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

• Published in: Computers & operations research Vol. 49; pp. 47 - 58
• Main Authors: Oliveira, F., Grossmann, I.E., Hamacher, S.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-09-2014; Elsevier; Pergamon Press Inc
• Subjects: Acceleration; Acceleration techniques; Algorithms; Applied sciences; Comparative studies; Computation; Crude oil, natural gas and petroleum products; Energy; Exact sciences and technology; Fuels; Logistics; Mathematical models; Mathematical problems; Mathematical programming; Operational research and scientific management; Operational research. Management science; Petroleum products; Petroleum products, gas and fuels. Motor fuels, lubricants and asphalts; Portfolio theory; Programming; Stochastic Benders decomposition; Stochastic models; Stochastic programming; Stochasticity; Supply chain investment planning; Supply chain management; Supply chains; Supply chain investment planning; Stochastic Benders decomposition; Acceleration techniques; Stochastic programming; Stochastic model; Partition method; Linear programming; Experimental study; Oil industry; Planning; Stochastic Décomposition; Investment; Logistics
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2014.03.021

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.