Multi-objective heterogeneous vehicle routing and scheduling problem with energy minimizing

• Published in: Swarm and evolutionary computation 2019-02, Vol.44, p.728-747
• Main Authors: Ghannadpour, Seyed Farid, Zarrabi, Abdolhadi
• Format: Article
• Language: English
• Subjects: Customers' priority; Energy consumption; Evolutionary algorithm; Multi-objective; Private and rental fleet; Vehicle routing problem
• ISSN: 2210-6502
• DOI: 10.1016/j.swevo.2018.08.012

A new model and solution for the multi-objective heterogeneous vehicle routing and scheduling problem, with energy minimizing, is presented in this paper. The concept of heterogeneities is concerned with the ownership of fleets. Ownership heterogeneities occur when the private fleet is not sufficient and the company has to rent some vehicles from external carriers to complete the shipments. A new mathematical formulation for vehicle routing problem with time windows (VRPTW) is also presented using the proposed concept of heterogeneities. Moreover, unlike prior attempts to minimize cost by minimizing overall traveling distance, the model also incorporates energy minimizing which meets the latest requirements of green logistics. This paper considers the customers' priority for servicing as well. The proposed model is interpreted as multi-objective optimization and used in two scenarios where, in the first scenario (I), the energy consumed and the total number of vehicles are minimized and the total satisfaction rate of customers is maximized. In the second scenario (II) the distance traveled by the vehicles, the total number of rental vehicles and the fuel consumed by the private vehicles are minimized and the total satisfaction is maximized. A new solution based on an evolutionary algorithm is proposed and its performance on several completely random instances is compared to the non-dominated sorting genetic algorithm II (NSGA II) and CPLEX Solver. The efficiency and effectiveness of the proposed approach is further demonstrated through several computational experiments. •Considering the ownership of vehicles and the available fleet as heterogeneous VRPTW (HVRPTW).•Considering a reduction in fuel consumption in modeling of the proposed HVRPTW.•Considering the customers' priority which are highly relevant to the customers’ satisfaction level.•Using a direct interpretation of the proposed model as a multi-objective problem.•The computational experiments on data sets illustrate the efficiency and effectiveness of the proposed approach.