Expansion method for the throughput analysis of open finite manufacturing/queueing networks with N-policy

In this paper we consider arbitrary topology manufacturing (queueing) syste ms with finite buffers and N-policy. N-policy involves a queueing system in which the machine (server) is assigned to alternative jobs when it becomes idle and becomes available only after the queue builds up to a predetermin ed level of N jobs. We use the decomposition, isolation and expansion metho dologies to calculate the throughput of the system. The methodology is test ed rigorously by using orthogonal arrays to design the experiments in order to cover a large experimental region. The results of the methodology are c ompared with simulation results. To this end, we also develop a simulation model (which in itself is quite challenging). The differences in the two re sults are investigated using t-tests. Based on the results, the methodology proves to be remarkably accurate and robust over a broad range of paramete rs. Scope and purpose Manufacturing systems these days are complex networks of service stations w ith finite capacities. In order to model such networks, researchers usually resort to simulation modeling just to capture the finite capacity restrict ion. However, a recently reported approximation technique, called the expan sion methodology, has proven to be extremely robust. In this paper, an addi tional complication is introduced to the manufacturing system. In order to increase the utilization of machines (or minimize machine idle time), the w ork at a station is actually accumulated while the machine at that station is assigned to alternative jobs as soon as it becomes idle. The machine kee ps processing these alternative jobs till the accumulated work at the stati on reaches a predetermined level of N jobs, In order to capture all these c omplications, this paper uses decomposition, isolation and expansion method ologies to develop an analytical (approximation) technique to model such a system. System throughput is used as the overall measure of performance. Th e technique is thoroughly tested and is found to be reliable, easy to progr am and robust. (C) 1999 Elsevier Science Ltd. All rights reserved.