Development and Comparison of Ten Differential-Evolution and Particle Swarm-Optimization Based Algorithms for Discount-Guaranteed Ridesharing Systems

Abstract

Savings on transportation costs provide an important incentive for shared mobility models in smart cities. Therefore, the problem of maximizing cost savings has been extensively studied in the ridesharing literature. Most studies on ridesharing focus on the maximization of the overall savings on transportation costs. However, the maximization of the overall savings on transportation costs may satisfy users’ expectations for cost savings. For people to adopt ridesharing as a means to reduce costs, a minimal expected cost savings discount must be offered. There is obviously a gap between the existing studies and the real problems faced by service providers. This calls for the development of a study to formulate a ridesharing model that guarantees the satisfaction of a minimal expected cost savings discount. In this paper, we considered a discount-guaranteed ridesharing model that ensures the provision of a minimal expected cost savings discount to ridesharing participants to improve users’ satisfaction with the ridesharing service in terms of cost savings. The goal was to maximize the overall cost savings under certain capacity, spatial, and time constraints and the constraint that the discount offered to ridesharing participants could be no lower than the minimal expected cost savings discount. Due to the complexity of the optimization problem, we adopted two evolutionary computation approaches, differential evolution and particle swarm optimization, to develop ten algorithms for solving the problem. We illustrated the proposed method by an example. The results indicated that the proposed method could guarantee that the discount offered to ridesharing participants was greater than or equal to the minimal expected cost savings discount. We also conducted two series of experiments to assess the performance and efficiency of the different solution algorithms. We analyzed the results to provide suggestions for selecting the appropriate solution algorithm based on its performance and efficiency.