Abstract
A variety of hybrid genetic algorithms has been recently proposed to address the vehicle routing problem with time windows (VRPTW), a problem known to be NP-hard. However, very few genetic-based approaches exploit implicit knowledge provided by the structure of the intermediate solutions computed during the evolutionary process to explore the solution space. This paper presents a new hybrid genetic algorithm for VRPTW. It investigates the impact of using explicitly domain knowledge and a priori knowledge/characteristics about expected solutions during the recombination and mutation phases of the algorithm. Basic principles borrow from recent hybrid and standard genetic algorithms, and features of well-known heuristics to drive the search process. Designed to support time-constrained reasoning tasks, the procedure is intended to be conceptually simple, easy to implement and allow fast computation of near-optimal solution. A computational experiment has been conducted to compare the performance of the proposed algorithm with similar and standard techniques.
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© 1998 Springer-Verlag Berlin Heidelberg
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Berger, J., Salois, M., Begin, R. (1998). A hybrid genetic algorithm for the vehicle routing problem with time windows. In: Mercer, R.E., Neufeld, E. (eds) Advances in Artificial Intelligence. Canadian AI 1998. Lecture Notes in Computer Science, vol 1418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64575-6_44
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DOI: https://doi.org/10.1007/3-540-64575-6_44
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