Ant colony intelligence in multi-agent dynamic manufacturing scheduling

This study aims at building an efficient agent-based dynamic scheduling for real-world manufacturing systems with various products, processes, and disturbances. Ant colony intelligence (ACI) is proposed to be combined with local agent coordination so as to make autonomous agents adaptive to changing circumstances and to give rise to efficient global performance. The work here differs from other dynamic scheduling research in two areas: (1) a more generic and realistic manufacturing model with multiple product types, multiple/parallel multi-purpose machines with sequence-dependent setup constraints, and various dynamic disturbances is used, (2) ACI integrated with both machine agents and job agents to solve not only the task allocation problem, but also the task sequencing problem. The implementation of the aforementioned issues in a multi-agent system (MAS) is discussed. Simulation results show that, for most of the performance measures, a MAS integrated with well-designed ant-inspired coordination performs well compared to a MAS using dispatching rules.