Using Genetic Algorithms to solve scheduling problems on flexible manufacturing systems (FMS): a literature survey, classification and analysis

This paper reviews the literature regarding Genetic Algorithms (GAs) applied to flexible manufacturing system (FMS) scheduling. On the basis of this literature review, a classification system is proposed that encompasses 6 main dimensions: FMS type, types of resource constraints, job description, scheduling problem, measure of performance and solution approach. The literature review found 40 papers, which were classified according to these criteria. The literature was analyzed using the proposed classification system, which provides the following results regarding the application of GAs to FMS scheduling: (1) combinations of GAs and other methods were relatively important in the reviewed papers; (2) although most studies deal with complex environments concerning both the routing flexibility and the job complexity, only a minority of papers simultaneously consider the variety of possible capacity constraints on an FMS environment, including pallets and automated guided vehicles; (3) local search is rarely used; (4) makespan is the most widely used measure of performance.