A hybrid genetic algorithm for the vehicle relocation problem with ride-sharing options in one-way car-sharing systems

Weimin Tan, Min Kong, Muhammet Deveci, Weizhong Wang, Witold Pedrycz

Abstract: The imbalance of idle cars at different stations remains a critical challenge in one-way car-sharing systems. This paper proposes a novel mixed user-operator-based relocation strategy for this problem. In this one-way car-sharing system, ride-sharing service is allowed, and customers can share trips with others by a rental vehicle. Ride-sharing, as a supplement to operator-based relocation, can relieve the pressure of vehicle relocation, lowering the relocation fee and reducing the required fleet size. In this study, the operators must determine a mixed vehicle relocation scheme, including operator-based vehicle relocation routes and user-based ride-sharing matches. This problem can be defined as a bi-objective mixed-integer linear programming model to minimize total user fees and maximize system benefits. The linear weighting method can combine those two objectives into one objective. To solve this problem, we propose a meta-heuristic algorithm based on the state-of-the-art hybrid genetic search with adaptive diversity control (HGSADC). The computational results show that the proposed algorithm can produce high-quality solutions within acceptable computing time. We also show that the proposed mixed vehicle relocation strategy can benefit operators and users.