Title: Decision support under uncertainties based on robust Bayesian networks in reverse logistics management
Author: Eduard Shevtshenko, Yan Wang
Department of Machinery, Tallinn University of Technology, Ehitajate tee 5, 19086, Tallinn, Estonia.
Department of Industrial Engineering & Management Systems, University of Central Florida, 4000 Central Florida Blvd., Orlando, Florida 32816-2993, USA
Journal: Int. J. of Computer Applications in Technology, 2009 Vol.36, No.3/4, pp.247 - 258
Abstract: One of the major challenges for product lifecycle management systems is the lack of integrated decision support tools to help decision-making with available information in collaborative enterprise networks. Uncertainties are inherent in such networks due to lack of perfect knowledge or conflicting information. In this paper, a robust decision support approach based on imprecise probabilities is proposed. Robust Bayesian belief networks with interval probabilities are used to estimate imprecise posterior probabilities in probabilistic inference. This generic approach is demonstrated with decision-makings in design for closed-loop supply chain. The ultimate goal of robust intelligent decision support systems is to enhance the effective use of information available in collaborative engineering environments.
Keywords: product lifecycle management; PLM; reverse logistics; interval analysis; imprecise probability; Bayesian networks; decision support; uncertainties; logistics management; collaborative networks; collaboration; belief networks; decision making; supply chain design; intelligent DSS; decision support systems; collaborative engineering; supply chain management; SCM.