AMC-Based Algorithm for Network Reliability Evaluation of a Manufacturing System with Scrapping and Rework

doi:10.23940/ijpe.22.04.p1.231240

Abstract

Abstract: Focusing on the stochastic flow manufacturing system (SFMS), an Absorptive Markov Chain-based algorithm is proposed to calculate the network reliability as the performance index. The SFMS is a hybrid flow shop with multiple production lines, and each workstation in the production line contains multiple identical parallel machines. Because of machine failure, human factors, maintenance, and other factors, machines may have multiple capacities and be regarded as random variables. In addition, in practice, to avoid the waste of raw materials and reduce manufacturing costs, rework and scrapping mechanisms are often used in manufacturing systems. Therefore, whether the SFMS is meeting the demand is an important issue, and network reliability is an ideal quantitative indicator. However, most of the algorithms proposed for SFMS in the past are established for specific scenarios and are not easy to be used in complex manufacturing systems. The Absorption Markov Chain model is used to calculate the capacity required by all workstations, and then the minimum capacity vectors are obtained for calculating network reliability.

Key words: Absorptive Markov Chain, network reliability, rework, hybrid flow shop

Yu-Lun Chao, Yi-Kuei Lin, and Cheng-Ta Yeh. AMC-Based Algorithm for Network Reliability Evaluation of a Manufacturing System with Scrapping and Rework [J]. Int J Performability Eng, 2022, 18(4): 231-240.

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