IDEAS home Printed from https://ideas.repec.org/a/zib/zbmecj/v1y2017i1p1-6.html
   My bibliography  Save this article

Ant Colony Algorithm And Multi-Agent-Based Production Scheduling Optimization Model

Author

Listed:
  • Jinghua zhao

    (School of Management, University of Shanghai for Science and Technology,200093,China)

  • Jie Lin

    (School of Economics and Management, Tongji University, 200092 Shanghai, China)

  • Xia Zhao

    (Department of Information Systems and Operations Management, University of North Carolina at Greensboro, Greensboro, NC 27402, USA)

Abstract

In the dynamic production environment of supply chain, based on information sharing among enterprises of supply chain, this paper puts forward a production scheduling optimization model for supply chain. It transforms the choice of cooperative enterprises and their process ordering into the choice of path in graph theory. With respect to the complexity of model solving, this paper designs an expert system aided multi-agent intelligent ant colony algorithm to solve the production scheduling optimization model， where ant colony is constructed with multi-agent and the order decomposition structure and constraint are expressed by expert system. Also, supply chain production scheduling optimization prototype architecture is implemented and the related technologies are given in the paper. Finally using the system，several experiments are conducted，which show that both of the model and the algorithm are effective and feasible.

Suggested Citation

  • Jinghua zhao & Jie Lin & Xia Zhao, 2017. "Ant Colony Algorithm And Multi-Agent-Based Production Scheduling Optimization Model," Malaysian E Commerce Journal (MECJ), Zibeline International Publishing, vol. 1(1), pages 1-6, January.
    • Handle: RePEc:zib:zbmecj:v:1:y:2017:i:1:p:1-6
    DOI: 10.26480/mecj.01.2017.01.06
    as

    Download full text from publisher

    File URL: https://myecommerecejournal.com/download/1649/
    Download Restriction: no
    File URL: https://libkey.io/10.26480/mecj.01.2017.01.06?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. Hung, Wing Yan & Samsatli, Nouri J. & Shah, Nilay, 2006. "Object-oriented dynamic supply-chain modelling incorporated with production scheduling," European Journal of Operational Research, Elsevier, vol. 169(3), pages 1064-1076, March.
    2. P Tormos & A Lova, 2001. "Tools for resource-constrained project scheduling and control: forward and backward slack analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(7), pages 779-788, July.
    3. Mula, Josefa & Peidro, David & Díaz-Madroñero, Manuel & Vicens, Eduardo, 2010. "Mathematical programming models for supply chain production and transport planning," European Journal of Operational Research, Elsevier, vol. 204(3), pages 377-390, August.
    4. Stephan Kreipl & Jörg Dickersbach, 2008. "Scheduling coordination problems in supply chain planning," Annals of Operations Research, Springer, vol. 161(1), pages 103-122, July.
    5. Lodree, Emmett Jr., 2007. "Advanced supply chain planning with mixtures of backorders, lost sales, and lost contract," European Journal of Operational Research, Elsevier, vol. 181(1), pages 168-183, August.
    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. Ba, Birome Holo & Prins, Christian & Prodhon, Caroline, 2016. "Models for optimization and performance evaluation of biomass supply chains: An Operations Research perspective," Renewable Energy, Elsevier, vol. 87(P2), pages 977-989.
    2. S.R. Patterson & E. Kozan & P. Hyland, 2016. "An integrated model of an open-pit coal mine: improving energy efficiency decisions," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4213-4227, July.
    3. Schmid, Verena & Doerner, Karl F. & Laporte, Gilbert, 2013. "Rich routing problems arising in supply chain management," European Journal of Operational Research, Elsevier, vol. 224(3), pages 435-448.
    4. Lodree Jr., Emmett J. & Uzochukwu, Benedict M., 2008. "Production planning for a deteriorating item with stochastic demand and consumer choice," International Journal of Production Economics, Elsevier, vol. 116(2), pages 219-232, December.
    5. Ivanov, Dmitry & Sokolov, Boris, 2013. "Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty," European Journal of Operational Research, Elsevier, vol. 224(2), pages 313-323.
    6. Chan, Chi Kin & Lee, Y.C.E. & Campbell, J.F., 2013. "Environmental performance—Impacts of vendor–buyer coordination," International Journal of Production Economics, Elsevier, vol. 145(2), pages 683-695.
    7. Zhou, Quan Spring & Olsen, Tava Lennon, 2018. "Rotating the medical supplies for emergency response: A simulation based approach," International Journal of Production Economics, Elsevier, vol. 196(C), pages 1-11.
    8. V. Pando & L. San-José & J. García-Laguna & J. Sicilia, 2014. "Some general properties for the newsboy problem with an extraordinary order," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 674-693, July.
    9. Soto-Silva, Wladimir E. & Nadal-Roig, Esteve & González-Araya, Marcela C. & Pla-Aragones, Lluis M., 2016. "Operational research models applied to the fresh fruit supply chain," European Journal of Operational Research, Elsevier, vol. 251(2), pages 345-355.
    10. Emenike, Scholastica N. & Falcone, Gioia, 2020. "A review on energy supply chain resilience through optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    11. Quetschlich, Mathias & Moetz, André & Otto, Boris, 2021. "Optimisation model for multi-item multi-echelon supply chains with nested multi-level products," European Journal of Operational Research, Elsevier, vol. 290(1), pages 144-158.
    12. Aleksandr Rakhmangulov & Olesya Kopylova, 2014. "Assessment of socio-economic potential of regions for placement of the logistic infrastructure objects," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(2), pages 254-263.
    13. Schuster Puga, Matías & Minner, Stefan & Tancrez, Jean-Sébastien, 2019. "Two-stage supply chain design with safety stock placement decisions," International Journal of Production Economics, Elsevier, vol. 209(C), pages 183-193.
    14. Heckmann, Iris & Comes, Tina & Nickel, Stefan, 2015. "A critical review on supply chain risk – Definition, measure and modeling," Omega, Elsevier, vol. 52(C), pages 119-132.
    15. Xiaoqing Liu & Felix T. S. Chan & Xinsheng Xu, 2019. "Hedging Risks in the Loss-Averse Newsvendor Problem with Backlogging," Mathematics, MDPI, vol. 7(5), pages 1-23, May.
    16. Vipin, B. & Amit, R.K., 2017. "Loss aversion and rationality in the newsvendor problem under recourse option," European Journal of Operational Research, Elsevier, vol. 261(2), pages 563-571.
    17. Annika Becker & Raik Stolletz & Thomas Stäblein, 2017. "Strategic ramp-up planning in automotive production networks," International Journal of Production Research, Taylor & Francis Journals, vol. 55(1), pages 59-78, January.
    18. Calvete, Herminia I. & Galé, Carmen & Iranzo, José A., 2014. "Planning of a decentralized distribution network using bilevel optimization," Omega, Elsevier, vol. 49(C), pages 30-41.
    19. Pereira, Daniel Filipe & Oliveira, José Fernando & Carravilla, Maria Antónia, 2020. "Tactical sales and operations planning: A holistic framework and a literature review of decision-making models," International Journal of Production Economics, Elsevier, vol. 228(C).
    20. Krystel K. Castillo-Villar, 2014. "Metaheuristic Algorithms Applied to Bioenergy Supply Chain Problems: Theory, Review, Challenges, and Future," Energies, MDPI, vol. 7(11), pages 1-33, November.

    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:zib:zbmecj:v:1:y:2017:i:1:p:1-6. 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: Zibeline International Publishing The email address of this maintainer does not seem to be valid anymore. Please ask Zibeline International Publishing to update the entry or send us the correct address (email available below). General contact details of provider: http://myecommerecejournal.com/ .

    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.