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Resource scheduling based on energy consumption for sustainable manufacturing

Author

Listed:
  • Silviu Raileanu

    (University Politehnica of Bucharest)

  • Florin Anton

    (University Politehnica of Bucharest)

  • Alexandru Iatan

    (University of Civil Engineering of Bucharest)

  • Theodor Borangiu

    (University Politehnica of Bucharest)

  • Silvia Anton

    (University Politehnica of Bucharest)

  • Octavian Morariu

    (University Politehnica of Bucharest)

Abstract

The paper proposes an agent-based approach for measuring in real time energy consumption of resources in job-shop manufacturing processes. Data from industrial robots is collected, analysed and assigned to operation types, and then integrated in an optimization engine in order to estimate how alternating between makespan and energy consumption as objective functions affects the performances of the whole system. This study focuses on the optimization of energy consumption in manufacturing processes through operation scheduling on available resources. The decision making algorithm relies on a decentralized system collecting data about resources implementing thus an intelligent manufacturing control system; the optimization problem is implemented using IBM ILOG OPL.

Suggested Citation

  • Silviu Raileanu & Florin Anton & Alexandru Iatan & Theodor Borangiu & Silvia Anton & Octavian Morariu, 2017. "Resource scheduling based on energy consumption for sustainable manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(7), pages 1519-1530, October.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:7:d:10.1007_s10845-015-1142-5
    DOI: 10.1007/s10845-015-1142-5
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    References listed on IDEAS

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    1. Depuru, Soma Shekara Sreenadh Reddy & Wang, Lingfeng & Devabhaktuni, Vijay, 2011. "Smart meters for power grid: Challenges, issues, advantages and status," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2736-2742, August.
    2. David Applegate & William Cook, 1991. "A Computational Study of the Job-Shop Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 3(2), pages 149-156, May.
    3. Kan Fang & Nelson Uhan & Fu Zhao & John Sutherland, 2013. "Flow shop scheduling with peak power consumption constraints," Annals of Operations Research, Springer, vol. 206(1), pages 115-145, July.
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    Cited by:

    1. Kamble, Sachin S. & Gunasekaran, Angappa & Ghadge, Abhijeet & Raut, Rakesh, 2020. "A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation," International Journal of Production Economics, Elsevier, vol. 229(C).
    2. Rami Naimi & Maroua Nouiri & Olivier Cardin, 2021. "A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives," Sustainability, MDPI, vol. 13(23), pages 1-36, November.
    3. Athar Ajaz Khan & János Abonyi, 2022. "Simulation of Sustainable Manufacturing Solutions: Tools for Enabling Circular Economy," Sustainability, MDPI, vol. 14(15), pages 1-40, August.
    4. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    5. João M. R. C. Fernandes & Seyed Mahdi Homayouni & Dalila B. M. M. Fontes, 2022. "Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review," Sustainability, MDPI, vol. 14(10), pages 1-34, May.
    6. Andy Ham, 2020. "Transfer-robot task scheduling in flexible job shop," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1783-1793, October.

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