A multi-period capacity scalability planning algorithm for job-shop-type reconfigurable manufacturing systems with increasing demands

Abstract

A reconfigurable manufacturing system (RMS) is an advanced manufacturing technology for rapid changes in hardware and software components to adjust production capacity and functionality exactly in response to market changes. In this study, we address a multi-period capacity scalability planning problem for job-shoptype RMSs, which determines the system components to be added in each period of a planning horizon while satisfying the non-decreasing part demands and the minimum allowable workstation utilization. The objective is to minimize the sum of component purchase, configuration change and pallet costs. After representing the problem as a nonlinear integer programming model with estimations of system throughputs and utilizations using a closed queuing network approach, new heuristics are proposed that determine the components to be added from the last to the first period using a priority rule based local search method and an existing heuristic. Computational results show that they outperform the existing ones significantly.