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Real-time production planning and control system for job-shop manufacturing: A system dynamics analysis

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  • Georgiadis, Patroklos
  • Michaloudis, Charalampos

Abstract

Much attention has been paid to production planning and control (PPC) in job-shop manufacturing systems. However, there is a remaining gap between theory and practice, in the ability of PPC systems to capture the dynamic disturbances in manufacturing process. Since most job-shop manufacturing systems operate in a stochastic environment, the need for sound PPC systems has emerged, to identify the discrepancy between planned and actual activities in real-time and also to provide corrective measures. By integrating production ordering and batch sizing control mechanisms into a dynamic model, we propose a comprehensive real-time PPC system for arbitrary capacitated job-shop manufacturing. We adopt a system dynamics (SD) approach which is proved to be appropriate for studying the dynamic behavior of complex manufacturing systems. We study the system’s response, under different arrival patterns for customer orders and the existence of various types real-time events related to customer orders and machine failures. We determine the near-optimal values of control variables, which improve the shop performance in terms of average backlogged orders, work in process inventories and tardy jobs. The results of extensive numerical investigation are statistically examined by using analysis of variance (ANOVA). The examination reveals an insensitivity of near-optimal values to real-time events and to arrival pattern and variability of customer orders. In addition, it reveals a positive impact of the proposed real-time PPC system on the shop performance. The efficiency of PPC system is further examined by implementing data from a real-world manufacturer.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:216:y:2012:i:1:p:94-104
    DOI: 10.1016/j.ejor.2011.07.022
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    References listed on IDEAS

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    2. Shen, Liji & Buscher, Udo, 2012. "Solving the serial batching problem in job shop manufacturing systems," European Journal of Operational Research, Elsevier, vol. 221(1), pages 14-26.
    3. Spiegler, Virginia L.M. & Naim, Mohamed M., 2017. "Investigating sustained oscillations in nonlinear production and inventory control models," European Journal of Operational Research, Elsevier, vol. 261(2), pages 572-583.
    4. Pérez-Pérez, Juan Fernando & Parra, Juan Felipe & Serrano-García, Jakeline, 2021. "A system dynamics model: Transition to sustainable processes," Technology in Society, Elsevier, vol. 65(C).
    5. Katsoras, Efthymios & Georgiadis, Patroklos, 2022. "An integrated System Dynamics model for Closed Loop Supply Chains under disaster effects: The case of COVID-19," International Journal of Production Economics, Elsevier, vol. 253(C).
    6. Jain, S. & Foley, W.J., 2016. "Dispatching strategies for managing uncertainties in automated manufacturing systems," European Journal of Operational Research, Elsevier, vol. 248(1), pages 328-341.
    7. Kazantsev, Nikolai & Petrovskyi, Oleksii & Müller, Julian M., 2023. "From supply chains towards manufacturing ecosystems: A system dynamics model," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    8. Efthymios Katsoras & Patroklos Georgiadis, 2022. "A Dynamic Analysis for Mitigating Disaster Effects in Closed Loop Supply Chains," Sustainability, MDPI, vol. 14(9), pages 1-20, April.

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