A Multi-stage Optimisation Approach to Design Relocation Strategies in One-way Car-sharing Systems with Stackable Cars

TL;DR

This paper introduces a scalable, multi-stage decision support system for vehicle relocation in one-way car-sharing, incorporating stackable vehicles and demand uncertainty, outperforming benchmarks in efficiency and service quality.

Contribution

It presents a novel multi-stage, modular approach for vehicle relocation that includes stackable vehicles and handles demand uncertainty effectively.

Findings

The approach outperforms benchmark schemes in vehicle utilization and service quality.

Stackable vehicles achieve near-autonomous car relocation performance with minimal workforce.

The system is scalable and adaptable to different relocation schemes and vehicle types.

Abstract

One of the main operational challenges faced by the operators of one-way car-sharing systems is to ensure vehicle availability across the regions of the service areas with uneven patterns of rental requests. Fleet balancing strategies are required to maximise the demand served while minimising the relocation costs. However, the design of optimal relocation policies is a complex problem, and global optimisation solutions are often limited to very small network sizes for computational reasons. In this work, we propose a multi-stage decision support system for vehicle relocation that decomposes the general relocation problem into three independent decision stages to allow scalable solutions. Furthermore, we adopt a rolling horizon control strategy to cope with demand uncertainty. Our approach is highly modular and flexible, and we leverage it to design user-based, operator-based and robotic relocation schemes. Besides, we formulate the relocation problem considering both conventional cars and a new class of compact stackable vehicles that can be driven in a road train. We compare the proposed relocation schemes with two recognised benchmarks using a large data set of taxi trips in New York. Our results show that our approach is scalable and outperforms the benchmark schemes in terms of quality of service, vehicle utilisation and relocation efficiency. Furthermore, we find that stackable vehicles can achieve a relocation performance close to that of autonomous cars, even with a small workforce of relocators.