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Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system

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  • Vinod, V.
  • Sridharan, R.

Abstract

This paper presents the salient aspects of a simulation study conducted to investigate the interaction between due-date assignment methods and scheduling rules in a typical dynamic job shop production system. The due-date assignment methods investigated are dynamic processing plus waiting time, total work content, dynamic total work content and random work content method. Seven scheduling rules are incorporated in the simulation model for the scheduling of jobs on machines. The performance of the system is evaluated using various measures based on flow time and tardiness of jobs. Simulation experiments are carried out for the different scenarios that arise out of the combination of due-date assignment methods and scheduling rules. The simulation results are subjected to statistical analysis. It is found that dynamic due-date assignment methods provide better performance. Regression-based metamodels are developed using the simulation results. These metamodels have been found to be valid analytical models of the simulation model.

Suggested Citation

  • Vinod, V. & Sridharan, R., 2011. "Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system," International Journal of Production Economics, Elsevier, vol. 129(1), pages 127-146, January.
    • Handle: RePEc:eee:proeco:v:129:y:2011:i:1:p:127-146
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    Cited by:

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    2. Chuang Wang & Pingyu Jiang, 2019. "Deep neural networks based order completion time prediction by using real-time job shop RFID data," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1303-1318, March.
    3. Jürgen Strohhecker & Michael Hamann & Jörn-Henrik Thun, 2016. "Loading and sequencing heuristics for job scheduling on two unrelated parallel machines with long, sequence-dependent set-up times," International Journal of Production Research, Taylor & Francis Journals, vol. 54(22), pages 6747-6767, November.
    4. Hankun Zhang & Borut Buchmeister & Xueyan Li & Robert Ojstersek, 2021. "Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment," Mathematics, MDPI, vol. 9(8), pages 1-22, April.
    5. Mallor, Fermin & Guardiola, Ivan G., 2014. "The Weibull scheduling index for client driven manufacturing processes," International Journal of Production Economics, Elsevier, vol. 150(C), pages 225-238.
    6. Al-Hinai, Nasr & ElMekkawy, T.Y., 2011. "Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm," International Journal of Production Economics, Elsevier, vol. 132(2), pages 279-291, August.
    7. Aijun Liu & John Fowler & Michele Pfund, 2016. "Dynamic co-ordinated scheduling in the supply chain considering flexible routes," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 322-335, January.
    8. A. S. Xanthopoulos & D. E. Koulouriotis, 2018. "Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 69-91, January.
    9. Cenk Sahin & Melek Demirtas & Rizvan Erol & Adil Baykasoğlu & Vahit Kaplanoğlu, 2017. "A multi-agent based approach to dynamic scheduling with flexible processing capabilities," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1827-1845, December.
    10. Adil Baykasoğlu & Fatma S. Karaslan, 2017. "Solving comprehensive dynamic job shop scheduling problem by using a GRASP-based approach," International Journal of Production Research, Taylor & Francis Journals, vol. 55(11), pages 3308-3325, June.
    11. Schaller, Jeffrey & Valente, Jorge M.S., 2020. "Minimizing total earliness and tardiness in a nowait flow shop," International Journal of Production Economics, Elsevier, vol. 224(C).
    12. Yao, Shiqing & Jiang, Zhibin & Li, Na & Zhang, Huai & Geng, Na, 2011. "A multi-objective dynamic scheduling approach using multiple attribute decision making in semiconductor manufacturing," International Journal of Production Economics, Elsevier, vol. 130(1), pages 125-133, March.
    13. Tanja Mlinar & Philippe Chevalier, 2016. "Pooling heterogeneous products for manufacturing environments," 4OR, Springer, vol. 14(2), pages 173-200, June.
    14. Saeed Yaghoubi, 2015. "Due-date assignment for multi-server multi-stage assembly systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(7), pages 1246-1256, May.
    15. Xiong, Hegen & Fan, Huali & Jiang, Guozhang & Li, Gongfa, 2017. "A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints," European Journal of Operational Research, Elsevier, vol. 257(1), pages 13-24.
    16. Hankun Zhang & Borut Buchmeister & Xueyan Li & Robert Ojstersek, 2023. "An Efficient Metaheuristic Algorithm for Job Shop Scheduling in a Dynamic Environment," Mathematics, MDPI, vol. 11(10), pages 1-24, May.
    17. Yu-Cheng Wang & Horng-Ren Tsai & Toly Chen, 2021. "A Selectively Fuzzified Back Propagation Network Approach for Precisely Estimating the Cycle Time Range in Wafer Fabrication," Mathematics, MDPI, vol. 9(12), pages 1-18, June.
    18. MLINAR, Tanja B. & CHEVALIER, Philippe, 2013. "Pooling in manufacturing: do opposites attract?," LIDAM Discussion Papers CORE 2013040, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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