An overnight relocation problem for one-way carsharing systems considering employment planning, return restrictions, and ride sharing of temporary workers

This paper studies a vehicle relocation problem for one-way carsharing systems in which vehicles are relocated by temporary workers during the night. The goal is to develop a method for constructing employment plans and work schedules simultaneously to minimize the total relocation cost. An integer programming model is proposed, which incorporates employment planning, return restrictions and ride sharing of temporary workers. An iterative optimization approach is developed to address large-scale rebalancing problems. The unbalanced stations are clustered based on their relocation demands. Considering a new employment limit in each rebalancing model, the optimization procedure iteratively solves a set of cluster relocation problems and an inter-cluster relocation problem until a convergent solution is obtained. Numerical experiments based on data from a carsharing company in Chengdu reveal two interesting findings: (1) Employing temporary workers from understocked stations costs less than that from overstocked stations; (2) Using a time-based salary contributes to 49.1% reduction in the working hours and 33.8% in the relocation costs.

Automated summary

This paper focuses on the vehicle relocation problem in one-way carsharing systems and proposes an integer programming model and an iterative optimization approach for large-scale rebalancing. The experimental results reveal that employing temporary workers from understocked stations is cost-effective, and using a time-based salary can significantly reduce working hours and relocation costs.