Routing a mixed fleet of electric and conventional vehicles

• Published in: European journal of operational research Vol. 245; no. 1; pp. 81 - 99
• Main Authors: Goeke, Dominik, Schneider, Michael
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16.08.2015; Elsevier Sequoia S.A
• Subjects: Electric vehicles; Energy consumption; Green logistics; Metaheuristic; Motor vehicle fleets; Optimization algorithms; Route optimization; Studies; Transportation problem (Operations research); Vehicle routing; Energy consumption; Electric vehicles; Metaheuristic; Vehicle routing; Green logistics
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2015.01.049

•E-VRPWTMF optimizes the routes of a mixed fleet of electric and diesel vehicles•A realistic energy consumption model with speed, gradient and cargo load is used•An efficient and effective Adaptive Large Neighborhood Search algorithm is proposed•We design new instances for the E-VRPTWMF•The impact of load distribution and different objective functions is investigated In this paper, we propose the Electric Vehicle Routing Problem with Time Windows and Mixed Fleet (E-VRPTWMF) to optimize the routing of a mixed fleet of electric commercial vehicles (ECVs) and conventional internal combustion commercial vehicles (ICCVs). Contrary to existing routing models for ECVs, which assume energy consumption to be a linear function of traveled distance, we utilize a realistic energy consumption model that incorporates speed, gradient and cargo load distribution. This is highly relevant in the context of ECVs because energy consumption determines the maximal driving range of ECVs and the recharging times at stations. To address the problem, we develop an Adaptive Large Neighborhood Search algorithm that is enhanced by a local search for intensification. In numerical studies on newly designed E-VRPTWMF test instances, we investigate the effect of considering the actual load distribution on the structure and quality of the generated solutions. Moreover, we study the influence of different objective functions on solution attributes and on the contribution of ECVs to the overall routing costs. Finally, we demonstrate the performance of the developed algorithm on benchmark instances of the related problems VRPTW and E-VRPTW.