Heuristic algorithms for the operator-based relocation problem in one-way electric carsharing systems

TL;DR

This paper develops and tests four heuristic algorithms for an electric vehicle relocation problem in one-way carsharing, aiming to maximize profit while reducing computational time.

Contribution

It introduces four novel heuristics for the E-VReP, improving solution efficiency and effectiveness over traditional MILP methods.

Findings

Heuristics outperform MILP in computational time.

Variable revenue requests impact heuristic performance.

Sensitivity analysis reveals key factors affecting profit maximization.

Abstract

This paper addresses an Electric Vehicle Relocation Problem (E-VReP), in one-way carsharing systems, based on operators who move through folding bicycles between a delivery request and one of pickup. In order to deal with its economical sustainability, a revenue associated with each relocation request satisfied and a cost due to each operator used are introduced. The new optimization objective maximizes the total profit. To overcome the drawback due to the high CPU time required by the Mixed Integer Linear Programming formulation of the E-VReP, four heuristics, also based on general properties of the feasible solutions, are designed. Their effectiveness is tested on two sets of realistic instances. In the first one, all the requests have the same revenue. In the second one, the revenue of each request has a variable component related to the user's rent-time and a fixed one related to the customer satisfaction. Finally, a sensitivity analysis is carried out on both the number of requests and the fixed revenue component.