Solving the Electric Share-A-Ride Problem Using a Hybrid Variable Neighborhood Search Algorithm

Abstract

This paper extends the share-a-ride problem (SARP) by incorporating electric vehicles (EVs) to reduce greenhouse gas (GHG) emissions, thus addressing environmental concerns. We introduce this new extension as the electric share-a-ride problem (E-SARP). We aim to generate E-SARP routing plans where EVs serve all passenger and parcel requests while visiting charging stations (CSs) as necessary for recharging. The objective is to maximize total profit from fulfilling passenger and parcel requests. We develop a mixed-integer programming (MIP) model and propose a hybrid algorithm based on the variable neighborhood search (VNS) framework, integrated with a simulated annealing (SA) acceptance criterion (HVNS). The MIP model provides optimal solutions for small E-SARP instances using the CPLEX solver, while the HVNS algorithm is designed to solve larger E-SARP instances. Numerical experiments are conducted to assess the performance of the proposed HVNS and to provide managerial insights, demonstrating that the use of EVs can effectively address environmental concerns without significantly compromising the profitability of the transportation network.