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“Just-for-Peak” buffer inventory for peak electricity demand reduction of manufacturing systems

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  • Fernandez, Mayela
  • Li, Lin
  • Sun, Zeyi

Abstract

The reduction of the electricity demand during peak periods is considered a main objective of electricity load management. It can relieve the financial pressure of the investment on the capacity expansion for the power grid in the United States. Compared to a great deal of research on commercial and residential building sectors, few studies on the electricity demand reduction during peak periods for industrial manufacturing systems have been conducted due to the concern of system throughput variation and the complexity of modern manufacturing systems. This paper presents a novel “Just-for-Peak” buffer inventory methodology to reduce the electricity consumption without compromising system throughput during peak periods for typical manufacturing systems with multiple machines and buffers. Nonlinear Integer Programming (NIP) formulation is used to establish the mathematical model. The optimal buffer inventory management policies and corresponding load management actions for the whole system are identified by minimizing the holding cost of the “Just-for-Peak” buffer inventory and energy consumption cost under the system throughput constraint throughout the production horizon. A numerical case study based on an automotive assembly line is used to illustrate the effectiveness of the proposed method.

Suggested Citation

  • Fernandez, Mayela & Li, Lin & Sun, Zeyi, 2013. "“Just-for-Peak” buffer inventory for peak electricity demand reduction of manufacturing systems," International Journal of Production Economics, Elsevier, vol. 146(1), pages 178-184.
    • Handle: RePEc:eee:proeco:v:146:y:2013:i:1:p:178-184
    DOI: 10.1016/j.ijpe.2013.06.020
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    References listed on IDEAS

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    1. Ashok, S., 2006. "Peak-load management in steel plants," Applied Energy, Elsevier, vol. 83(5), pages 413-424, May.
    2. Salameh, M. K. & Ghattas, R. E., 2001. "Optimal just-in-time buffer inventory for regular preventive maintenance," International Journal of Production Economics, Elsevier, vol. 74(1-3), pages 157-161, December.
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    Cited by:

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    2. Xiangxin An & Guojin Si & Tangbin Xia & Qinming Liu & Yaping Li & Rui Miao, 2022. "Operation and Maintenance Optimization for Manufacturing Systems with Energy Management," Energies, MDPI, vol. 15(19), pages 1-19, October.
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    5. Gahm, Christian & Denz, Florian & Dirr, Martin & Tuma, Axel, 2016. "Energy-efficient scheduling in manufacturing companies: A review and research framework," European Journal of Operational Research, Elsevier, vol. 248(3), pages 744-757.
    6. Wang, Yong & Li, Lin, 2016. "Critical peak electricity pricing for sustainable manufacturing: Modeling and case studies," Applied Energy, Elsevier, vol. 175(C), pages 40-53.
    7. Konstantin Biel & Christoph H. Glock, 2017. "Prerequisites of efficient decentralized waste heat recovery and energy storage in production planning," Journal of Business Economics, Springer, vol. 87(1), pages 41-72, January.
    8. Wang, Yong & Li, Lin, 2014. "Time-of-use based electricity cost of manufacturing systems: Modeling and monotonicity analysis," International Journal of Production Economics, Elsevier, vol. 156(C), pages 246-259.
    9. Marcos Manoel Lopes Junior & Claudia Aparecida de Mattos & Fábio Lima, 2024. "Toward Cleaner Production by Evaluating Opportunities of Saving Energy in a Short-Cycle Time Flowshop," Sustainability, MDPI, vol. 16(6), pages 1-23, March.
    10. Matthias Gerhard Wichmann & Christoph Johannes & Thomas Stefan Spengler, 2019. "An extension of the general lot-sizing and scheduling problem (GLSP) with time-dependent energy prices," Journal of Business Economics, Springer, vol. 89(5), pages 481-514, July.
    11. Zavanella, Lucio & Zanoni, Simone & Ferretti, Ivan & Mazzoldi, Laura, 2015. "Energy demand in production systems: A Queuing Theory perspective," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 393-400.
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