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Reinforcement Learning Strategy for Solving the Resource-Constrained Project Scheduling Problem by a Team of A-Teams

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Intelligent Information and Database Systems (ACIIDS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8398))

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Abstract

In this paper the Team of A-Teams for solving the resource-constrained project scheduling problem (RCPSP) using the reinforcement learning interactive strategy is proposed. RCPSP belongs to the NP-hard problem class. To solve this problem a parallel cooperating A-Teams consisting of the asynchronous agents implemented using JABAT middleware have been proposed. Within each of the A-Team the interaction strategy using reinforcement learning is used. To evaluate the proposed approach computational experiment has been carried out.

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Author information

Authors and Affiliations

  1. Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225, Gdynia, Poland

    Piotr Jędrzejowicz & Ewa Ratajczak-Ropel

Authors
  1. Piotr Jędrzejowicz
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  2. Ewa Ratajczak-Ropel
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Editor information

Editors and Affiliations

  1. Institute of Informatics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Ngoc Thanh Nguyen  & Bogdan Trawiński  & 

  2. Faculty of Engineering, Department of Computer Engineering, King Mongkut’s Institute of Technology Ladkrabang, 10520, Bangkok, Thailand

    Boonwat Attachoo

  3. Software Engineering Program, King Mongkut’s Institute of Technology Ladkrabang, 10520, Bangkok, Thailand

    Kulwadee Somboonviwat

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Jędrzejowicz, P., Ratajczak-Ropel, E. (2014). Reinforcement Learning Strategy for Solving the Resource-Constrained Project Scheduling Problem by a Team of A-Teams. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8398. Springer, Cham. https://doi.org/10.1007/978-3-319-05458-2_21

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  • DOI: https://doi.org/10.1007/978-3-319-05458-2_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05457-5

  • Online ISBN: 978-3-319-05458-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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