Arc-Guided Evolutionary Algorithm for the Vehicle Routing Problem With Time Windows

• Published in: IEEE transactions on evolutionary computation Vol. 13; no. 3; pp. 624 - 647
• Main Authors: Repoussis, P.P., Tarantilis, C.D., Ioannou, G.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Combinatorial analysis; Computer science; control theory; systems; Evolution strategies; Evolutionary algorithms; Evolutionary computation; Exact sciences and technology; Firm modelling; Flows in networks. Combinatorial problems; Frequency diversity; Genetic mutations; guided local search; Heuristic; Intervals; Job shop scheduling; Large-scale systems; Learning and adaptive systems; Logistics; Operational research and scientific management; Operational research. Management science; Representations; Routing; Strategy; Studies; tabu search; Technology management; Trajectories; Transportation; vehicle routing; vehicle scheduling; Vehicles; Windows (intervals); vehicle scheduling; Recombination; Scalability; Evolutionary algorithm; Vehicle routing problem; Adaptability; Competitiveness; Very large databases; Combinatorial optimization; guided local search; Tabu search; Minimal distance; Time interval; Deterministic approach; Local search; Fleet; Evolution strategies; Routing; Scheduling; Search algorithm; Time window; Genetic algorithm; vehicle routing; Capacity constraint; Large scale
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2008.2011740

This paper presents an arc-guided evolutionary algorithm for solving the vehicle routing problem with time windows, which is a well-known combinatorial optimization problem that addresses the service of a set of customers using a homogeneous fleet of capacitated vehicles within fixed time intervals. The objective is to minimize the fleet size following routes of minimum distance. The proposed method evolves a population of mu individuals on the basis of an (mu + lambda) evolution strategy; at each generation, a new intermediate population of lambda individuals is generated, using a discrete arc-based representation combined with a binary vector of strategy parameters. Each offspring is produced via mutation out of arcs extracted from parent individuals. The selection of arcs is dictated by the strategy parameters and is based on their frequency of appearance and the diversity of the population. A multiparent recombination operator enables the self-adaptation of the strategy parameters, while each offspring is further improved via novel memory-based trajectory local search algorithms. For the selection of survivors, a deterministic scheme is followed. Experimental results on well-known large-scale benchmark datasets of the literature demonstrate the competitiveness of the proposed method.