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A memetic NSGA-II for the multi-objective flexible job shop scheduling problem with real-time energy tariffs

Author

Listed:
  • Sascha Christian Burmeister

    (Paderborn University)

  • Daniela Guericke

    (University of Twente)

  • Guido Schryen

    (Paderborn University)

Abstract

Rising costs for energy are increasingly becoming a vital factor for the production planning of manufacturing companies. Manufacturers face the challenge to react to dynamic energy prices and design energy cost efficient schedules in their production planning. In the literature, the energy cost-aware Flexible Job Shop Scheduling Problem addresses minimization of both makespan and energy costs. Recent studies provide multi-objective approaches to model the trade-off of minimizing makespan and energy costs. However, the literature is limited to coarse-grained time periods and does not consider dynamic tariffs where costs change at short intervals, so that production schedules may fall short on energy costs. We aim to close this research gap by considering frequently changing real-time energy tariffs. We propose a multi-objective memetic algorithm based on the non-dominated sorting genetic algorithm (NSGA-II) with both makespan and energy cost minimization as the objectives. We evaluate our approach by conducting computational experiments using prominent FJSP-benchmark instances from the literature, which we supplement with empiric dynamic energy prices. We show results on method performance and compare the memetic NSGA-II with the results of an exact state-of-the-art solver. To investigate the trade-off between a short makespan and low energy costs, we present solutions on the approximated Pareto front and discuss our results.

Suggested Citation

  • Sascha Christian Burmeister & Daniela Guericke & Guido Schryen, 2024. "A memetic NSGA-II for the multi-objective flexible job shop scheduling problem with real-time energy tariffs," Flexible Services and Manufacturing Journal, Springer, vol. 36(4), pages 1530-1570, December.
  • Handle: RePEc:spr:flsman:v:36:y:2024:i:4:d:10.1007_s10696-023-09517-7
    DOI: 10.1007/s10696-023-09517-7
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    References listed on IDEAS

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    1. Mehdi Golari & Neng Fan & Tongdan Jin, 2017. "Multistage Stochastic Optimization for Production-Inventory Planning with Intermittent Renewable Energy," Production and Operations Management, Production and Operations Management Society, vol. 26(3), pages 409-425, March.
    2. Masmoudi, Oussama & Delorme, Xavier & Gianessi, Paolo, 2019. "Job-shop scheduling problem with energy consideration," International Journal of Production Economics, Elsevier, vol. 216(C), pages 12-22.
    3. Konstantin Biel & Fu Zhao & John W. Sutherland & Christoph H. Glock, 2018. "Flow shop scheduling with grid-integrated onsite wind power using stochastic MILP," International Journal of Production Research, Taylor & Francis Journals, vol. 56(5), pages 2076-2098, March.
    4. Xuran Gong & Qianwang Deng & Guiliang Gong & Wei Liu & Qinghua Ren, 2018. "A memetic algorithm for multi-objective flexible job-shop problem with worker flexibility," International Journal of Production Research, Taylor & Francis Journals, vol. 56(7), pages 2506-2522, April.
    5. Biel, K. & Zhao, F. & Sutherland, J. & Glock, C. H., 2018. "Flow shop scheduling with grid-integrated onsite wind power using stochastic MILP," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 88879, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    6. Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.
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