Workflow scheduling using multi-agent systems in a dynamically changing environment

The application of intelligent agent technologies is considered a promising approach to improve system performance in complex and changeable environments. Especially, in the case of unforeseen events, for example, machine breakdowns that usually lead to a deviation from the initial production schedule, a multi-agent approach can be used to enhance system flexibility and robustness. In this paper we apply this approach to revise and re-optimize the dynamic system schedule in response to unexpected events. We employ Multi-Agent System simulation to optimize the total system output (eg, number of finished products) for recovery from machine and/or conveyor failure cases. Diverse types of failure classes (conveyor and machine failures), as well as duration of failures are used to test a range of dispatching rules in combination with the All Rerouting re-scheduling policy, which showed supreme performance in our previous studies. In this context, the Critical Ratio rule, which includes the transportation time into the calculation for the selection of the next job, outperformed all other dispatching rules. We also analysed the impact of diverse simulation parameters (such as number of pallets, class of conveyor failure and class of machine failure) on the system effectiveness. Presented research also enlightens the economic interdependencies between the examined parameters and the benefits of using the agent paradigm to minimize the impact of the disrupting events on the dynamic system.