Combinatorial auction method for decentralized task assignment of multiple-loading capacity AGV based on intelligent agent architecture

Muhammad Hafidz Fazli Bin Md Fauadi, Wan Ling Li, Tomohiro Murata

Research output: Contribution to conferencePaper

5 Citations (Scopus)

Abstract

Distributed manufacturing system has steadily becoming more significant for topics for researchers within manufacturing industry. It is due to the reason that it could provide higher flexibility for an organization to cope with a demanding and fluctuating market. This paper proposes distributed task assignment mechanism for multi-load Automated Guided Vehicle (AGV) operation in manufacturing industry. The architecture is based on multi-agent system (MAS) while the task assignment is based on combinatorial auction method. Additionally, necessary improvement have been made to the standard combinatorial auction method to support dynamic attributes of AGV task assignment mechanism.

Original languageEnglish
Pages207-211
Number of pages5
DOIs
Publication statusPublished - 2011 Dec 1
Event2011 2nd International Conference on Innovations in Bio-inspired Computing and Applications, IBICA 2011 - Shenzhen, Guangdong, China
Duration: 2011 Dec 162011 Dec 18

Conference

Conference2011 2nd International Conference on Innovations in Bio-inspired Computing and Applications, IBICA 2011
CountryChina
CityShenzhen, Guangdong
Period11/12/1611/12/18

Keywords

  • autonomous AGV
  • combinatorial auction
  • intelligent agent
  • multi-load capacity

ASJC Scopus subject areas

  • Biotechnology
  • Computational Theory and Mathematics
  • Computer Science Applications

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    Fauadi, M. H. F. B. M., Li, W. L., & Murata, T. (2011). Combinatorial auction method for decentralized task assignment of multiple-loading capacity AGV based on intelligent agent architecture. 207-211. Paper presented at 2011 2nd International Conference on Innovations in Bio-inspired Computing and Applications, IBICA 2011, Shenzhen, Guangdong, China. https://doi.org/10.1109/IBICA.2011.56