Abstract
We propose an overall reconstruction of the traditional genetic algorithm method so that its inherent weaknesses such as slow convergence can be overcome. We explore a number of variations of crossover operators and of the genetic search scheme. The algorithm is also implemented as a partially parallel algorithm on a multi-processors workstation and is capable of handling a large class of real-life location problems. Hub location problems from airline networks and location-allocation problems from the oil industry have been solved successfully.
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Cheung, BS., Langevin, A. & Villeneuve, B. High performing evolutionary techniques for solving complex location problems in industrial system design. Journal of Intelligent Manufacturing 12, 455–466 (2001). https://doi.org/10.1023/A:1012248319870
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DOI: https://doi.org/10.1023/A:1012248319870