Agent-Based Modeling and Simulation of Intelligent Distributed Scheduling Systems

For responsiveness and agility, disruptive events must be managed locally to avoid propagating the effects along the value chain. In this work, a novel approach based on emergent distributed scheduling is proposed to overcome the traditional separation between task scheduling and execution control. An interaction mechanism designed around the concept of order and resource agents acting as autonomic managers is described. The proposed Manufacturing Execution System (MES) for simultaneous distributed (re)scheduling and local execution control is able to reject disturbances and successfully handle unforeseen events by autonomic agents implementing the monitor-analyze-plan-execution loop while achieving their corresponding goals. For detailed design of the autonomic MES and verification of its emergent behaviors, a goal-oriented methodology for designing interactions is proposed. Encouraging results obtained for different operating scenarios using a generative simulation model of the interaction mechanism implemented in Netlogo are presented.