title: Learning to Select Supplier Portfolios for Service Supply Chain.
abstract: The research on service supply chain has attracted more and more focus from both academia and industrial community. In a service supply chain, the selection of supplier portfolio is an important and difficult problem due to the fact that a supplier portfolio may include multiple suppliers from a variety of fields. To address this problem, we propose a novel supplier portfolio selection method based on a well known machine learning approach, i.e., Ranking Neural Network (RankNet). In the proposed method, we regard the problem of supplier portfolio selection as a ranking problem, which integrates a large scale of decision making features into a ranking neural network. Extensive simulation experiments are conducted, which demonstrate the feasibility and effectiveness of the proposed method. The proposed supplier portfolio selection model can be applied in a real corporation easily in the future.
authors.affiliation: College of Management and Economics, Tianjin University, Tianjin, China.|School of Computer Science and Technology, Hulunbuir College, Hulunbuir, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Zhang
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin, China.
authors.forename: Jingfei
authors.initials: J
authors.lastname: Li
authors.affiliation: College of Management and Economics, Tianjin University, Tianjin, China.
authors.forename: Shaoyu
authors.initials: S
authors.lastname: Wu
authors.affiliation: School of Economics, Tianjin University of Commerce, Tianjin, China.
authors.forename: Dabin
authors.initials: D
authors.lastname: Meng
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003199:Computer Systems; D063045:Crowdsourcing; D003657:Decision Making; D000069550:Machine Learning; D018803:Models, Economic; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests