Abstract
This paper focuses on Multiobjective job-shop scheduling with Genetic Algorithms (GAs). The efficiency of G As for combinatorial problems largely depends on how the solutions are represented. We introduce a new kind of representation, which combines the advantages of two well-known encodings and allows the use of standard recombination operators without losing solution feasibility. Our solution alternation model efficiently guides the search towards promising regions of the search space. This approach tested in a single objective context aiming at the optimization of the makespan of classical benchmarks instances performs excellently compared to state-of-the-art GAs. The usefulness of the proposed method for Multiobjective scheduling is finally shown by tackling four job-shop scheduling problems introduced by Bagchi with the goal of minimizing the makespan, the mean flow-time and the mean tardiness simultaneously.
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Garen, J. (2004). A Genetic Algorithm for Tackling Multiobjective Job-shop Scheduling Problems. In: Gandibleux, X., Sevaux, M., Sörensen, K., T’kindt, V. (eds) Metaheuristics for Multiobjective Optimisation. Lecture Notes in Economics and Mathematical Systems, vol 535. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17144-4_8
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DOI: https://doi.org/10.1007/978-3-642-17144-4_8
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