1. Adaptive production control system for a flexible-manufacturing cell using support vector machine-based approach
- Author
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Lyes Benyoucef, Manoj Kumar Tiwari, Jitesh J. Thakkar, Fausto P. Garsia, Rohit Anand, Vijaya Kumar Manupati, Department of Industrial Engineering and Management [Kharagpur], Indian Institute of Technology Kharagpur (IIT Kharagpur), Laboratoire des Sciences de l'Information et des Systèmes (LSIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Arts et Métiers Paristech ENSAM Aix-en-Provence-Centre National de la Recherche Scientifique (CNRS), Universidad de Castilla-La Mancha = University of Castilla-La Mancha (UCLM), Centre National de la Recherche Scientifique (CNRS)-Arts et Métiers Paristech ENSAM Aix-en-Provence-Université de Toulon (UTLN)-Aix Marseille Université (AMU), and Universidad de Castilla-La Mancha (UCLM)
- Subjects
0209 industrial biotechnology ,Engineering ,Visual Basic ,Support vector machine ,Scheduling (production processes) ,02 engineering and technology ,Machine learning ,computer.software_genre ,Industrial and Manufacturing Engineering ,020901 industrial engineering & automation ,High productivity ,Dynamic environment ,0202 electrical engineering, electronic engineering, information engineering ,Sequencing ,computer.programming_language ,business.industry ,Scheduling ,Mechanical Engineering ,Control engineering ,[INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] ,Adaptive ,Computer Science Applications ,Control and Systems Engineering ,Production control ,Flexible manufacturing cell ,020201 artificial intelligence & image processing ,Artificial intelligence ,Manufacturing cell ,business ,Production control system ,computer ,Software ,Simulation - Abstract
National audience; Real-time adaptive production control in the flexible manufacturing cell (FMC) is a complex issue that needs to be addressed to realize good performance and high productivity. In this paper, we have considered a support vector machine (SVM)-based simulation approach to resolve a production control problem in an FMC that operates in a dynamic environment. A SVM-based simulation approach chooses the most relevant scheduling rule out of several predefined ones on the basis of the current states of the system. This paper examines and compares the performance of the SVM-based simulation approach with the competent scheduling rules under two different operational environments which are characterized by the uncertainty of demand. We have also developed a Visual Basic-based simulation approach for scheduling of component parts in the context of FMC under different situations. The SVM methodology to control the production offers better performance than the single-rule-based production control system.
- Published
- 2013