Efficiency of task allocation based on contract net protocol with audience restriction in a manufacturing control application

Wing Lok YEUNG*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

11 Citations (Scopus)

Abstract

The contract net protocol (CNP) serves as a framework for cooperative distributed problem-solving agents to allocate tasks among themselves. Multi-agent systems (MAS) based on CNP are, however, susceptible to message congestion which hampers the efficiency of agents in making their task allocation decisions. Various attempts have been made to alleviate the problem. This article focuses on a particular solution, namely, audience restriction (AR). While AR has been incorporated into several CNP-based MAS, there is a lack of empirical evidence on its effectiveness in reducing message congestion and whether its contribution could be outweighed by its overheads. Furthermore, AR has a direct impact on the participation rate of agents in making task allocation decisions which has not been properly considered. By comparing the performance of a CNP-based task allocation algorithm with its AR variants in a simulation study in manufacturing control, our results show that, while AR can effectively be applied to reduce message traffic under heavy load conditions, there is also evidence that if audience size is only marginally reduced, message traffic can increase as well as decrease. The new findings contribute to more efficient CNP-based manufacturing control algorithms.

Original languageEnglish
Pages (from-to)1005-1017
Number of pages13
JournalInternational Journal of Computer Integrated Manufacturing
Volume31
Issue number10
Early online date12 Jul 2018
DOIs
Publication statusPublished - 3 Oct 2018

Bibliographical note

Publisher Copyright:
© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

Funding

This work was supported by the Lingnan University Direct Research Grant under Grant DR14A7.

Keywords

  • discrete event simulation
  • dynamic scheduling
  • Manufacturing control systems
  • multi-agent systems
  • shop-floor control

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