IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v56y2008i5p1218-1237.html

Optimization Models of Discrete-Event System Dynamics

Author

Listed:
  • Wai Kin (Victor) Chan

    (Department of Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute, Troy, New York 12180)

  • Lee Schruben

    (Department of Industrial Engineering and Operations Research, University of California, Berkeley, California 94720)

Abstract

A methodology is given for modeling the dynamics of discrete-event stochastic systems as optimization problems. The intent is to provide a means to utilize the rich mathematical theory and algorithms of optimization in the study of this important class of systems. A procedure for mapping a simulation event relationship graph into a mixed-integer program is presented, along with examples of queueing networks and manufacturing systems that illustrate the approach. Several potential applications are examined, including automatic constraint generation for optimal resource scheduling, representations of max-plus algebra models for queueing system dynamics, response gradient estimation, and an unconventional technique for simulating queueing systems using virtual resources that are identified from the optimization models for these systems.

Suggested Citation

  • Wai Kin (Victor) Chan & Lee Schruben, 2008. "Optimization Models of Discrete-Event System Dynamics," Operations Research, INFORMS, vol. 56(5), pages 1218-1237, October.
    • Handle: RePEc:inm:oropre:v:56:y:2008:i:5:p:1218-1237
    DOI: 10.1287/opre.1080.0559
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.1080.0559
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.1080.0559?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. George Liberopoulos & Yves Dallery, 2000. "A unified framework for pull control mechanisms in multi‐stage manufacturing systems," Annals of Operations Research, Springer, vol. 93(1), pages 325-355, January.
    2. John N. Hooker, 2002. "Logic, Optimization, and Constraint Programming," INFORMS Journal on Computing, INFORMS, vol. 14(4), pages 295-321, November.
    3. Williams, H. P., 1995. "Logic applied to integer programming and integer programming applied to logic," European Journal of Operational Research, Elsevier, vol. 81(3), pages 605-616, March.
    4. Eric L. Savage & Lee W. Schruben & Enver Yücesan, 2005. "On the Generality of Event-Graph Models," INFORMS Journal on Computing, INFORMS, vol. 17(1), pages 3-9, February.
    5. Eginhard J. Muth, 1979. "The Reversibility Property of Production Lines," Management Science, INFORMS, vol. 25(2), pages 152-158, February.
    6. Tito Homem-de-Mello & Alexander Shapiro & Mark L. Spearman, 1999. "Finding Optimal Material Release Times Using Simulation-Based Optimization," Management Science, INFORMS, vol. 45(1), pages 86-102, January.
    7. Rajan Suri & Michael A. Zazanis, 1988. "Perturbation Analysis Gives Strongly Consistent Sensitivity Estimates for the M/G/1 Queue," Management Science, INFORMS, vol. 34(1), pages 39-64, January.
    8. Milind Dawande & Chelliah Sriskandarajah & Suresh Sethi, 2002. "On Throughput Maximization in Constant Travel-Time Robotic Cells," Manufacturing & Service Operations Management, INFORMS, vol. 4(4), pages 296-312, August.
    9. Dinah W. Cheng, 1995. "Line Reversibility of Tandem Queues with General Blocking," Management Science, INFORMS, vol. 41(5), pages 864-873, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arianna Alfieri & Andrea Matta & Giulia Pedrielli, 2015. "Mathematical programming models for joint simulation–optimization applied to closed queueing networks," Annals of Operations Research, Springer, vol. 231(1), pages 105-127, August.
    2. George Liberopoulos, 2020. "Comparison of optimal buffer allocation in flow lines under installation buffer, echelon buffer, and CONWIP policies," Flexible Services and Manufacturing Journal, Springer, vol. 32(2), pages 297-365, June.
    3. Alfieri, Arianna & Matta, Andrea, 2013. "Mathematical programming time-based decomposition algorithm for discrete event simulation," European Journal of Operational Research, Elsevier, vol. 231(3), pages 557-566.
    4. S. Göttlich & S. Kühn & J. A. Schwarz & R. Stolletz, 2016. "Approximations of time-dependent unreliable flow lines with finite buffers," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 83(3), pages 295-323, June.
    5. Alfieri, Arianna & Matta, Andrea, 2012. "Mathematical programming formulations for approximate simulation of multistage production systems," European Journal of Operational Research, Elsevier, vol. 219(3), pages 773-783.
