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

Abstract

This study addresses capacity scalability planning for job-shop-type reconfigurable manufacturing systems with dynamic demands over a planning horizon. The problem is to determine the number of machines, transporters, loading/unloading stations and pallets required in each period of the planning horizon while satisfying the demands and the minimum allowable station utilization. For the basic problem with non-decreasing demands, the previous model is modified into a more practical one with a limited number of pallets. The objective is to minimize the sum of component acquisition and configuration change costs. After formulating the problem as a nonlinear integer programming model that includes a closed queuing network model based approximations of throughputs and utilizations, new backward heuristics are proposed that determine the system components to be added from the last to the first period using a priority rule based local search method. Computational experiments were done on various test instances, and the results show that they outperform the existing ones significantly. In addition, for the extended problem with fluctuating demands, two types of variable neighborhood search (VNS) algorithms are proposed that minimize the sum of component acquisition/removal and configuration change costs and their test results are reported.