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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Kang, K., Zhang, R. F., & Yang, Y. Q. (2007). MAS equipped with ant colony applied into dynamic job shop scheduling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4682 LNAI, pp. 823–835). Springer Verlag. https://doi.org/10.1007/978-3-540-74205-0_86
Mendeley helps you to discover research relevant for your work.