The electric vehicle routing problem with energy consumption uncertainty

• Published in: Transportation research. Part B: methodological Vol. 126; pp. 225 - 255
• Main Authors: Pelletier, Samuel, Jabali, Ola, Laporte, Gilbert
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.08.2019; Elsevier Science Ltd; Elsevier
• Subjects: City logistics; Computer applications; Computer Science; Driver behavior; Drivers; Electric vehicle routing; Electric vehicles; Energy consumption; Heuristic methods; Metaheuristics; Minimum cost; Mixed integer; Optimization; Optimization techniques; Parameter identification; Quality assessment; Robust optimization; Robustness (mathematics); Route planning; Routing; Uncertainty; Urban areas; Vehicle routing; Vehicles; Weather; Electric vehicle routing; Robust optimization; Metaheuristics; City logistics
• ISSN: 0191-2615; 1879-2367
• DOI: 10.1016/j.trb.2019.06.006

•We introduce and solve the electric vehicle routing problem with energy consumption uncertainty.•We formulate the problem as a robust mixed integer linear program.•We solve small instances to optimality using robust optimization techniques.•We develop a two-phase heuristic method based on large neighbourhood search to solve larger instances.•We perform an extensive computational study. Compared with conventional freight vehicles, electric freight vehicles create less local pollution and are thus generally perceived as a more sustainable means of goods distribution. In urban areas, such vehicles must often perform the entirety of their delivery routes without recharging. However, their energy consumption is subject to a fair amount of uncertainty, which is due to exogenous factors such as the weather and road conditions, endogenous factors such as driver behaviour, and several energy consumption parameters that are difficult to measure precisely. Hence we propose a robust optimization framework to take into account these energy consumption uncertainties in the context of an electric vehicle routing problem. The objective is to determine minimum cost delivery routes capable of providing strong guarantees that a given vehicle will not run out of charge during its route. We formulate the problem as a robust mixed integer linear program and solve small instances to optimality using robust optimization techniques. Furthermore, we develop a two-phase heuristic method based on large neighbourhood search to solve larger instances of the problem, and we conduct several numerical tests to assess the quality of the methodology. The computational experiments illustrate the trade-off between cost and risk, and demonstrate the influence of several parameters on best found solutions. Furthermore, our heuristic identifies 42 new best solutions when tested on instances of the closely related robust capacitated vehicle routing problem.