Production planning and performance optimization of reconfigurable manufacturing systems using genetic algorithm

To stay competitive in the new dynamic market having large fluctuations in product demand, manufacturing companies must use systems that not only produce their goods with high productivity but also allow for rapid response to market changes. Reconfigurable manufacturing system (RMS) is a new paradigm that enables manufacturing systems to respond quickly and cost effectively to market demand. In other words, RMS is a system designed from the outset, for rapid changes in both hardware and software components, in order to quickly adjust its production capacity to fluctuations in market demand and adapt its functionality to new products. The effectiveness of an RMS depends on implementing its key characteristics and capabilities in the design as well as utilization stage. This paper focuses on the utilization stage of an RMS and introduces a methodology to effectively adjust scalable production capacities and the system functionalities to market demands. It is supposed that arrival orders of product families follow the Poisson distribution. The orders are lost if they are not met immediately. Considering these assumptions, a mixed integer nonlinear programming model is developed to determine optimum sequence of production tasks, corresponding configurations, and batch sizes. A genetic algorithm-based procedure is used to solve the model. The model is also applied to make decision on how to improve the performance of an RMS. Since there is no practical RMS, a numerical example is used to validate the results of the proposed model and its solution procedure.