Agent-Based Generative Simulation of an Intelligent Distributed Scheduling World with Netlogo
Abstract
Unplanned disruptive events and disturbances such as arrivals of rush orders or machine breakdowns must be
managed locally to avoid propagating the effects along the value chain. To overcome the traditional separation
between task scheduling and manufacturing execution systems the novel idea of emergent synthesis/control of
schedules for better handling the dynamics at the shop-floor is proposed. A new interaction mechanism for
simultaneous distributed scheduling and execution control is evaluated using a generative simulation model in
Netlogo. The interaction mechanism has been designed around the concept of order and resource agents acting as
autonomic managers within the artificial society of a dynamic Gantt world. The advantages of generative modelling
in agent-based simulation are discussed to emphasize how difficult to predict emerging behaviours and bottom-up
macroscopic dynamics in a manufacturing case study can be addressed by proper design of agent interactions.
Results obtained for different abnormal scenarios are presented to highlight the benefits of simulating artificial
societies of intelligent agents.
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