IDEAS home Printed from https://ideas.repec.org/a/wly/isacfm/v9y2000i4p257-270.html
   My bibliography  Save this article

Agent‐based stochastic production lines design

Author

Listed:
  • D. J. Wu

Abstract

An artificial agent‐based approach has been developed to improve the design and control of stochastic production lines. Genetic algorithms have been used as the premier agents learning mechanism. We benchmark our agent‐based approach with other well‐studied approaches such as infinitesimal perturbation analysis and mean‐value analysis methods. The performances of our agents are comparable with other approaches; in some cases, the agent‐based approach discovers even better solutions than the so‐called ‘optimal’ solutions by other approaches. The paper is one of the series of our work on multi‐agent intelligent enterprise modeling, and it seves as one of the most fundamental building blocks for other related works. © 2000 John Wiley & Sons, Ltd.

Suggested Citation

  • D. J. Wu, 2000. "Agent‐based stochastic production lines design," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(4), pages 257-270, December.
    • Handle: RePEc:wly:isacfm:v:9:y:2000:i:4:p:257-270
    DOI: 10.1002/1099-1174(200012)9:43.0.CO;2-4
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/1099-1174(200012)9:43.0.CO;2-4
    Download Restriction: no
    File URL: https://libkey.io/10.1002/1099-1174(200012)9:43.0.CO;2-4?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. Holland, John H & Miller, John H, 1991. "Artificial Adaptive Agents in Economic Theory," American Economic Review, American Economic Association, vol. 81(2), pages 365-371, May.
    2. Marimon, Ramon & McGrattan, Ellen & Sargent, Thomas J., 1990. "Money as a medium of exchange in an economy with artificially intelligent agents," Journal of Economic Dynamics and Control, Elsevier, vol. 14(2), pages 329-373, May.
    3. W. Brian Arthur & Paul Tayler, "undated". "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Computing in Economics and Finance 1997 57, Society for Computational Economics.
    4. Wellman, Michael P. & Walsh, William E. & Wurman, Peter R. & MacKie-Mason, Jeffrey K., 2001. "Auction Protocols for Decentralized Scheduling," Games and Economic Behavior, Elsevier, vol. 35(1-2), pages 271-303, April.
    5. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
    6. John J. Bartholdi & Donald D. Eisenstein, 1996. "A Production Line that Balances Itself," Operations Research, INFORMS, vol. 44(1), pages 21-34, February.
    Full references (including those not matched with items on IDEAS)

    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. Tesfatsion, Leigh, 1998. "Teaching Agent-Based Computational Economics to Graduate Students," ISU General Staff Papers 199807010700001043, Iowa State University, Department of Economics.
    2. Karolina Safarzyńska & Jeroen Bergh, 2010. "Evolutionary models in economics: a survey of methods and building blocks," Journal of Evolutionary Economics, Springer, vol. 20(3), pages 329-373, June.
    3. Tesfatsion, Leigh, 1995. "How Economists Can Get Alife," Economic Reports 18196, Iowa State University, Department of Economics.
    4. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, University Library of Munich, Germany, revised 15 Aug 2002.
    5. Santiago J. Gangotena, 2017. "Dynamic coordinating non-equilibrium," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 30(1), pages 51-82, March.
    6. Troy Tassier, 2013. "Handbook of Research on Complexity, by J. Barkley Rosser, Jr. and Edward Elgar," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 39(1), pages 132-133.
    7. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    8. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    9. Antonio Doria, Francisco, 2011. "J.B. Rosser Jr. , Handbook of Research on Complexity, Edward Elgar, Cheltenham, UK--Northampton, MA, USA (2009) 436 + viii pp., index, ISBN 978 1 84542 089 5 (cased)," Journal of Economic Behavior & Organization, Elsevier, vol. 78(1-2), pages 196-204, April.
    10. Ehrentreich, Norman, 2006. "Technical trading in the Santa Fe Institute Artificial Stock Market revisited," Journal of Economic Behavior & Organization, Elsevier, vol. 61(4), pages 599-616, December.
    11. Kollman, Ken & Miller, John H. & Page, Scott E., 1997. "Landscape formation in a spatial voting model," Economics Letters, Elsevier, vol. 55(1), pages 121-130, August.
    12. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2001. "Microscopic Models of Financial Markets," Papers cond-mat/0110354, arXiv.org.
    13. Ho, Teck-Hua, 1996. "Finite automata play repeated prisoner's dilemma with information processing costs," Journal of Economic Dynamics and Control, Elsevier, vol. 20(1-3), pages 173-207.
    14. Marco Casari, 2002. "Can genetic algorithms explain experimental anomalies? An application to common property resources," UFAE and IAE Working Papers 542.02, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    15. Tamotsu Onozaki, 2018. "Nonlinearity, Bounded Rationality, and Heterogeneity," Springer Books, Springer, number 978-4-431-54971-0, September.
    16. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    17. Benoît Desmarchelier & Faridah Djellal & Faïz Gallouj, 2019. "Users' Involvement in Value Co‐Creation: The More the Better?," Post-Print hal-02354136, HAL.
    18. Tay, Nicholas S.P. & Lusch, Robert F., 2005. "A preliminary test of Hunt's General Theory of Competition: using artificial adaptive agents to study complex and ill-defined environments," Journal of Business Research, Elsevier, vol. 58(9), pages 1155-1168, September.
    19. Beltrametti, Luca & Fiorentini, Riccardo & Marengo, Luigi & Tamborini, Roberto, 1997. "A learning-to-forecast experiment on the foreign exchange market with a classifier system," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1543-1575, June.
    20. Lijian Wei & Xiong Xiong & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2017. "The effect of genetic algorithm learning with a classifier system in limit order markets," Published Paper Series 2017-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.

    More about this item

    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:wly:isacfm:v:9:y:2000:i:4:p:257-270. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1099-1174/ .

    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.