Abstract
The job-shop scheduling problem (JSSP) is well known as one of the most difficult NP-hard combinatorial optimization problems. Genetic Algorithms (GAs) for solving the JSSP have been proposed, and they perform well compared with other approaches [1].
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References
Jain, A.S., and Meeran, S.: Deterministic job-shop scheduling: past, present and future, European Journal of Operational Research, vol.113, pp. 390–434, 1999.
Matsui, S., Watanabe, I., and Tokoro, K.: Real-coded parameter-free genetic algorithm for job-shop scheduling problems, Proc. Seventh Parallel Problem Solving from Nature — PPSN VII, pp. 800–810, 2002.
Sawai, H., Kizu, S.: Parameter-free genetic algorithm inspired by “disparity theory of evolution”, Proc. Seventh Parallel Problem Solving from Nature — PPSN V, pp. 702–711, 1998.
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Matsui, S., Watanabe, I., Tokoro, Ki. (2003). Performance Evaluation of a Parameter-Free Genetic Algorithm for Job-Shop Scheduling Problems. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_43
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DOI: https://doi.org/10.1007/3-540-45110-2_43
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