2020/11/16 by Xiaoping Jiang, Jiang, Xiaoping, Ruibin Bai +5
Business, Management and Accounting · Decision Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Multi-Criteria Decision Making #Supply Chain and Inventory Management #Sustainable Supply Chain Management
paper · pdf · doi:10.48550/arxiv.2011.09890
openalex publication_date 2020/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Stochastic service network designs with uncertain demand represented by a set of scenarios can be modelled as a large-scale two-stage stochastic mixed-integer program (SMIP). The progressive hedging algorithm (PHA) is a decomposition method for solving the resulting SMIP. The computational performance of the PHA can be greatly enhanced by decomposing according to scenario bundles instead of individual scenarios. At the heart of bundle-based decomposition is the method for grouping the scenarios into bundles. In this paper, we present a fuzzy c-means-based scenario bundling method to address this problem. Rather than full membership of a bundle, which is typically the case in existing scenario bundling strategies such as k-means, a scenario has partial membership in each of the bundles and can be assigned to more than one bundle in our method.