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Dynamic task assignment involves assigning arriving tasks to a limited numberof resources in order to minimize the overall cost of the assignments. Toachieve optimal task assignment, it is necessary to model the assignmentproblem first. While there exist separate formalisms, specifically MarkovDecision Processes and (Colored) Petri Nets, to model, execute, and solvedifferent aspects of the problem, there is no integrated modeling technique. Toaddress this gap, this paper proposes Action-Evolution Petri Nets (A-E PN) as aframework for modeling and solving dynamic task assignment problems. A-E PNprovides a unified modeling technique that can represent all elements ofdynamic task assignment problems. Moreover, A-E PN models are executable, whichmeans they can be used to learn close-to-optimal assignment policies throughReinforcement Learning (RL) without additional modeling effort. To evaluate theframework, we define a taxonomy of archetypical assignment problems. We showfor three cases that A-E PN can be used to learn close-to-optimal assignmentpolicies. Our results suggest that A-E PN can be used to model and solve abroad range of dynamic task assignment problems.

Action-Evolution Petri Nets: a Framework for Modeling and Solving Dynamic Task Assignment Problems