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Energy Efficiency Improvement through Optimal Batch Sizing in Job Shop

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  • Samira Alvandi

Abstract

The increasing customization of products with greater variances and smaller lot sizes, has motivated manufacturers to adopt highly dynamic production planning. The production plans not only need to adapt to the production system state changes rapidly but also need to adopt energy reduction schemes to satisfy key sustainability performance indicators. The dilemma from industry point of view is to tackle multi-faceted problem of optimising economic and environmental performance. This research aims to overcome the multi-faceted objectives of small and medium-sized enterprises (SME’s) by providing a simulation-optimisation platform that creates the best possible production plans for optimum results. The applicability of the proposed framework is demonstrated through a real-life job-shop environment with the focus on optimisation of energy as well as job tardiness.

Suggested Citation

  • 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.
    • Handle: RePEc:ibn:masjnl:v:14:y:2020:i:10:p:6
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    References listed on IDEAS

    as
    1. 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.
    2. Samira Alvandi & Wen Li & Sami Kara, 2017. "An Integrated Simulation Optimisation Decision Support Tool for Multi-Product Production Systems," Modern Applied Science, Canadian Center of Science and Education, vol. 11(6), pages 1-56, June.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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