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The flexible manufacturing system (FMS) scheduling problem is one of the most difficult NP-hard combinatorial optimisation problems. The exact solution of an FMS scheduling problem cannot be found within a reasonable amount of time, even for small size problems. Therefore, a metaheuristic algorithm is required to solve such a problem. The objective of this study is to develop a genetic algorithm (GA) approach to minimise makespan of the scheduling problem. A Taguchi orthogonal array is proposed instead of a full factorial experimental design for determining the parameters of the GA. The effects of the GA parameters on the minimum makespan values are determined and an analysis of variance is performed to investigate significance factors on the results.Keywords: flexible manufacturing system scheduling; genetic algorithm; Taguchi orthogonal arrays method 1. Introduction A flexible manufacturing system (FMS) is a highly automated machine cell consisting of a group of processing workstations (usually CNC machine tools), interconnected by an automated material handling and storage system and controlled by a distributed computer system (Groover 2001). Another definition of the FMS is that it is a reprogrammable manufacturing system capable of producing a variety of products automatically (Chryssolouris 2006). In the global competitive environment, developing technology in order to meet customer demands and expectations has brought flexibility in production. The FMS has been studied over the last 25 years. In the last decade, they became important elements in the success of enterprises (Chan and Chan 2004). The FMS is a complex discrete event dynamic system and complete utilisation of the available resources in a system is extremely important to optimise its productivity. The most important objective of FMS scheduling is to increase the utilisation of resources and to reduce the idle time. The resource utilisation is improved by scheduling the set of tasks so as to reduce the makespan (C max ) (Jyothi 2012).One way of achieving high productivity in an FMS is to solve scheduling problems optimal or near optimal. The scheduling problem can be considered more complex in an FMS than in a traditional manufacturing system (transfer line, job shop, flow shop, etc.). In general, it is very difficult to determine optimal solutions for FMS scheduling problems (Lee and Kim 1999). Various methods are used to find appropriate job schedules.In this study, a metaheuristic genetic algorithm (GA) technique commonly used as a stochastic search method in recent years was employed and an appropriate job schedule was obtained. The effects of the GA's factors affecting the scheduling were determined using Taguchi experimental design technique. The objective of the problem was to minimise the makespan (C max ) of an FMS scheduling problem. Fourty two different FMS problem data-sets were generated to find out parameters of the proposed GA. A bench-mark problem was also solved to illustrate effectiveness of the proposed approach.Although there ...

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