Abstract
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.
Highlights
The use of large-scale, complex mixed integer linear programming (MILP) in the context of investment planning for supply chain problems is becoming more widespread
In this paper we have presented the development of acceleration techniques for the stochastic Benders decomposition to solve an investment planning problem applied to the petroleum products supply chain
We have proposed a new methodology for generating dynamically updated near-maximal Benders cuts, and compared it with acceleration techniques proposed by Papadakos [16] and Sherali and Lunday [25] for the stochastic Benders algorithm
Summary
The use of large-scale, complex mixed integer linear programming (MILP) in the context of investment planning for supply chain problems is becoming more widespread. Under certain conditions, the traditional Benders decomposition (and its stochastic version) might fail to achieve the aforementioned efficiency, a fact that has been broadly mentioned in the literature (see, for example Rei et al [19], Saharidis et al [23]) To circumvent this drawback various strategies have been proposed for accelerating Benders decomposition. Cote and Laughton [5] showed another approach for accelerating Benders algorithms In their approach, the MP is not solved to optimality but only the first integer solution obtained is used to generate the optimality or feasibility cut from the SP. The proposed techniques addresses two different aspects in terms of algorithmic acceleration, since they aim at generating stronger cuts for the Benders decomposition in the context of stochastic programming, and they apply techniques for improving the quality of solutions obtained during the algorithm execution.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
