A simulation analysis of the impact of finite buffer storage on manufacturing system reliability

Abstract This paper develops a Monte Carlo Simulation (MCS) approach to estimate the system reliability for a multistate manufacturing network with parallel production lines (MMN-PPL) considering finite buffer storage. System reliability indicates the probability that all workstations provide sufficient capacity to satisfy a specified demand and buffers possess adequate storage. The buffers are modeled as a network-structured MMN-PPL. Storage usage of buffers is analyzed based on the MMN-PPL. MCS algorithms are developed to generate the capacity state and to check the storage usage of buffers to determine whether the demand can be satisfied or not. System reliability of the MMN-PPL is estimated through simulation. The MCS approach is an efficient method to estimate system reliability for an MMN-PPL with a reasonable accuracy and time. A pair of practical examples including a tile and a touch panel manufacturing systems shows that system reliability is overestimated when buffer storage is assumed to be infinite. Demand satisfaction probability is further addressed to provide guidance for a proper production policy.

[1]  Yi-Kuei Lin,et al.  System reliability of a manufacturing network with reworking action and different failure rates , 2012 .

[2]  Yi-Kuei Lin,et al.  System reliability evaluation of a touch panel manufacturing system with defect rate and reworking , 2013, Reliab. Eng. Syst. Saf..

[3]  Zhao Xiaobo,et al.  Analysis of a production system in a general configuration , 2001 .

[4]  Yi-Kuei Lin,et al.  Reliability Evaluation of a Hybrid Flow-Shop With Stochastic Capacity Within a Time Constraint , 2016, IEEE Transactions on Reliability.

[5]  Yi-Kuei Lin,et al.  Two-commodity reliability evaluation of a stochastic-flow network with varying capacity weight in terms of minimal paths , 2009, Comput. Oper. Res..

[6]  Yi-Kuei Lin,et al.  A Novel Reliability Evaluation Technique for Stochastic-Flow Manufacturing Networks With Multiple Production Lines , 2013, IEEE Transactions on Reliability.

[7]  David W. Coit,et al.  A Monte-Carlo simulation approach for approximating multi-state two-terminal reliability , 2005, Reliab. Eng. Syst. Saf..

[8]  Wei-Ning Yang,et al.  A simple recursive importance and stratified sampling scheme for stochastic network reliability estimation , 2012, Simul. Model. Pract. Theory.

[9]  Wei-Chang Yeh,et al.  Performance analysis of cellular automata Monte Carlo Simulation for estimating network reliability , 2010, Expert Syst. Appl..

[10]  Yi-Kuei Lin,et al.  Reliability analysis for an apparel manufacturing system applying fuzzy multistate network , 2015, Comput. Ind. Eng..

[11]  Yi-Kuei Lin,et al.  System reliability for a multistate flow network with multiple joint minimal paths under time constraint , 2012, Simul. Model. Pract. Theory.

[12]  Elsayed A. Elsayed,et al.  Reliability analysis of production systems with buffer storage , 1979 .

[13]  Armin Scholl,et al.  A survey on problems and methods in generalized assembly line balancing , 2006, Eur. J. Oper. Res..

[14]  James T. Lin,et al.  Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing , 2015, Simul. Model. Pract. Theory.

[15]  Yi-Kuei Lin,et al.  Simulation approach to estimate the system reliability of a time-based capacitated flow network susceptible to correlated failures , 2013, Simul. Model. Pract. Theory.

[16]  Deniz Türsel Eliiyi,et al.  The state of the art on buffer allocation problem: a comprehensive survey , 2014, J. Intell. Manuf..

[17]  D. R. Fulkerson,et al.  Flows in Networks. , 1964 .

[18]  William J. Stevenson,et al.  Operations Management , 2011 .

[19]  Wei-Chang Yeh,et al.  A simple minimal path method for estimating the weighted multi-commodity multistate unreliable networks reliability , 2008, Reliab. Eng. Syst. Saf..

[20]  Wei-Chang Yeh,et al.  A Sequential Decomposition Method for Estimating Flow in a Multi-Commodity, Multistate Network , 2011, IEEE Transactions on Reliability.

[21]  Yi-Kuei Lin,et al.  System Performance and Reliability Modeling of a Stochastic-Flow Production Network: A Confidence-Based Approach , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[22]  Ping-Chen Chang,et al.  On quality improvement of a multistate production network with service level consideration , 2017 .

[23]  Thomas F. Coleman,et al.  Smoothing and parametric rules for stochastic mean-CVaR optimal execution strategy , 2016, Ann. Oper. Res..