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- W2602388575 abstract "Open-shop scheduling problems are highly complex (NP-Hard), where solution is represented by a permutation of machines order that each job must follow to be processed, and for each job, it can have a different machines order. The main goal in these problems is to reduce consumption resources, and at same time, improving several performance criteria. In this work we propose a computational strategy based on genetic algorithms to solve open-shop problems, focused both potential genetic operators for permutations without repetition that may contribute to better solutions, as well selection mechanisms to not quickly converge to optimal local solutions. Furthermore, we propose a simple heuristic to improve the performance of each individual on initial population of genetic algorithm. Among the results, we make a results comparison obtained by our strategy compared to Taillard reference problems. Finally, we make an analysis of the positive impact from the heuristic proposed to guide the strategy." @default.
- W2602388575 created "2017-04-07" @default.
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- W2602388575 date "2016-11-01" @default.
- W2602388575 modified "2023-09-26" @default.
- W2602388575 title "Production programming model in open-shop systems using genetic algorithms approach" @default.
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- W2602388575 doi "https://doi.org/10.1109/la-cci.2016.7885743" @default.
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