Development of a hybrid negotiation scheme for multi-agent manufacturing systems

N. Kumar, M. K. Tiwari, Tung Sun Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

A hybrid inter-agent negotiation mechanism based on currency and a pre-emption control scheme is proposed to improve the performance of multi-agent manufacturing systems. The multi-agent system considered consists mainly of four types of agents: machine, clone, part and mediator. The machine agent controls the scheduling and the execution of a task. The clone agent aims to maximize the utilization rate by attracting relevant work to the machine. The part agent communicates with the machine agent or clone agent to acquire necessary production resources in order to get the required processing done, and the mediator agent contains the status of the part that will be processed by the subcontracting machine agent. The primary objective is to design decentralized control protocols for discrete part manufacturing systems to enhance the efficiency of the system and to allocate dynamically the resources to critical jobs based on the dynamic search tree. This research incorporates both the currency and the pre-emption schemes within a common framework. Currency functions are used to help the agents meet their individual objectives, whereas the pre-emption scheme is used to expedite the processing of parts based on their due dates. A dynamic search algorithm for the best route selection of different operations based on the job completion time is also proposed and it is implemented on a small manufacturing unit.
Original languageEnglish
Pages (from-to)539-569
Number of pages31
JournalInternational Journal of Production Research
Volume46
Issue number3
DOIs
Publication statusPublished - 1 Feb 2008
Externally publishedYes

Keywords

  • Dynamic search algorithm
  • Manufacturing systems
  • Multi-agent
  • Negotiation

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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