A Hybrid Firefly-DE algorithm for Ridesharing Systems with Cost Savings Allocation Schemes

Abstract

Ridesharing or shared mobility have been attracting significant attention in relevant research community. Most studies focus on how to match drivers and riders to minimize the overall travel distance based on their requirements. As cost savings is an essential function in ridesharing systems, allocation of cost savings has attracted researchers' attention recently. Several simple schemes have been proposed in the literature. For example, a simple scheme is to divide cost savings equally between driver and passengers in a ride. Another scheme is to allocate cost savings to participants proportional to their original travel distance. Although these simple schemes are easy to implement, there still lack a study that compare their effectiveness in ridesharing systems by applying different metaheuristic algorithms. In this paper, a hybrid meta-heuristic algorithm called hybrid Firefly-DE algorithm based on Differential Evolution and Firefly Algorithm will be adopted to match drivers and riders. We will compare three cost savings allocation schemes based on the numerical results. In our experiments, meta-heuristic algorithms are applied to find the matches to minimize the overall travel distance. The above schemes are then used to allocate cost savings among participants. The results indicate that the proportional cos savings allocation scheme is more effective than the other schemes to allocate cost savings equally between the drivers and the passengers.