A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial inte...

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Veröffentlicht in:	Sustainability 2021-12, Vol.13 (23), p.13016
Hauptverfasser:	Naimi, Rami, Nouiri, Maroua, Cardin, Olivier
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial Intelligence Breakdowns Computer Science Cost control Decision making Employment Energy consumption Energy efficiency Genetic algorithms Heuristic Integer programming Job shops Learning algorithms Machine learning Manufacturing Methods Operations Research Optimization Process controls Productivity Rescheduling Researchers Scheduling Sustainability Systems stability
Online-Zugang:	Volltext
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