Affiliations: Sector of Industrial Management and Operational Research, School of Mechanical Engineering, National Technical University of Athens, Athens, Greece
Correspondence:
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Corresponding author. Grigorios D. Konstantakopoulos, Sector of Industrial Management and Operational Research, School of Mechanical Engineering, National Technical University of Athens, Athens, Greece. E-mail: gkonpoulos@mail.ntua.gr.
Abstract: Routing of vehicles and scheduling of deliveries play a crucial role in logistics operations as they affect both the distribution cost and customer satisfaction. That is why researchers have intensively studied this problem in conjunction with the multiple variables and constraints involved in the logistics operations. In this paper, the cases of time windows and simultaneous pickups and deliveries, where goods are simultaneously delivered and collected from customers within a predetermined time slot, are studied. The objective of our research is to create efficient routes that minimize both the number of vehicles and the total distance travelled, as both of them affect the total distribution cost. Considering various plans of routes that are differentiated by the number of routes and the sequence of visitations, can be beneficial for decision-makers, since they have the opportunity to select the plan that better fits their needs. Therefore, in this paper we develop a multiobjective evolutionary algorithm (MOEA) that integrates an improved construction algorithm and a new crossover operator for efficient distribution services. Through the proposed MOEA a set of solutions (route plans), known as Pareto-optimal, is obtained, while single biased solutions are avoided. The proposed algorithm is tested in two well-known datasets in order to evaluate the algorithm’s efficiency. The results indicate that the algorithm’s solutions have small deviation from the best-published and some non-dominated solutions are also obtained.
Keywords: Vehicle routing problem, time windows, simultaneous pickups and deliveries, multiobjective optimization, evolutionary algorithm, logistics
