Capacity Scalability Planning Algorithms for Job-shop-type Reconfigurable Manufacturing Systems with Dynamic Demands

Abstract

This study addresses dynamic capacity scalability planning for job-shop-type reconfigurable manufacturing systems (RMSs). The problem is to determine the system components that satisfies the part demands and the minimum workstation utilization in each period of a planning horizon. For the basic case of non-decreasing demands, the previous model is extended by considering a limited number of pallets. After formulating the problem that minimizes the sum of component acquisition and configuration change costs as a nonlinear integer programming model with closed queueing network estimations of part throughputs and workstation utilizations, two backward heuristics are proposed that determine the system components from the last to the first period. Computational results show that they outperform the previous ones significantly. In addition, for the general case of fluctuating demands, two variable neighborhood search (VNS) algorithms are proposed that minimize the sum of component acquisition/removal and configuration change costs, and computational results are reported.