期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 302, 期 2, 页码 544-559出版社
ELSEVIER
DOI: 10.1016/j.ejor.2022.01.005
关键词
Manufacturing; Spare parts; Stochastic flow lines; Markov processes; Decomposition
We consider a model of an N-stage flow line with stochastic processing times and interstage buffers. Machine downtimes caused by failures of critical components are addressed by maintaining spare parts in stock. We propose a novel decomposition approach to approximate the average throughput and inventory for systems with different numbers of machines, buffers, and spare parts. Our method demonstrates remarkable accuracy and allows for studying the interaction and substitution effects between buffer sizes and spare part base-stock levels on the logistical performance of the flow line.
We consider a model of an N-stage flow line with stochastic processing times and interstage buffers that decouple adjacent production stages. Machine downtimes are induced by failures of critical machine components. Each machine is assumed to have exactly one of these failure-prone components. To achieve high machine availability, it is assumed that spare parts for those failure-prone critical components are kept in stock. Failed components are immediately replaced with new, functioning components, and a one-for-one replenishment policy is applied for the restocking of those spare parts. We present a novel decomposition approach to approximate the average throughput and inventory for a system with an arbitrary number of machines, buffers, and spare parts. With a detailed numerical study, we analyze the impact of different parameter constellations on the approximation quality. We demonstrate the remarkable accuracy of our method by comparing our results with both exact and simulated values. Using our method, we further study the complex interaction and partial substitution effects between buffer sizes and spare part base-stock levels on the logistical performance of the flow line. (C) 2022 Elsevier B.V. All rights reserved.
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