TY - GEN
T1 - Action-Evolution Petri Nets
T2 - a Framework for Modeling and Solving Dynamic Task Assignment Problems
AU - Lo Bianco, Riccardo
AU - Dijkman, Remco M.
AU - Nuijten, Wim P.M.
AU - van Jaarsveld, Willem L.
PY - 2023/9/1
Y1 - 2023/9/1
N2 - Dynamic task assignment involves assigning arriving tasks to a limited number of resources in order to minimize the overall cost of the assignments. To achieve optimal task assignment, it is necessary to model the assignment problem first. While there exist separate formalisms, specifically Markov Decision Processes and (Colored) Petri Nets, to model, execute, and solve different aspects of the problem, there is no integrated modeling technique. To address this gap, this paper proposes Action-Evolution Petri Nets (A-E PN) as a framework for modeling and solving dynamic task assignment problems. A-E PN provides a unified modeling technique that can represent all elements of dynamic task assignment problems. Moreover, A-E PN models are executable, which means they can be used to learn close-to-optimal assignment policies through Reinforcement Learning (RL) without additional modeling effort. To evaluate the framework, we define a taxonomy of archetypical assignment problems. We show for three cases that A-E PN can be used to learn close-to-optimal assignment policies. Our results suggest that A-E PN can be used to model and solve a broad range of dynamic task assignment problems.
AB - Dynamic task assignment involves assigning arriving tasks to a limited number of resources in order to minimize the overall cost of the assignments. To achieve optimal task assignment, it is necessary to model the assignment problem first. While there exist separate formalisms, specifically Markov Decision Processes and (Colored) Petri Nets, to model, execute, and solve different aspects of the problem, there is no integrated modeling technique. To address this gap, this paper proposes Action-Evolution Petri Nets (A-E PN) as a framework for modeling and solving dynamic task assignment problems. A-E PN provides a unified modeling technique that can represent all elements of dynamic task assignment problems. Moreover, A-E PN models are executable, which means they can be used to learn close-to-optimal assignment policies through Reinforcement Learning (RL) without additional modeling effort. To evaluate the framework, we define a taxonomy of archetypical assignment problems. We show for three cases that A-E PN can be used to learn close-to-optimal assignment policies. Our results suggest that A-E PN can be used to model and solve a broad range of dynamic task assignment problems.
KW - Business Process Optimization
KW - Dynamic Assignment Problem
KW - Markov Decision Processes
KW - Petri Nets
KW - Reinforcement Learning
UR - http://www.scopus.com/inward/record.url?scp=85172225523&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-41620-0_13
DO - 10.1007/978-3-031-41620-0_13
M3 - Conference contribution
SN - 978-3-031-41619-4
T3 - Lecture Notes in Computer Science (LNCS)
SP - 216
EP - 231
BT - Business Process Management
A2 - Di Francescomarino, Chiara
A2 - Burattin, Andrea
A2 - Janiesch, Christian
A2 - Sadiq, Shazia
PB - Springer
CY - Cham
ER -