Skip to main content
Springer Nature Link
Log in

NSGA-II for Solving a Multi-objective, Sustainable and Flexible Job Shop Scheduling Problem

  • Conference paper
  • First Online:
Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures (APMS 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 691))

  • 1559 Accesses

Abstract

Recently, sustainable scheduling has emerged to make the trade-off between economic, environmental and social factors. While classical scheduling research focuses on economic and environmental indicators such as makespan and energy consumption, modern human-centred manufacturing systems consider additional important factors such as workers’ well-being and ergonomic risks. This study proposes a multi-objective mathematical model that jointly optimizes the makespan, the total energy consumption, and the OCRA (Occupational Repetitive Actions) index (a measure of ergonomic risk) in the case of a flexible job shop. The model has the originality of also taking into account the travel times of operators and products between machines. We use the NSGA-II method to solve it. The first results allow us to analyze the mutual influences of the criteria and demonstrate the advantage of such a method to obtain a good compromise between the three objectives in a reasonable resolution time.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Xu, X., Lu, Y., Vogel-Heuser, B., Wang, L.: Industry 4.0 and industry 5.0-inception, conception and perception. J. Manuf. Syst. (2021)

    Google Scholar 

  2. Parente, M., Figueira, G., Amorim, P., Marques, A.: Production scheduling in the context of industry 4.0: review and trends. Int. J. Prod. Res. 58, 5401–5431 (2020)

    Google Scholar 

  3. Occhipinti, E.: OCRA: a concise index for the assessment of exposure to repetitive movements of the upper limbs. Ergonomics 41, 1290–1311 (1998)

    Google Scholar 

  4. Gong, G., Deng, Q., Gong, X., Liu, W., Ren, Q.: A new double flexible job-shop scheduling problem integrating processing time, green production, and human factor indicators. J. Clean. Prod. 174, 560–576 (2018)

    Article  Google Scholar 

  5. Hongyu, L., Xiuli, W.: A survival duration-guided NSGA-III for sustainable flexible job shop scheduling problem considering dual resources. IET Collaborative Intell. Manuf. 3, 119–130 (2021)

    Article  Google Scholar 

  6. Coca, G., Castrillón, O., Ruiz, S., Mateo-Sanz, J., Jiménez, L.: Sustainable evaluation of environmental and occupational risks scheduling flexible job shop manufacturing systems. J. Cleaner Prod. 209, 146–168 (2019)

    Article  Google Scholar 

  7. Homayouni, S.M., Fontes, D.B.M.M.: Production and transport scheduling in flexible job shop manufacturing systems. J. Global Optim. 79(2), 463–502 (2021). https://doi.org/10.1007/s10898-021-00992-6

    Article  MathSciNet  Google Scholar 

  8. Sanogo, K., Mekhalef Benhafssa, A., Sahnoun, M., Bettayeb, B., Abderrahim, M., Bekrar, A.: A multi-agent system simulation based approach for collision avoidance in integrated job-shop scheduling problem with transportation tasks. J. Manuf. Syst. 68, 209–226 (2023)

    Google Scholar 

  9. Tan, W., Yuan, X., Wang, J., Zhang, X.: A fatigue-conscious dual resource constrained flexible job shop scheduling problem by enhanced NSGA-II: an application from casting workshop. Comput. Ind. Eng. 160, 107557 (2021)

    Article  Google Scholar 

  10. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)

    Article  Google Scholar 

  11. Grabowska, S., Saniuk, S., Gajdzik, B.: Industry 5.0: improving humanization and sustainability of industry 4.0. Scientometrics 127, 3117–3144 (2022)

    Article  Google Scholar 

  12. Xiong, H., Shi, S., Ren, D., Hu, J.: A survey of job shop scheduling problem: the types and models. Comput. Oper. Res. 142, 105731 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  13. Destouet, C., Tlahig, H., Bettayeb, B., Mazari, B.: Flexible job shop scheduling problem under industry 5.0: a survey on human reintegration, environmental consideration and resilience improvement. J. Manuf. Syst. 67, 155–173 (2023)

    Article  Google Scholar 

  14. Luo, S., Zhang, L., Fan, Y.: Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. J. Clean. Prod. 234, 1365–1384 (2019)

    Article  Google Scholar 

  15. Sano, M., Nagao, M., Morinaga, Y.: Balancing setup workers load of flexible job shop scheduling using hybrid genetic algorithm with tabu search strategy. Int. J. Decis. Support Syst. 2, 71–90 (2016)

    Article  Google Scholar 

  16. Jaber, M., Neumann, W.: Modelling worker fatigue and recovery in dual-resource constrained systems. Comput. Ind. Eng. 59, 75–84 (2010)

    Article  Google Scholar 

  17. Sun, X., Guo, S., Guo, J., Du, B.: A hybrid multi-objective evolutionary algorithm with heuristic adjustment strategies and variable neighborhood search for flexible job-shop scheduling problem considering flexible rest time. IEEE Access 7, 157003–157018 (2019)

