time and the loading and unloading numbers of shared bicycles at each scheduling site. Meanwhile, compared with greedy algorithm, the multi-objective ant colony algorithm shows a clear superiority in the quality of the solutions, and the scheduling costs and total demand dissatisfaction degree obtained in a large-scale case are reduced by 62% and 23%, respectively.