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A genetic algorithm for energy-efficiency in job-shop scheduling

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Abstract

Many real-world scheduling problems are solved to obtain optimal solutions in term of processing time, cost, and quality as optimization objectives. Currently, energy-efficiency is also taken into consideration in these problems. However, this problem is NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the Job-shop Scheduling Problem in which machines can consume different amounts of energy to process tasks at different rates (speed scaling). This problem represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The evaluation section shows that a powerful commercial tool for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality.

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Authors and Affiliations

  1. Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain

    Miguel A. Salido, Joan Escamilla & Federico Barber

  2. Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain

    Adriana Giret

Authors
  1. Miguel A. Salido
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  2. Joan Escamilla
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  3. Adriana Giret
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  4. Federico Barber
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Correspondence to Miguel A. Salido.

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Salido, M.A., Escamilla, J., Giret, A. et al. A genetic algorithm for energy-efficiency in job-shop scheduling. Int J Adv Manuf Technol 85, 1303–1314 (2016). https://doi.org/10.1007/s00170-015-7987-0

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  • DOI: https://doi.org/10.1007/s00170-015-7987-0

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