The configurations of Reconfigurable Manufacturing Systems (RMS) evolve over time in order to provide the functionality and capacity needed, when it is needed. This paper provides a model for optimizing the capital cost of RMS configurations with multiple aspects using Genetic Algorithms (GAs). The optimized configurations can handle multiple parts and their structure is that of a flow line allowing paralleling of identical machines in each production stage. The various aspects of the RMS configurations being considered include arrangement of machines (number of stages and number of parallel machines per stage), equipment selection (machine type and corresponding machine configuration for each stage) and assignment of operations (operation clusters assigned to each stage corresponding to each part type). A novel procedure to overcome the complexity of the search space by mapping from the discrete domain of the decision variables to a continuous domain of variables that guarantees the generation of feasible alternatives is introduced. A case study is presented to demonstrate the use of the developed optimization model for which a toolbox was developed using MATLAB software. The results show that the developed procedure not only overcomes the challenge of constraint satisfaction of such a complicated problem but also generates economical configurations in a reasonable time. This methodology can support manufacturing systems configuration selection decisions both at the initial design and reconfiguration stages.

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The selection of Reconfigurable Manufacturing Systems (RMS) configurations that include arrangement of machines, equipment selection, and assignment of operations, has a significant impact on their performance. This paper reviews the relevant literature and highlights the gaps that exist in this area of research. A novel ''RMS Configuration Selection Approach'' is introduced. It consists of two phases; the first deals with the selection of the near-optimal alternative configurations for each possible demand scenario over the considered configuration periods. It uses a constraint satisfaction procedure and powerful meta-heuristics, real-coded Genetic Algorithms (GAs) and Tabu Search (TS), for the continuous optimization of capital cost and system availability. The second phase utilizes integer-coded GAs and TS to determine the alternatives, from those produced in the first phase, that would optimize the degree of transition smoothness over the planning horizon. It uses a stochastic model of the level of reconfiguration smoothness (RS) across all the configuration periods in the planning horizon according to the anticipated demand scenarios. This model is based on a RS metric and a reconfiguration planning procedure that guide the development of execution plans for reconfiguration. The developed approach is demonstrated and validated using a case study. It was shown that it is possible to provide the manufacturing capacity and functionality needed when needed while minimizing the reconfiguration effort. The proposed approach can provide decision support for management in selecting RMS configurations at the beginning of each configuration period.

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