Ant Colony Algorithm And Multi-Agent-Based Production Scheduling Optimization Model
Jie Lin and
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Jinghua Zhao: School of Management, University of Shanghai for Science and Technology,200093,China
Jie Lin: School of Economics and Management, Tongji University, 200092 Shanghai, China
Xia Zhao: Department of Information Systems and Operations Management, University of North Carolina at Greensboro, Greensboro, NC 27402, USA
Malaysian E Commerce Journal (MECJ), 2017, vol. 1, issue 1, 1-6
In the dynamic production environment of supply chain, based on information sharing among enterprises of supply chain, this paper puts forward a production scheduling optimization model for supply chain. It transforms the choice of cooperative enterprises and their process ordering into the choice of path in graph theory. With respect to the complexity of model solving, this paper designs an expert system aided multi-agent intelligent ant colony algorithm to solve the production scheduling optimization model， where ant colony is constructed with multi-agent and the order decomposition structure and constraint are expressed by expert system. Also, supply chain production scheduling optimization prototype architecture is implemented and the related technologies are given in the paper. Finally using the system，several experiments are conducted，which show that both of the model and the algorithm are effective and feasible.
Keywords: expert system; ant colony algorithm; production scheduling optimization; multi-agentJournal: Malaysian E Commerce Journal (MECJ) (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:zib:zbmecj:v:1:y:2017:i:1:p:1-6
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