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Fuzzy job shop scheduling with lot-sizing

Author

Listed:
  • Sanja Petrovic
  • Carole Fayad
  • Dobrila Petrovic
  • Edmund Burke
  • Graham Kendall

Abstract

This paper deals with a problem of determining lot-sizes of jobs in a real-world job shop-scheduling in the presence of uncertainty. The main issue discussed in this paper is lot-sizing of jobs. A fuzzy rule-based system is developed which determines the size of lots using the following premise variables: size of the job, the static slack of the job, workload on the shop floor, and the priority of the job. Both premise and conclusion variables are modelled as linguistic variables represented by using fuzzy sets (apart from the priority of the job which is a crisp value). The determined lots’ sizes are input to a fuzzy multi-objective genetic algorithm for job shop scheduling. Imprecise jobs’ processing times and due dates are modelled by using fuzzy sets. The objectives that are used to measure the quality of the generated schedules are average weighted tardiness of jobs, the number of tardy jobs, the total setup time, the total idle time of machines and the total flow time of jobs. The developed algorithm is analysed on real-world data obtained from a printing company. Copyright Springer Science+Business Media, LLC 2008

Suggested Citation

  • Sanja Petrovic & Carole Fayad & Dobrila Petrovic & Edmund Burke & Graham Kendall, 2008. "Fuzzy job shop scheduling with lot-sizing," Annals of Operations Research, Springer, vol. 159(1), pages 275-292, March.
    • Handle: RePEc:spr:annopr:v:159:y:2008:i:1:p:275-292:10.1007/s10479-007-0287-9
    DOI: 10.1007/s10479-007-0287-9
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    References listed on IDEAS

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    Cited by:

    1. Carlos Mencía & María Sierra & Ramiro Varela, 2013. "Depth-first heuristic search for the job shop scheduling problem," Annals of Operations Research, Springer, vol. 206(1), pages 265-296, July.
    2. Georgiadis, Patroklos & Michaloudis, Charalampos, 2012. "Real-time production planning and control system for job-shop manufacturing: A system dynamics analysis," European Journal of Operational Research, Elsevier, vol. 216(1), pages 94-104.
    3. Zeiträg, Yannik & Figueira, José Rui & Pereira, Miguel Alves, 2024. "A web-based interactive decision support system for a multi-objective lot-sizing and production scheduling model," International Journal of Production Economics, Elsevier, vol. 271(C).
    4. J. Behnamian, 2016. "Survey on fuzzy shop scheduling," Fuzzy Optimization and Decision Making, Springer, vol. 15(3), pages 331-366, September.
    5. Gabriel Mauricio Zambrano-Rey & Eliana María González-Neira & Gabriel Fernando Forero-Ortiz & María José Ocampo-Monsalve & Andrea Rivera-Torres, 2024. "Minimizing the expected maximum lateness for a job shop subject to stochastic machine breakdowns," Annals of Operations Research, Springer, vol. 338(1), pages 801-833, July.
    6. Xinchang Hao & Mitsuo Gen & Lin Lin & Gursel A. Suer, 2017. "Effective multiobjective EDA for bi-criteria stochastic job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 28(3), pages 833-845, March.
    7. J A Vázquez-Rodríguez & G Ochoa, 2011. "On the automatic discovery of variants of the NEH procedure for flow shop scheduling using genetic programming," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(2), pages 381-396, February.

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