Journal
COMPUTERS & INDUSTRIAL ENGINEERING
Volume 59, Issue 1, Pages 166-180Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2009.09.003
Keywords
Process planning and scheduling; Ant colony optimization; Multi-agent system
Funding
- Research Grants Council of Hong Kong [HKU 7208/03E]
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This paper presents an ant colony optimization (ACO) algorithm in an agent-based system to integrate process planning and shopfloor scheduling (IPPS). The search-based algorithm which aims to obtain optimal solutions by an autocatalytic process is incorporated into an established multi-agent system (MAS) platform, with advantages of flexible system architectures and responsive fault tolerance. Artificial ants are implemented as software agents. A graph-based solution method is proposed with the objective of minimizing makespan. Simulation studies have been established to evaluate the performance of the ant approach. The experimental results indicate that the ACO algorithm can effectively solve the IPPS problems and the agent-based implementation can provide a distributive computation of the algorithm. (C) 2009 Elsevier Ltd. All rights reserved.
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