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Resource scheduling based on energy consumption for sustainable manufacturing

Authors:

Silviu Raileanu,

Alexandru Iatan,

Theodor Borangiu,

Octavian MorariuAuthors Info & Claims

Journal of Intelligent Manufacturing, Volume 28, Issue 7

Pages 1519 - 1530

https://doi.org/10.1007/s10845-015-1142-5

Published: 01 October 2017 Publication History

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.

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Cited By

Lin CChen P(2025)Eco-friendly smart design strategy for integrated semiconductor processesJournal of Intelligent Manufacturing10.1007/s10845-023-02226-w36:1(243-257)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s10845-023-02226-w
Camarinha-Matos LRocha AGraça P(2024)Collaborative approaches in sustainable and resilient manufacturingJournal of Intelligent Manufacturing10.1007/s10845-022-02060-635:2(499-519)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10845-022-02060-6
Ham A(2020)Transfer-robot task scheduling in flexible job shopJournal of Intelligent Manufacturing10.1007/s10845-020-01537-631:7(1783-1793)Online publication date: 5-Feb-2020
https://dl.acm.org/doi/10.1007/s10845-020-01537-6
Show More Cited By

Resource scheduling based on energy consumption for sustainable manufacturing
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning

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Published In

cover image Journal of Intelligent Manufacturing

Journal of Intelligent Manufacturing Volume 28, Issue 7

October 2017

259 pages

ISSN:0956-5515

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Copyright © Copyright © 2017 Springer Science+Business Media, LLC.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 October 2017

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Cited By

Lin CChen P(2025)Eco-friendly smart design strategy for integrated semiconductor processesJournal of Intelligent Manufacturing10.1007/s10845-023-02226-w36:1(243-257)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s10845-023-02226-w
Camarinha-Matos LRocha AGraça P(2024)Collaborative approaches in sustainable and resilient manufacturingJournal of Intelligent Manufacturing10.1007/s10845-022-02060-635:2(499-519)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10845-022-02060-6
Ham A(2020)Transfer-robot task scheduling in flexible job shopJournal of Intelligent Manufacturing10.1007/s10845-020-01537-631:7(1783-1793)Online publication date: 5-Feb-2020
https://dl.acm.org/doi/10.1007/s10845-020-01537-6
Lamjahdi ABouloiz HGallab MBerrado ABakkoury ZBouchon-Meunier BRamdani M(2018)Multi-agents systemsProceedings of the 12th International Conference on Intelligent Systems: Theories and Applications10.1145/3289402.3289521(1-6)Online publication date: 24-Oct-2018
https://dl.acm.org/doi/10.1145/3289402.3289521

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