A GA-based approach for optimizing single-part flow-line configurations of RMS

Generating economical single-part flow-line (SPFL) configurations as candidates for a given demand period is an important optimization problem for reconfigurable manufacturing systems (RMS). The optimization problem addresses the questions of selecting number of workstations, number and type of paralleling identical machines as well as operation setups (OSs) for each workstation. The inputs include a precedence graph for a part, relationships between OSs and operations, machine options for each OS. The objective is to minimize the capital costs of the SPFL configurations. A 0–1 nonlinear programming (NLP) model is developed to handle the key issue of sharing machine utilization over consecutive OSs which is ignored in existing 0–1 integer linear programming (ILP) model. Then a GA-based approach is proposed to identify a set of economical solutions. To overcome the complexity of search space, a novel procedure is presented to guide GA to search within a refined solution space comprising the optimal configurations associated with feasible OS sequences. A case study shows that the best solution derived from the 0–1 NLP model through GA is better than the optimum of existing 0–1 ILP model. The results illustrate the effectiveness of our model and the efficiency of the GA-based approach.