MAS Equipped with Ant Colony Applied into Dynamic Job Shop Scheduling

Authors:
Kai Kang;Ren Feng Zhang;Yan Qing Yang
Affiliations:
School of Management, Hebei University of Technology, Tianjin, 300401, China;School of Management, Hebei University of Technology, Tianjin, 300401, China;School of Management, Hebei University of Technology, Tianjin, 300401, China
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

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Abstract

This paper presents a methodology adopting the new structure of MAS(multi-agent system) equipped with ACO(ant colony optimization) algorithm for a better schedule in dynamic job shop. In consideration of the dynamic events in the job shop arriving indefinitely schedules are generated based on tasks with ant colony algorithm. Meanwhile, the global objective is taken into account for the best solution in the actual manufacturing environment. The methodology is tested on a simulated job shop to determine the impact with the new structure.