Throughput of a constant work in process manufacturing line subject to failures

We consider a production line consisting of several machines in tandem operating under a constant work in process (CONWIP) control strategy. We assume that processing times are deterministic but machines are subject to exponential failures and repairs. We model this system as a closed queueing network and develop an approximate regenerative model (ARM) for estimating throughput and average cycle time as a function of WIP level. We compare ARM with mean value analysis (MVA) and develop readily computable tests of the suitability of the two approaches to a given production system. Through comparison with simulations, we show that ARM gives better predictions than MVA in a range of realistic situations.

[1]  Rajan Suri,et al.  Robustness of queuing network formulas , 1983, JACM.

[2]  Samuel Karlin,et al.  A First Course on Stochastic Processes , 1968 .

[3]  Sheldon M. Ross,et al.  Stochastic Processes , 2018, Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics.

[4]  Michael H. Rothkopf,et al.  Reliability and Inventory in a Production-Storage System , 1979 .

[5]  Michael A. Zazanis,et al.  Push and Pull Production Systems: Issues and Comparisons , 1992, Oper. Res..

[6]  Wallace J. Hopp,et al.  Optimal inventory control in a production flow system with failures , 1989 .

[7]  J. A. Buzacott,et al.  The role of inventory banks in flow-line production systems , 1971 .

[8]  Don T. Phillips,et al.  A comparison of order release strategies in production control systems , 1992 .

[9]  J. Wijngaard.  The Effect of Interstage Buffer Storage on the Output of Two Unreliable Production Units in Series, with Different Production Rates , 1979 .

[10]  A. Sharifnia,et al.  Production control of a manufacturing system with multiple machine states , 1988 .

[11]  K. G. Ramakrishnan,et al.  An overview of PANACEA, a software package for analyzing Markovian queueing networks , 1982, The Bell System Technical Journal.

[12]  W. Whitt,et al.  The Queueing Network Analyzer , 1983, The Bell System Technical Journal.

[13]  Ward Whitt,et al.  The Influence of Service-Time Variability in a Closed Network of Queues , 1986, Perform. Evaluation.

[14]  W. J. Gordon,et al.  Closed Queuing Systems with Exponential Servers , 1967, Oper. Res..

[15]  Stanley B. Gershwin,et al.  An algorithm for the computer control of a flexible manufacturing system , 1983 .

[16]  R. Akella,et al.  Optimal control of production rate in a failure prone manufacturing system , 1985, 1985 24th IEEE Conference on Decision and Control.

[17]  D. König,et al.  Queueing Networks: A Survey of Their Random Processes , 1985 .

[18]  Stephen S. Lavenberg,et al.  Mean-Value Analysis of Closed Multichain Queuing Networks , 1980, JACM.

[19]  Sheldon M. Ross,et al.  VII – Bandit Processes , 1983 .

[20]  David L. Woodruff,et al.  CONWIP: a pull alternative to kanban , 1990 .

[21]  W. Whitt,et al.  Open and closed models for networks of queues , 1984, AT&T Bell Laboratories Technical Journal.