A hybrid evolutionary algorithm for heterogeneous fleet vehicle routing problems with time windows

• Published in: Computers & operations research Vol. 64; pp. 11 - 27
• Main Authors: Koç, Çağrı, Bektaş, Tolga, Jabali, Ola, Laporte, Gilbert
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.12.2015; Pergamon Press Inc
• Subjects: Benchmarking; Computation; Cost engineering; Cost reduction; Distribution costs; Evolutionary algorithms; Genetic algorithm; Genetic algorithms; Heat treatment; Heterogeneous fleet; Heuristic; Motor vehicle fleets; Neighborhood search; Route selection; Studies; Time windows; Transportation problem (Operations research); Vehicle routing; Vehicles; Windows (intervals); Time windows; Heterogeneous fleet; Neighborhood search; Vehicle routing; Genetic algorithm
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2015.05.004

This paper presents a hybrid evolutionary algorithm (HEA) to solve heterogeneous fleet vehicle routing problems with time windows. There are two main types of such problems, namely the fleet size and mix vehicle routing problem with time windows (F) and the heterogeneous fixed fleet vehicle routing problem with time windows (H), where the latter, in contrast to the former, assumes a limited availability of vehicles. The main objective is to minimize the fixed vehicle cost and the distribution cost, where the latter can be defined with respect to en-route time (T) or distance (D). The proposed unified algorithm is able to solve the four variants of heterogeneous fleet routing problem, called FT, FD, HT and HD, where the last variant is new. The HEA successfully combines several metaheuristics and offers a number of new advanced efficient procedures tailored to handle the heterogeneous fleet dimension. Extensive computational experiments on benchmark instances have shown that the HEA is highly effective on FT, FD and HT. In particular, out of the 360 instances we obtained 75 new best solutions and matched 102 within reasonable computational times. New benchmark results on HD are also presented. •We develop a unified algorithm for four heterogeneous routing problems.•We introduce a new heterogeneous routing problem.•The algorithm combines two state-of-the-art metaheuristic concepts.•Out of the 360 instances we obtain 75 strictly new best solutions.