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Title: Multisourcing supply network design : two-stage chance-constrained model, tractable approximations, and computational results
Authors: Li, Y
Shu, J
Song, M 
Zhang, J
Zheng, H
Issue Date: 2017
Source: Informs journal on computing, Spring 2017, v. 29, no. 2, p. 287-300
Abstract: In this paper, we study a multisourcing supply network design problem, in which each retailer faces uncertain demand and can source products from more than one distribution center (DC). The decisions to be simultaneously optimized include DC locations and inventory levels, which set of DCs serves each retailer, and the amount of shipments from DCs to retailers. We propose a nonlinear mixed integer programming model with a joint chance constraint describing a certain service level. Two approaches-set- wise approximation and linear decision rule-based approximation-are constructed to robustly approximate the service level chance constraint with incomplete demand information. Both approaches yield sparse multisourcing distribution networks that effectively match uncertain demand using on-hand inventory, and hence successfully reach a high service level. We show through extensive numerical experiments that our approaches outperform other commonly adopted approximations of the chance constraint.
Keywords: Multisourcing
Network design
Chance constraint approximation
Process flexibility
Publisher: INFORMS
Journal: Informs journal on computing 
ISSN: 1091-9856
EISSN: 1526-5528
DOI: 10.1287/ijoc.2016.0730
Rights: © 2017 INFORMS
This is the accepted manuscript of the following article: Li, Y., Shu, J., Song, M., Zhang, J., & Zheng, H. (2017). Multisourcing supply network design: two-stage chance-constrained model, tractable approximations, and computational results. INFORMS Journal on Computing, 29(2), 287-300, which has been published in final form at
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