A Lagrangean decomposition approach for oil supply chain investment planning under uncertainty with risk considerations

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • Pontificia Universidade Catolica do Rio de Janeiro
  • Carnegie Mellon University

Abstract

We present a scenario decomposition framework based on Lagrangean decomposition for the multi-product, multi-period, supply investment planning problem considering network design and discrete capacity expansion under demand uncertainty. We also consider a risk measure that allows to reduce the probability of incurring in high costs while preserving the decomposable structure of the problem. To solve the resulting large-scale two-stage mixed-integer stochastic linear programming problem we propose a novel Lagrangean decomposition scheme, and compare different formulations for the non-anticipativity conditions. In addition, we present a new hybrid algorithm for updating the Lagrangean multiplier set based on the combination of cutting-plane, subgradient and trust-region strategies. Numerical results suggest that different formulations of the non-anticipativity conditions have a significant impact on the performance of the algorithm. Moreover, we observe that the proposed hybrid approach has superior performance in terms of faster computational times when compared with the traditional subgradient algorithm.

Details

Original languageEnglish
Pages (from-to)184-195
Number of pages12
JournalComputers and Chemical Engineering
Volume50
Publication statusPublished - 5 Mar 2013
MoE publication typeA1 Journal article-refereed

    Research areas

  • Lagrangean decomposition, Oil and gas, Risk management, Scenario decomposition, Stochastic integer programming, Supply chain investment planning

ID: 16007869