    Article  Google Scholar 

  18. Xu, S., Hall, N.: Fatigue, personnel scheduling and operations: review and research opportunities. Eur. J. Oper. Res. 295, 807–822 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  19. Gong, X., De Pessemier, T., Martens, L., Joseph, W.: Energy- and labor-aware flexible job shop scheduling under dynamic electricity pricing: a many-objective optimization investigation. J. Cleaner Prod. 209, 1078–1094 (2019)

    Article  Google Scholar 

  20. Chiandussi, G., Codegone, M., Ferrero, S., Varesio, F.: Comparison of multi-objective optimization methodologies for engineering applications. Comput. Math. Appl. 63, 912–942 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  21. Zhang, G., Hu, Y., Sun, J., Zhang, W.: An improved genetic algorithm for the flexible job shop scheduling problem with multiple time constraints. Swarm Evol. Comput. 54, 100664 (2020)

    Article  Google Scholar 

  22. Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, PART I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18, 577–601 (2014)

    Article  Google Scholar 

  23. Liang, X., Chen, J., Gu, X., Huang, M.: Improved adaptive non-dominated sorting genetic algorithm with elite strategy for solving multi-objective flexible job-shop scheduling problem. IEEE Access 9, 106352–106362 (2021)

    Article  Google Scholar 

  24. Luo, Q., Deng, Q., Xie, G., Gong, G.: A pareto-based two-stage evolutionary algorithm for flexible job shop scheduling problem with worker cooperation flexibility. Rob. Comput.-Integr. Manuf. 82, 102534 (2023)

    Article  Google Scholar 

  25. Wang, H., Cheng, J., Liu, C., Zhang, Y., Hu, S., Chen, L.: Multi-objective reinforcement learning framework for dynamic flexible job shop scheduling problem with uncertain events. Appl. Soft Comput. 131, 109717 (2022)

    Article  Google Scholar 

  26. EN 1005–1+A1, “Cen/tc 122”. Ergonomie (2008)

    Google Scholar 

  27. I. 11228–3:2006, “Iso/tc 159/sc 3,”. Ergonomie (2007)

    Google Scholar 

  28. Akyol, S.D., Baykasoğlu, A.: ErgoALWABP: a multiple-rule based constructive randomized search algorithm for solving assembly line worker assignment and balancing problem under ergonomic risk factors. J. Intell. Manuf. 30, 291–302 (2019)

    Article  Google Scholar 

  29. Song, W., Zhang, C., Lin, W., Shao, X.: Flexible job-shop scheduling problem with maintenance activities considering energy consumption. Appl. Mech. Mater. 521, 707–713 (2014)

    Article  Google Scholar 

  30. Yang, X., Zeng, Z., Wang, R., Sun, X.: Bi-objective flexible job-shop scheduling problem considering energy consumption under stochastic processing times. PLOS ONE 11, e0167427 (2016)

    Article  Google Scholar 

  31. Amjad, M., et al.: Recent research trends in genetic algorithm based flexible job shop scheduling problems. Mathe. Probl. Eng. 2018, 1–32 (2018)

    Article  Google Scholar 

  32. Kacem, I., Hammadi, S., Borne, P.: Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Sched. Control Intell. Forecast. Fault Diagn. 60, 245–276 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CESI LINEACT, 15C Av. Albert Einstein, Villeurbanne, France

    Candice Destouet & Houda Tlahig

  2. CESI LINEACT, 8 Bd. Louis XIV, Lille, France

    Belgacem Bettayeb

  3. CESI LINEACT, 80 Av. Edmund Halley Rouen, Saint-Etienne-du-Rouvray, France

    Bélahcène Mazari

Authors
  1. Candice Destouet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Houda Tlahig
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Belgacem Bettayeb
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bélahcène Mazari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Candice Destouet .

Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Erlend Alfnes

  2. Norwegian University of Science and Technology, Trondheim, Norway

    Anita Romsdal

  3. Norwegian University of Science and Technology, Trondheim, Norway

    Jan Ola Strandhagen

  4. ZF Friedrichshafen AG, Friedrichshafen, Germany

    Gregor von Cieminski

  5. Tecnológico de Monterrey, Mexico City, Mexico

    David Romero

Rights and permissions

Reprints and permissions

Copyright information

© 2023 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Destouet, C., Tlahig, H., Bettayeb, B., Mazari, B. (2023). NSGA-II for Solving a Multi-objective, Sustainable and Flexible Job Shop Scheduling Problem. In: Alfnes, E., Romsdal, A., Strandhagen, J.O., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. APMS 2023. IFIP Advances in Information and Communication Technology, vol 691. Springer, Cham. https://doi.org/10.1007/978-3-031-43670-3_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43670-3_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43669-7

  • Online ISBN: 978-3-031-43670-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships