IDEAS home Printed from https://ideas.repec.org/a/igg/jisscm/v10y2017i3p84-95.html
   My bibliography  Save this article

Multi-Agent Reinforcement Learning for Value Co-Creation of Collaborative Transportation Management (CTM)

Author

Listed:
  • Liane Okdinawati

    (Bandung Institute of Technology, School of Business and Management, Bandung, Indonesia)

  • Togar M. Simatupang

    (Bandung Institute of Technology, School of Business and Management, Bandung, Indonesia)

  • Yos Sunitiyoso

    (Bandung Institute of Technology, School of Business and Management, Bandung, Indonesia)

Abstract

Collaborative Transportation Management (CTM) is a collaboration model in transportation area. The use of CTM in today's business process is to create efficiency in transportation planning and execution processes. However, previous research paid little attention to demonstrate the ability for all agents in CTM to co-create value in services. The purpose of this paper is to increase the understanding of value co-creation in CTM area and learning processes in real systems based on value co-creation of CTM. Multiple case studies were used to analyze the value that was perceived by all agents in CTM in each collaboration stage and provided empirical evidence on the interactions among agents. Model-free reinforcement learning was used to predict how CTM could reduce transportation cost, increase visibility, and improve agility. The simulation results show that the input, feedback, and the experience of the agents are used to structure the collaboration processes and determine the strategies.

Suggested Citation

  • Liane Okdinawati & Togar M. Simatupang & Yos Sunitiyoso, 2017. "Multi-Agent Reinforcement Learning for Value Co-Creation of Collaborative Transportation Management (CTM)," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 10(3), pages 84-95, July.
    • Handle: RePEc:igg:jisscm:v:10:y:2017:i:3:p:84-95
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJISSCM.2017070105
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Jun Zhang & Shuyang Li & Yichuan Wang, 2023. "Shaping a Smart Transportation System for Sustainable Value Co-Creation," Information Systems Frontiers, Springer, vol. 25(1), pages 365-380, February.

    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:igg:jisscm:v:10:y:2017:i:3:p:84-95. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.