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A heuristic algorithm for master production scheduling problem with controllable processing times and scenario-based demands

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  • Fateme Akhoondi
  • M.M. Lotfi

Abstract

Master production scheduling (MPS) is widely used by manufacturing industries in order to handle the production scheduling decisions in the production planning hierarchy. The classical approach to MPS assumes infinite capacity, fixed (i.e. non-controllable) processing times and a single pre-determined scenario for the demand forecasts. However, the deterministic optimisation approaches are sometimes not suitable for addressing the real-world problems with high uncertainty and flexibility. Accordingly, in this paper, we propose a new practical model for designing an optimal MPS for the environments in which processing times may be controllable by allocating resources such as facilities, energy or manpower. Due to the NP-hardness of our model, an efficient heuristic algorithm using local search technique and theory of constraints is developed and analysed. The computational results especially for large-sized test problems show that the average optimality gap of proposed algorithm is four times lower than that of exact solution using GAMS while it consumes also significantly smaller run times. Also, the analysis of computational results confirms that considering the controllable processing times may improve the solution space and help to more efficiently utilise the available resources. According to the model structure and performance of the algorithm, it may be proposed for solving large and complex real-world problems particularly the machining and steel industries.

Suggested Citation

  • Fateme Akhoondi & M.M. Lotfi, 2016. "A heuristic algorithm for master production scheduling problem with controllable processing times and scenario-based demands," International Journal of Production Research, Taylor & Francis Journals, vol. 54(12), pages 3659-3676, June.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:12:p:3659-3676
    DOI: 10.1080/00207543.2015.1125032
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    References listed on IDEAS

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    1. Chu, Sydney C. K., 1995. "A mathematical programming approach towards optimized master production scheduling," International Journal of Production Economics, Elsevier, vol. 38(2-3), pages 269-279, March.
    2. Vargas, Vicente & Metters, Richard, 2011. "A master production scheduling procedure for stochastic demand and rolling planning horizons," International Journal of Production Economics, Elsevier, vol. 132(2), pages 296-302, August.
    3. Koca, Esra & Yaman, Hande & Selim Aktürk, M., 2015. "Stochastic lot sizing problem with controllable processing times," Omega, Elsevier, vol. 53(C), pages 1-10.
    4. Körpeoglu, Ersin & Yaman, Hande & Selim Aktürk, M., 2011. "A multi-stage stochastic programming approach in master production scheduling," European Journal of Operational Research, Elsevier, vol. 213(1), pages 166-179, August.
    5. Kailiang Xu & Zuren Feng & Liangjun Ke, 2010. "A branch and bound algorithm for scheduling jobs with controllable processing times on a single machine to meet due dates," Annals of Operations Research, Springer, vol. 181(1), pages 303-324, December.
    6. Gahm, Christian & Dünnwald, Bastian & Sahamie, Ramin, 2014. "A multi-criteria master production scheduling approach for special purpose machinery," International Journal of Production Economics, Elsevier, vol. 149(C), pages 89-101.
    7. Kayan, Rabia K. & Akturk, M. Selim, 2005. "A new bounding mechanism for the CNC machine scheduling problems with controllable processing times," European Journal of Operational Research, Elsevier, vol. 167(3), pages 624-643, December.
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    1. Habib Zare & Mahyar Kamali Saraji & Madjid Tavana & Dalia Streimikiene & Fausto Cavallaro, 2021. "An Integrated Fuzzy Goal Programming—Theory of Constraints Model for Production Planning and Optimization," Sustainability, MDPI, vol. 13(22), pages 1-15, November.
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    3. Christian Scheller & Kerstin Schmidt & Thomas Stefan Spengler, 2021. "Decentralized master production and recycling scheduling of lithium-ion batteries: a techno-economic optimization model," Journal of Business Economics, Springer, vol. 91(2), pages 253-282, March.
    4. Estefania Tobon-Valencia & Samir Lamouri & Robert Pellerin & Alexandre Moeuf, 2022. "Modeling of the Master Production Schedule for the Digital Transition of Manufacturing SMEs in the Context of Industry 4.0," Sustainability, MDPI, vol. 14(19), pages 1-28, October.

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