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1School of Business, Renmin University of China, Beijing, China 100872; 2International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, Anhui, China 230026; 3College of Engineering, University of California, Berkeley, California 94704; Faculty of Engineering, The University of Hong Kong, Hong Kong 999077, China; Faculty of Business and Economics, The University of Hong Kong, Hong Kong 999077, China,
We address the reliable facility location problem in a data-driven setting by presenting a model aiming to balance solution conservatism with efficiency. In particular, our model approximates the total cost by a tractable data-driven estimator, which equals to a probabilistic upper bound on the intractable Kolmogorov DRO estimator. Our approach is proved to be asymptotically optimal, and offers a theoretical guarantee for its out-of-sample performance in situations with limited data.
A Random Model of Supply Chain Networks with an Application to the Guaranteed Service Model
Philippe Blaettchen1, Andre Calmon2, Georgina Hall3
1Bayes Business School (formerly Cass), City, University of London, United Kingdom; 2Scheller College of Business, Georgia Institute of Technology, United States; 3INSEAD, France
Supply chain models often receive little testing due to a lack of data. We propose a random model of supply chain networks to overcome this problem and establish that it generates accurate representations of real networks. Generated networks' treewidth is logarithmic in the number of firms, which has important implications for tractability. We illustrate this with the NP-hard guaranteed service model, showing a pseudo-polynomial time algorithm for networks with logarithmic treewidth.
Modeling supply chain network with semiparametric matrix variate factor models
Zhaocheng Zhang1, Weichen Wang2, Jing Wu3
1Faculty of Economics, University of Cambridge; 2HKU Business School, the University of Hong Kong; 3CUHK Business School, the Chinese University of Hong Kong
This paper proposes an empirical framework for analyzing the evolution patterns of supply chain networks over time. Using a semiparametric matrix variate factor model, we investigate the latent lower-dimensional structure of the network dynamics and the loading matrices that connect the underlying latent factors with the surface supply chain networks and characterize the latent nodes. Our findings shed light on the latent structure, centrality, trends, and patterns of supply chain networks.
