Multisourcing supply network design: Two-stage chance-constrained model, tractable approximations, and computational results

Yongzhen Li, Jia Shu, Miao Song, Jiawei Zhang, Huan Zheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)287-300
Number of pages14
JournalINFORMS Journal on Computing
Volume29
Issue number2
DOIs
Publication statusPublished - 1 Mar 2017

Keywords

  • Chance constraint approximation
  • Multisourcing
  • Network design
  • Process flexibility

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research

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