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Scheduling in Flexible Manufacturing Systems: Genetic Algorithms Approach

Scheduling in Flexible Manufacturing Systems: Genetic Algorithms Approach
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Author(s): Fraj Naifar (Digital Research Center of Sfax, Tunisia), Mariem Gzara (University of Monastir, Tunisia) and Taicir Loukil Moalla (Tabuk University, Saudi Arabia)
Copyright: 2018
Pages: 19
Source title: Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems
Source Author(s)/Editor(s): Ömer Faruk Yılmaz (Istanbul Technical University, Turkey & Yalova University, Turkey) and Süleyman Tüfekçí (University of Florida, USA)
DOI: 10.4018/978-1-5225-2944-6.ch001

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Abstract

Flexible manufacturing systems have many advantages like adaptation to changes and reduction of lateness. But flexible machines are expensive. The scheduling is a central functionality in manufacturing systems. Optimizing the job routing through the system, while taking advantage from the flexibility of the machines, aims at improving the system's profitability. The introduction of the flexibility defines a variant of the scheduling problems known as flexible job shop scheduling. This variant is more difficult than the classical job shop since two sub-problems are to be solved the assignment and the routing. To guarantee the generation of efficient schedules in reasonable computation time, the metaheuristic approach is largely explored. Particularly, much research has addressed the resolution of the flexible job shop problem by genetic algorithms. This chapter presents the different adaptations of the genetic scheme to the flexible job shop problem. The solution encodings and the genetic operators are presented and illustrated by examples.

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