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Predictive Reactive Approach for Energy-Aware Scheduling and Control of Flexible Manufacturing Processes: Application on Flexible Job Shop

Predictive Reactive Approach for Energy-Aware Scheduling and Control of Flexible Manufacturing Processes: Application on Flexible Job Shop

Mohammed El Amine Meziane, Noria Taghezout
Copyright: © 2018 |Volume: 11 |Issue: 4 |Pages: 20
ISSN: 1935-5726|EISSN: 1935-5734|EISBN13: 9781522543138|DOI: 10.4018/IJISSCM.2018100103
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MLA

Meziane, Mohammed El Amine, and Noria Taghezout. "Predictive Reactive Approach for Energy-Aware Scheduling and Control of Flexible Manufacturing Processes: Application on Flexible Job Shop." IJISSCM vol.11, no.4 2018: pp.43-62. http://doi.org/10.4018/IJISSCM.2018100103

APA

Meziane, M. E. & Taghezout, N. (2018). Predictive Reactive Approach for Energy-Aware Scheduling and Control of Flexible Manufacturing Processes: Application on Flexible Job Shop. International Journal of Information Systems and Supply Chain Management (IJISSCM), 11(4), 43-62. http://doi.org/10.4018/IJISSCM.2018100103

Chicago

Meziane, Mohammed El Amine, and Noria Taghezout. "Predictive Reactive Approach for Energy-Aware Scheduling and Control of Flexible Manufacturing Processes: Application on Flexible Job Shop," International Journal of Information Systems and Supply Chain Management (IJISSCM) 11, no.4: 43-62. http://doi.org/10.4018/IJISSCM.2018100103

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Abstract

Manufacturing processes are responsible for a considerable amount of global energy consumption and world CO2 emissions. Reducing energy consumption during manufacturing is considered one of the most important strategies in contributing to the green supply chain. In this context, the authors propose a new predictive-reactive approach to control energy consumption during manufacturing processes. In addition to forecasting the energy needs, the proposed approach controls the uncertainty of energy volatility and limits energy waste during manufacturing processes. With the integration of this economic-environmental manufacturing efficiency in supply chains, and controlling uncertainty, this approach positively contributes to green and agile supply chains. A multi-objective genetic algorithm (NSGA-2) is proposed as a predictive method, and a new reactive method is developed to dynamically control the energy consumption throughout the peak energy consumption in real time. The approach was tested on the AIP-PRIMECA benchmark, which reflects a real production cell.

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