A robust multi-objective routing problem for heavy-duty electric trucks with uncertain energy consumption

Abstract

In the Vehicle Routing Problem (VRP), utilizing Battery Electric Vehicles (BEVs) adds some challenges such as limited driving range and long recharging time to the problem. The energy consumption in BEVs plays an important role in determining their range coverage and the frequency of recharging. This paper introduces a new robust mathematical model for the EVRP with Heavy-duty Battery Electric Trucks to handle the energy consumption uncertainty in the short-haul delivery problems. Moreover, the on-time delivery factor that results in customers’ satisfaction is addressed by minimizing the delay and the earliness during distribution. Hence, the presented EVRP is a bi-objective problem that simultaneously minimizes transportation costs and maximizes customers’ satisfaction. To solve the problem, two metaheuristic algorithms are developed, including a Nondominated Sorting Genetic Algorithm II (NSGA-II), and Adaptive Large Neighborhood Search (ALNS) combined with multi-objective solution approaches (e.g., weighted-sum,ε-constraint, and hybrid methods). The results show that the ALNS algorithm combined with the weighted-sum method performed better than the other approaches.Moreover, a simulation study is conducted to analyze the robust solutions obtained for different levels of uncertainty to provide managerial insights for decision-makers.