Algorithmical approaches to business process design
The paper introduces formal models of the business process design problem, which can be used to analytically determine optimal designs with respect to various objective functions. Specifically, we consider additive and maxmin type functions. Three solution techniques for such models are analyzed: mathematical programming, a branch and bound method, and genetic algorithms. Computational experiments indicate that among those methods, the branch and bound method is best suited to solve problems of realistic size. While there are several methodologies for structuring Business process redesign (BPR) projects, the task of developing optimal designs of business processes is left to the designer's intuition. The present paper studies possibilities to provide analytical support for optimizing the design of (mainly administrative) business processes. Several solution approaches are compared and initial tests show that problems of realistic size can be solved in reasonable time.
process design branch and bound genetic algorithms combinatorial optimization mixed integer programming business process reengineering