Multi-Compartment Electric Vehicle Routing Problem for Perishable Products

Abstract

The study first proposes a heterogeneous fleet, multi-compartment electric vehicle routing problem for perishable products (MCEVRP-PP). We capture a lot of practical demands and constraints of the MCEVRP-PP, such as multiple temperature zones, the hard time window, charging more than once during delivery, various power consumption per unit of refrigeration, etc. We model the MCEVRP-PP as a mixed integer program and aim to optimize the total cost including vehicle fixed cost, power cost, and cooling cost. A hybrid ant colony optimization (HACO) is developed to solve the problem. In the transfer rule, the time window is introduced to improve flexibility in route construction. According to the features of multi-compartment electric vehicles, the capacity constraint judgment algorithm is developed in route construction. Six local search strategies are designed with time windows, recharging stations, etc. Experiments based on various instances validate that HACO solves MCEVRP-PP more effectively than the ant colony optimization (ACO). Compared with fuel vehicles and single-compartment vehicles, electric vehicles and multi-compartment electric vehicles can save the total cost and mileage, and increase utilization of vehicles.

Keywords

• multiple compartments
• electric vehicle
• cold chain logistics
• heterogeneous fleet
• vehicle routing problem
• hybrid ant colony optimization