Accelerating Benders stochastic decomposition for the optimization under uncertainty of the petroleum product supply chain

Research output: Contribution to journalArticle


Research units

  • Pontifícia Universidade Católica do Rio de Janeiro
  • Carnegie Mellon University


This paper addresses the solution of a two-stage stochastic programming model for an investment planning problem applied to the petroleum products supply chain. In this context, we present the development of acceleration techniques for the stochastic Benders decomposition that aim to strengthen the cuts generated, as well as to improve the quality of the solutions obtained during the execution of the algorithm. Computational experiments are presented for assessing the efficiency of the proposed framework. We compare the performance of the proposed algorithm with two other acceleration techniques. Results suggest that the proposed approach is able to efficiently solve the problem under consideration, achieving better performance in terms of computational times when compared to other two techniques.


Original languageEnglish
Pages (from-to)47-58
Number of pages12
JournalComputers and Operations Research
Publication statusPublished - 2014
MoE publication typeA1 Journal article-refereed

    Research areas

  • Acceleration techniques, Stochastic Benders decomposition, Stochastic programming, Supply chain investment planning

ID: 16007839