We propose a solution approach for stochastic network design problems with uncertain demands. We investigate how to efficiently use reduced cost information as a means of guiding variable fixing to define a restriction that reduces the complexity of solving the stochastic model without sacrificing the quality of the solution obtained. We then propose a matheuristic approach that iteratively defines and explores restricted regions of the global solution space that have a high potential of containing good solutions. Extensive computational experiments show the effectiveness of the proposed approach in obtaining high-quality solutions, while reducing the computational effort to obtain them.

针对需求不确定的随机网络设计问题，我们提出了一种解决方案。我们研究如何有效地使用降低成本的信息作为指导变量固定的方法，以定义一个限制，该限制可降低求解随机模型的复杂性而又不牺牲获得的解决方案的质量。然后，我们提出一种数学方法，该方法可以迭代地定义和探索全局解决方案空间的受限区域，这些区域可能包含良好的解决方案。大量的计算实验证明了该方法在获得高质量解决方案方面的有效性，同时减少了获得解决方案的计算量。