A Workload Data Aggregation Process for Flexible Manufacturing System Modeling using Queueing Network Models

In this article, we focus on the use of queueing network (QN) models for quantitatively evaluating the steady-state performance of flexible manufacturing systems (FMSs) at the strategic and tactical decision levels. The first problem encountered with such an approach concerns the description of an FMS, that is, obtaining reliable data from industrial experts. Secondly, when exploited directly, such data (especially those related to FMS workload) often result in a QN model that cannot be analytically or numerically analyzed because of a prohibitive number of customer chains and classes. In this context, we propose a formalization of the workload of an FMS, which is further exploited for defining a systematic workload data aggregation process. This process makes it possible to derive transparently the exact characteristics of a QN model which is much more tractable. The automated data aggregation approach is discussed and its robustness is studied in several examples, one of which is an industrial case.

[1]  Nico M. van Dijk.  Technical Note - Comment on Yao and Buzacott's "Modeling a Class of Flexible Manufacturing Systems with Reversible Routing" , 1989, Oper. Res..

[2]  Y. Dallery,et al.  Operational analysis of multiclass queueing networks , 1986, 1986 25th IEEE Conference on Decision and Control.

[3]  Yves Dallery,et al.  Manufacturing flow line systems: a review of models and analytical results , 1992, Queueing Syst. Theory Appl..

[4]  Jeffrey P. Buzen,et al.  Computational algorithms for closed queueing networks with exponential servers , 1973, Commun. ACM.

[5]  Rajan Suri,et al.  From CAN-Q to MPX: Evolution of Queuing Software for Manufacturing , 1995 .

[6]  Charles S. Tapiero,et al.  Network of queues modeling in flexible manufacturing systems : a survey , 1993 .

[7]  Ramakrishna Desiraju,et al.  Performance Analysis of Flexible Manufacturing Systems with a Single Discrete Material-Handling Device , 1997 .

[8]  MengChu Zhou,et al.  Petri net synthesis for discrete event control of manufacturing systems , 1992, The Kluwer international series in engineering and computer science.

[9]  J. A. Buzacott,et al.  Flexible manufacturing systems: a review of analytical models , 1986 .

[10]  K. Mani Chandy,et al.  Open, Closed, and Mixed Networks of Queues with Different Classes of Customers , 1975, JACM.

[11]  Bruno Baynat,et al.  A Product-Form Approximation Method for General Closed Queueing Networks with Several Classes of Customers , 1996, Perform. Evaluation.

[12]  J. A. Buzacott,et al.  Queueing models for a flexible machining station Part II: The method of Coxian phases , 1985 .

[13]  John A. Buzacott,et al.  Stochastic models of manufacturing systems , 1993 .

[14]  Richard E. Nance.  Model Representation in Discrete Event Simulation: The Conical Methodology , 1981 .

[15]  Michael K. Molloy,et al.  On the integration of delay and throughput measures in distributed processing models , 1981 .

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

[17]  S. Albin.  On Poisson Approximations for Superposition Arrival Processes in Queues , 1982 .

[18]  David F. Rogers,et al.  Similarity and distance measures for cellular manufacturing. Part I. A survey , 1993 .

[19]  Kathryn E. Stecke,et al.  Formulation and Solution of Nonlinear Integer Production Planning Problems for Flexible Manufacturing Systems , 1983 .

[20]  Jiyin Liu,et al.  A new classification scheme for flexible manufacturing systems , 1993 .

[21]  Peter J. Denning,et al.  The Operational Analysis of Queueing Network Models , 1978, CSUR.

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

[23]  Tadao Murata,et al.  Petri nets: Properties, analysis and applications , 1989, Proc. IEEE.

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

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

[26]  Rajan Suri,et al.  Modelling flexible manufacturing systems using mean-value analysis , 1984 .

[27]  B. L. Dietrich.  A Taxonomy of Discrete Manufacturing Systems , 1991, Oper. Res..

[28]  Nikolay Tchernev,et al.  Object-oriented methodology for FMS modelling and simulation , 1997 .

[29]  Marco Ajmone Marsan,et al.  Performance models of multiprocessor systems , 1987, MIT Press series in computer systems.

[30]  Kathryn E. Stecke,et al.  Design, planning, scheduling, and control problems of flexible manufacturing systems , 1985 .

[31]  Harry G. Perros.  Queueing networks with blocking , 1994 .

[32]  Michel Gourgand,et al.  Performance evaluation of a job-shop-like flexible manufacturing system with transfer blocking using a Markovian queuing network model , 1998, Int. J. Comput. Integr. Manuf..

[33]  John Zahorjan,et al.  The approximate solution of large queueing network models , 1980 .

[34]  J. A. Buzacott,et al.  Queueing models for a flexible machining station Part I: The diffusion approximation , 1985 .

[35]  Y. Dallery.  On modelling flexible manufacturing systems using closed queueing networks , 1986 .

[36]  Kimon P. Valavanis.  On the hierarchical modeling analysis and simulation of flexible manufacturing systems with extended Petri nets , 1990, IEEE Trans. Syst. Man Cybern..

[37]  Ward Whitt,et al.  Approximating a Point Process by a Renewal Process, I: Two Basic Methods , 1982, Oper. Res..