Business process management is a very promising paradigm for optimizing the way in which work is performed in an organization. Decisions that are always implicit in business processes include: assigning resources to the tasks for which they are most suited, deciding on the execution order of tasks to meet customer deadlines, and deciding on the overall design of the process to minimize the overall processing time of customer cases.
Traditionally, the research area of operations research has studied techniques for modeling and solving such optimization problems in much detail. At the same time, the research area of business process management has studied techniques for aggregating the data that is needed for modeling, analyzing, and in the end optimizing business processes. Consequently, combining techniques from both areas makes it possible to solve optimization problems from practice, using models that are based on real-world data, with fewer assumptions.
To look at those possibilities, this workshop aims to bring together researchers from both the area of Business Process Management and the area of Operations Research as well as other related areas, with the overall goal of developing techniques for optimizing business processes in an organization based on models that are created from real-world data.
 
Arik Senderovich (arik.senderovich@utoronto.ca)
Remco Dijkman (r.m.dijkman@tue.nl)
Willem van Jaarsveld (W.L.v.Jaarsveld@tue.nl)
 
Website: https://sites.google.com/view/bpo2022/

Call for Papers: https://uni-muenster.sciebo.de/s/p2yL12Mua9K21ou