    6. Alexander H. Gose & Brian T. Denton, 2016. "Sequential Bounding Methods for Two-Stage Stochastic Programs," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 351-369, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Liming & Yuan, Xue-Ming, 2001. "Throughput, flow times, and service level in an unreliable assembly system," European Journal of Operational Research, Elsevier, vol. 135(3), pages 602-615, December.
    2. Raf Jans, 2009. "Solving Lot-Sizing Problems on Parallel Identical Machines Using Symmetry-Breaking Constraints," INFORMS Journal on Computing, INFORMS, vol. 21(1), pages 123-136, February.
    3. Neil Geismar, H. & Dawande, Milind & Sriskandarajah, Chelliah, 2005. "Approximation algorithms for k-unit cyclic solutions in robotic cells," European Journal of Operational Research, Elsevier, vol. 162(2), pages 291-309, April.
    4. Jeffrey M. Alden & Lawrence D. Burns & Theodore Costy & Richard D. Hutton & Craig A. Jackson & David S. Kim & Kevin A. Kohls & Jonathan H. Owen & Mark A. Turnquist & David J. Vander Veen, 2006. "General Motors Increases Its Production Throughput," Interfaces, INFORMS, vol. 36(1), pages 6-25, February.
    5. Huizhi Ren & Shenshen Sun, 2016. "A Hybrid IP/GA Approach to the Parallel Production Lines Scheduling Problem," Discrete Dynamics in Nature and Society, John Wiley & Sons, vol. 2016(1).
    6. Robert Fourer & David M. Gay, 2002. "Extending an Algebraic Modeling Language to Support Constraint Programming," INFORMS Journal on Computing, INFORMS, vol. 14(4), pages 322-344, November.
    7. John N. Hooker, 2002. "Logic, Optimization, and Constraint Programming," INFORMS Journal on Computing, INFORMS, vol. 14(4), pages 295-321, November.
    8. Manafzadeh Dizbin, Nima & Tan, Barış, 2020. "Optimal control of production-inventory systems with correlated demand inter-arrival and processing times," International Journal of Production Economics, Elsevier, vol. 228(C).
    9. Schumacher, J.M., 1988. "Discrete events : Perspectives from system theory," Research Memorandum FEW 354, Tilburg University, School of Economics and Management.
    10. Matthias Thürer & Nuno O. Fernandes & Mark Stevenson & Cristovao Silva & Silvio Carmo-Silva, 2019. "POLC-A: an assessment of POLCA’s authorization element," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2435-2447, August.
    11. Kumar Satyam & Ananth Krishnamurthy, 2013. "Performance analysis of CONWIP systems with batch size constraints," Annals of Operations Research, Springer, vol. 209(1), pages 85-114, October.
    12. Tallys Yunes & Ionuţ D. Aron & J. N. Hooker, 2010. "An Integrated Solver for Optimization Problems," Operations Research, INFORMS, vol. 58(2), pages 342-356, April.
    13. Yongqiang Wang & Michael C. Fu & Steven I. Marcus, 2012. "A New Stochastic Derivative Estimator for Discontinuous Payoff Functions with Application to Financial Derivatives," Operations Research, INFORMS, vol. 60(2), pages 447-460, April.
    14. Cigdem Gurgur, 2013. "Optimal configuration of a decentralized, market-driven production/inventory system," Annals of Operations Research, Springer, vol. 209(1), pages 139-157, October.
    15. Yannis Pavlis & Will Recker, 2009. "A Mathematical Logic Approach for the Transformation of the Linear Conditional Piecewise Functions of Dispersion-and-Store and Cell Transmission Traffic Flow Models into Linear Mixed-Integer Form," Transportation Science, INFORMS, vol. 43(1), pages 98-116, February.
    16. Rodney P. Parker & Roman Kapuscinski, 2004. "Optimal Policies for a Capacitated Two-Echelon Inventory System," Operations Research, INFORMS, vol. 52(5), pages 739-755, October.
    17. M. Hossein Safizadeh, 1990. "Optimization in simulation: Current issues and the future outlook," Naval Research Logistics (NRL), John Wiley & Sons, vol. 37(6), pages 807-825, December.
    18. Mark J. Cathcart & Steven Morrison & Alexander J. McNeil, 2011. "Calculating Variable Annuity Liability 'Greeks' Using Monte Carlo Simulation," Papers 1110.4516, arXiv.org.
    19. Rubinstein, Reuven Y. & Shapiro, Alexander, 1990. "Optimization of static simulation models by the score function method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 32(4), pages 373-392.
    20. Francisco Trespalacios & Ignacio E. Grossmann, 2015. "Algorithmic Approach for Improved Mixed-Integer Reformulations of Convex Generalized Disjunctive Programs," INFORMS Journal on Computing, INFORMS, vol. 27(1), pages 59-74, February.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:oropre:v:56:y:2008:i:5:p:1218-1237. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.