Operating Expense Optimization for EVs in Multiple Depots and Charge Stations Environment Using Evolutionary Heuristic Method

In this paper, an operating cost optimization problem of Electric Vehicles (EVs) is studied in a large-scale logistics and transportation network. An extended EV operational model is proposed for a multiple depots and charge stations environment where practical constraints are included. In the proposed model, new practical mathematical schemes are proposed to describe the constraints. Then, a new Two-step Clustering Heuristic Optimization (TCHO) method is developed to minimize the total operating cost of the EV routes while satisfying all the constraints. In the first step, a novel Heuristic Edge Sharing Assigning Algorithm (HESAA) is designed to split the large scale logistic network into different clusters. In the second step, a new Shortest Path Heuristic (SPH) method is developed to minimize the total expense of the EV routes for each cluster. Furthermore, based on the TCHO, a novel Discrete Differential Evolution-TCHO (DDE-TCHO) is proposed to improve the performance on solving the problem. The effectiveness of the proposed models and methods is verified by comprehensive numerical simulations where the well-known vehicle routing problem benchmarks are applied.