A Survey of The Optimization of The Flexible Job Shop Problem
Ghiath Al Aqel
Ghiath Al Aqel, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
Manuscript received on 18 June 2018 | Revised Manuscript received on 27 June 2018 | Manuscript published on 30 June 2018 | PP: 13-16 | Volume-7 Issue-5, June 2018 | Retrieval Number: E5379067518/18©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The flexible job shop problem is an important problem in modern manufacturing systems. It is known to be an NP-hard problem. The optimization of this problem can bring in considerable improvements in the manufacturing efficiency. In recent studies, it has attracted the attention of most researchers in this field. Several metaheuristic methods were proposed to solve this problem. These methods started with exact algorithms and later approximate methods, which include heuristic methods, evolutionary algorithms, swarm intelligence, local search and hybrid algorithms, were introduced to cope with the development and the growing scale of the flexible job shop problem. In this paper we explore the algorithms that are most commonly used to solve this problem. This paper also aims to evaluate and compare the performance of these algorithms
Keywords: Flexible Job Shop, Optimization Algorithms
Scope of the Article: Algorithm Engineering