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Simulation optimization-based decision support tool for steel manufacturing

Author

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  • Melouk, Sharif H.
  • Freeman, Nickolas K.
  • Miller, David
  • Dunning, Michelle

Abstract

To buffer against increasing global competition and variability in the price of raw materials, steel manufacturers continuously strive to improve operations and lower costs. In this research, we employ a simulation optimization approach to develop a decision support tool to aid in strategic and operational decision-making. Specifically, we investigate work-in-process inventory levels and potential manufacturing process modifications to reduce utilization costs. A simulation model captures the complex nature of the system while an optimizer searches the solution space and sends trial solutions to the simulation for evaluation. Experimentation suggests that significant daily cost savings are possible by modifying current inventory practices and production process capabilities. Overall, the work demonstrates the ability of the solution approach to analyze complex industrial systems and identify potential improvements in a short time frame.

Suggested Citation

  • Melouk, Sharif H. & Freeman, Nickolas K. & Miller, David & Dunning, Michelle, 2013. "Simulation optimization-based decision support tool for steel manufacturing," International Journal of Production Economics, Elsevier, vol. 141(1), pages 269-276.
    • Handle: RePEc:eee:proeco:v:141:y:2013:i:1:p:269-276
    DOI: 10.1016/j.ijpe.2012.08.001
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    References listed on IDEAS

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    1. Zulch, Gert & Rottinger, Sven & Vollstedt, Thorsten, 2004. "A simulation approach for planning and re-assigning of personnel in manufacturing," International Journal of Production Economics, Elsevier, vol. 90(2), pages 265-277, July.
    2. Samuel Gorenstein, 1970. "Planning Tire Production," Management Science, INFORMS, vol. 17(2), pages 72-82, October.
    3. Bonney, Maurice & Jaber, Mohamad Y., 2011. "Environmentally responsible inventory models: Non-classical models for a non-classical era," International Journal of Production Economics, Elsevier, vol. 133(1), pages 43-53, September.
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    Cited by:

    1. Leung, Lawrence C. & Wong, Wai Hung & Hui, Yer Van & Wan, Yulai, 2013. "Managing third-party logistics under uncertainty: A decision scheme and managerial implications," International Journal of Production Economics, Elsevier, vol. 145(2), pages 630-644.
    2. Trigos, Federico & Vazquez, Alan R. & Cárdenas-Barrón, Leopoldo Eduardo, 2019. "A simulation-based heuristic that promotes business profit while increasing the perceived quality of service industries," International Journal of Production Economics, Elsevier, vol. 211(C), pages 60-70.
    3. Samira Alvandi, 2020. "Energy Efficiency Improvement through Optimal Batch Sizing in Job Shop," Modern Applied Science, Canadian Center of Science and Education, vol. 14(10), pages 1-6, October.
    4. Gansterer, Margaretha & Almeder, Christian & Hartl, Richard F., 2014. "Simulation-based optimization methods for setting production planning parameters," International Journal of Production Economics, Elsevier, vol. 151(C), pages 206-213.
    5. Dávid Gyulai & András Pfeiffer & László Monostori, 2017. "Robust production planning and control for multi-stage systems with flexible final assembly lines," International Journal of Production Research, Taylor & Francis Journals, vol. 55(13), pages 3657-3673, July.

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