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Scheduling in an FMS environment is more complex and difficult than in a conventional manufacturing environment. Therefore, determining an optimal schedule and controlling an FMS is considered a difficult task. To achieve high performance for an FMS, a good scheduling system should make a right decision at a right time according to system conditions. In recent years, the use of Meta heuristic based approaches such as Tabu Search, Simulated Annealing and genetic algorithms increased for scheduling in a Flexible Manufacturing System (FMS). These, do not guarantee to find an optimal schedule, but have the ability to find near to optimum solutions in a short time.
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