Joel L. Wolf, Mark S. Squillante, et al.
IEEE Transactions on Knowledge and Data Engineering
Business process modeling is a well established methodology for analyzing and optimizing complex processes. To address critical challenges in ubiquitous black-box approaches, we develop a two-stage business process optimization framework. The first stage is based on an analytical approach that exploits structural properties of the underlying stochastic network and renders a near-optimal solution. Starting from this candidate solution, the second stage employs advanced simulation optimization to locally search for optimal business process solutions. Numerical experiments demonstrate the efficacy of our approach. © 2013 IEEE.
Joel L. Wolf, Mark S. Squillante, et al.
IEEE Transactions on Knowledge and Data Engineering
K. Warren, R. Ambrosio, et al.
IBM J. Res. Dev
J.R.M. Hosking, Ramesh Natarajan, et al.
Appl Stochastic Models Bus Indus
Kyomin Jung, Yingdong Lu, et al.
Mathematics of Operations Research