Risk management for a global supply chain planning under uncertainty: Models and algorithms

Fengqi You, John M. Wassick, Ignacio E. Grossmann

Research output: Contribution to journalArticlepeer-review

167 Scopus citations

Abstract

In this article, we consider the risk management for mid-term planning of a global multi-product chemical supply chain under demand and freight rate uncertainty. A two-stage stochastic linear programming approach is proposed within a multi-period planning model that takes into account the production and inventory levels, transportation modes, times of shipments, and customer service levels. To investigate the potential improvement by using stochastic programming, we describe a simulation framework that relies on a rolling horizon approach. The studies suggest that at least 5% savings in the total real cost can be achieved compared with the deterministic case. In addition, an algorithm based on the multi-cut L-shaped method is proposed to effectively solve the resulting large scale industrial size problems. We also introduce risk management models by incorporating risk measures into the stochastic programming model, and multi-objective optimization schemes are implemented to establish the tradeoffs between cost and risk. To demonstrate the effectiveness of the proposed stochastic models and decomposition algorithms, a case study of a realistic global chemical supply chain problem is presented.

Original languageEnglish (US)
Pages (from-to)931-946
Number of pages16
JournalAICHE Journal
Volume55
Issue number4
DOIs
StatePublished - Apr 1 2009

Keywords

  • Multicut L-shaped method
  • Risk management
  • Simulation
  • Stochastic programming
  • Supply chain management

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Biotechnology
  • Environmental Engineering

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