Abstract
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.