Design of self-regulating production control systems by Tradeoffs Programming

Abstract Self-regulating Production Control Systems (SPCSs) include production control systems such as kanban, buffered lines, base-stock, and their variations and combinations. In this work we develop and implement, for SPCS design, a new simulation-based optimization method called Tradeoffs Programming (TOP). TOP is a technique closely related to multi-objective dynamic programming that attempts to optimize inseparable problems. TOP is based on the idea that given correctly defined performance measures, many systems are nearly separable, and we can decompose the overall system using efficiency frontiers of simulated performance measures of the subsystems. We apply this technique to SPCS design and perform a number of experiments to validate and analyze our algorithm. We find TOP to be very accurate, extremely robust, and widely applicable.

[1]  S. Tayur.  Structural properties and a heuristic for kanban-controlled serial lines , 1993 .

[2]  Stan Uryasev,et al.  Probabilistic Constrained Optimization , 2000 .

[3]  R. S. Laundy,et al.  Multiple Criteria Optimisation: Theory, Computation and Application , 1989 .

[4]  R. Ramesh,et al.  Flow control in kanban based multicell manufacturing: I. A structural analysis of general blocking design space , 1997 .

[5]  Stephen G. Powell,et al.  Allocation of buffers to serial production lines with bottlenecks , 1996 .

[6]  Yushin Hong,et al.  Heuristic algorithms for buffer allocation in a production line with unreliable machines , 1995 .

[7]  Paul Glasserman.  Perturbation Analysis of Production Networks , 1994 .

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

[9]  Jonathan F. Bard,et al.  Determining the number of kanbans in a multiproduct, multistage production system , 1991 .

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

[11]  Y. T. Herer,et al.  CONWIP-based production lines with multiple bottlenecks: performance and design implications , 1999 .

[12]  Sridhar R. Tayur.  Properties of serial kanban systems , 1992, Queueing Syst. Theory Appl..

[13]  Samia M. Siha.  The pull production system: modelling and characteristics , 1994 .

[14]  SO KUTC..  Optimal buffer allocation strategy for minimizing work-in- process inventory in unpaced production lines , 1997 .

[15]  Michael Masin,et al.  Mathematical programming formulation of CONWIP based production lines; and relationships to MRP , 1997 .

[16]  Jong-Tae Lim,et al.  Bottlenecks in serial production lines: A system-theoretic approach , 1996 .

[17]  C.-L. Lin,et al.  Performance evaluation of unbalanced serial production lines , 1994 .

[18]  Semyon M. Meerkov,et al.  A system-theoretic property of serial production lines: improvability , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[19]  David D. Yao,et al.  Structured buffer-allocation problems , 1996, Discret. Event Dyn. Syst..

[20]  Asbjoern M. Bonvik,et al.  A comparison of production-line control mechanisms , 1997 .

[21]  Wallace J. Hopp,et al.  Throughput of a constant work in process manufacturing line subject to failures , 1991 .

[22]  David D. Yao,et al.  Algebraic structure of some stochastic discrete event systems, with applications , 1991, Discret. Event Dyn. Syst..

[23]  Ben Atkinson,et al.  Queueing theory in manufacturing systems analysis and design , 1993 .

[24]  Debasis Mitra,et al.  Analysis of a Kanban Discipline for Cell Coordination in Production Lines, II: Stochastic Demands , 1991, Oper. Res..

[25]  W. Hopp,et al.  CONWIP ASSEMBLY WITH DETERMINISTIC PROCESSING AND RANDOM OUTAGES , 1992 .

[26]  Moosa Sharafali,et al.  Overplanning Factor in Toyota's Formula for Computing the Number of Kanban , 1997 .

[27]  Osamu Kimura,et al.  Desiǵn and analysis of Pull System, a method of multi-staǵe production control , 1981 .

[28]  Reinaldo Morabito,et al.  OPEN QUEUEING NETWORKS: OPTIMIZATION AND PERFORMANCE EVALUATION MODELS FOR DISCRETE MANUFACTURING SYSTEMS * , 2009 .

[29]  Debasis Mitra,et al.  Analysis of a Kanban discipline for cell coordination in production lines , 1990 .

[30]  Wallace J. Hopp,et al.  Characterizing the Output Process of a CONWIP Line with Deterministic Processing and Random Outages , 1993 .

[31]  J. Little.  A Proof for the Queuing Formula: L = λW , 1961 .

[32]  Y. C. Ho,et al.  A New Approach to Determine Parameter Sensitivities of Transfer Lines , 1983 .

[33]  Mark L. Spearman,et al.  Customer Service in Pull Production Systems , 1992, Oper. Res..

[34]  S. Uryasev.  Probabilistic constrained optimization : methodology and applications , 2000 .

[35]  Gabriel R. Bitran,et al.  A mathematical programming approach to a deterministic Kanban system , 1987 .

[36]  James E. Schor.  Efficient algorithms for buffer allocation , 1995 .

[37]  A. D. Knott,et al.  THE INEFFICIENCY OF A SERIES OF WORK STATIONS—A SIMPLE FORMULA , 1970 .

[38]  Pieter W. Adriaans,et al.  Production control , 2002 .

[39]  Y. Sugimori,et al.  Toyota production system and Kanban system Materialization of just-in-time and respect-for-human system , 1977 .

[40]  S. Gershwin,et al.  PRODUCTION CONTROL FOR A TANDEM TWO-MACHINE SYSTEM , 1993 .

[41]  Wallace J. Hopp,et al.  Factory physics : foundations of manufacturing management , 1996 .

[42]  Dinah W. Cheng,et al.  Tandem queues with general blocking: A unified model and comparison results , 1993, Discret. Event Dyn. Syst..

[43]  Stanley B. Gershwin,et al.  Efficient algorithms for buffer space allocation , 2000, Ann. Oper. Res..

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

[45]  Reuven Y. Rubinstein,et al.  Modern simulation and modeling , 1998 .

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

[47]  Marc Gravel,et al.  A review of optimisation models of Kanban-based production systems , 1994 .

[48]  Heinrich Kuhn,et al.  Analysis of production control systems kanban and CONWIP , 1996 .

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

[50]  Izak Duenyas,et al.  Estimating the throughput of a cyclic assembly system , 1994 .