Generating economical single-product flow-line configurations as candidates for a given demand period is a key optimization problem for reconfigurable manufacturing systems (RMS) at both initial design and reconfiguration stages. The optimization problem addresses the questions of selecting number of workstations, number and type of machines as well as assigned operations for each workstation. Given an operation precedence graph for a product and machine options for each operation, the objective is to minimize the capital costs of the configurations subject to constraints on space, initial investment, production functionality and capacity. A genetic algorithm (GA) based approach is presented to identify a set of economical configurations for the complicated constrained optimization problem. To overcome the complexity of search space, a novel procedure is introduced to guide GA to search within a refined feasible solution space which only includes the optimal configurations associated with feasible operation sequences. A case study illustrates the effectiveness and efficiency of the GA based approach.
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