A Simulation-Based Genetic Algorithm Approach for Process Plans Selection in Uncertain Reconfigurable Environment

Reconfigurable Manufacturing Systems (RMSs) are a new paradigm of manufacturing able to customize its capacity and functionality when needed, to be reactive to market changes and uncertainties. In this paper, to take full advantage of the reconfigurability of RMSs, we propose a new approach using genetic algorithms and a simulation based optimization for process planning for a single product type. The proposed approach copes with market uncertainty and demands fluctuation in order to satisfy demands within their deadlines and with a minimum total cost. The total cost consists of five major costs: machine using cost, machine changing cost, configuration changing cost, tool using cost and tool changing cost. An illustrative example is presented to show the applicability of the approach.