Bidding-based multi-agent system for integrated process planning and scheduling: A data-mining and hybrid tabu-SA algorithm-oriented approach

Sanjay Kumar Shukla, M. K. Tiwari, Young Jun Son

Research output: Contribution to journalArticle

44 Scopus citations

Abstract

This paper conceptualizes a bidding-based multi-agent system for solving integrated process-planning and scheduling problem. The proposed architecture consists of various autonomous agents capable of communicating (bidding) with each other and making decisions based on their knowledge. Moreover, in contrast to the traditional model of integrated process-planning and scheduling problem, a new paradigm has been conceptualized by considering tool cost as a dynamic quantity rather than a constant. Tool cost is assumed to comprise tool-using cost and its repairing cost. The repairing cost is considered to depend on the tool-breaking probability, which is predicted by the data-mining agent equipped with the virtues of C-fuzzy decision tree. When a job arrives at the shop floor, the component agent announces a bid for one feature at a time to all the machine agents. Among the machine agents capable of producing the first feature, one comes forward to become a "leader", and groups other machine agents for the processing of remaining features of the job. Once all features are assigned to the appropriate machines, the leader then sends this allocation information to the optimization agent. The optimization agent finds optimal/near-optimal process plans and schedules via the hybrid tabu-SA algorithm.

Original languageEnglish (US)
Pages (from-to)163-175
Number of pages13
JournalInternational Journal of Advanced Manufacturing Technology
Volume38
Issue number1-2
DOIs
StatePublished - Jul 1 2008

Keywords

  • C-fuzzy decision trees
  • Dynamic tool cost
  • Hybrid tabu-SA algorithm
  • Integrated process planning and scheduling
  • Multi-agent system

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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