Negotiating Agents in Manufacturing Decision Making Processes

Authors:
P. W. Lei;Malcolm I. Heywood;Chris R. Chatwin
Affiliations:
-;-;-
Venue:
E-Commerce Agents, Marketplace Solutions, Security Issues, and Supply and Demand
Year:
2001

Citing 13
Cited 0

Network-based heuristics for constraint-satisfaction problems

Artificial Intelligence
The ecology of computation

The ecology of computation
Rules of encounter: designing conventions for automated negotiation among computers

Rules of encounter: designing conventions for automated negotiation among computers
Using computerized exchange systems to solve an allocation problem in project management

Journal of Organizational Computing - Special issue: organizational computing coordination and collaboration
Market-based control: a paradigm for distributed resource allocation

Market-based control: a paradigm for distributed resource allocation
Metaphor or reality: a case study where agents BID with actual costs to schedule a factory

Market-based control
Machining task allocation in discrete manufacturing systems

Market-based control
Variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem

Artificial Intelligence
Digital library query clearing using clustering and fuzzy decision-making

Information Processing and Management: an International Journal
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver

IEEE Transactions on Computers
Resource allocation as an evolving strategy

Evolutionary Computation
A market-oriented programming environment and its application to distributed multicommodity flow problems

Journal of Artificial Intelligence Research
Maximizing sets and fuzzy Markoff algorithms

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Quantified Score

Hi-index	0.00

Visualization

Abstract

Real-time decision making for on-the-spot orders is considered using a novel distributed agent architecture. The order agents will have the ability to perform multi-stage negotiation using continuous double auction market-based model representing resources as producers and orders as customers. Qualitative information of resources and order requirements is captured in a Quality Function Definition (QFD) decision table. The QFD decision table also provides the interface for the agent to evaluate objectives, attributes and user preferences relating to resources and order requirements. This directs the negotiation against the most applicable resources at each stage through the market. In particular, the agents can learn from experience and adapt to the market change. The operation of such a system is illustrated within a garment industry application context.