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首页> 外文期刊>4OR: Quarterly Journal of the Belgian, French and Italian Operations Research Societies >Comparing stage-scenario with nodal formulation for multistage stochastic problems
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Comparing stage-scenario with nodal formulation for multistage stochastic problems

机译:比较stage-scenario和节点对于多级随机问题

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摘要

To solve real life problems under uncertainty in Economics, Finance, Energy, Transportation and Logistics, the use of stochastic optimization is widely accepted and appreciated. However, the nature of stochastic programming leads to a conflict between adaptability to reality and tractability. To formulate a multistage stochastic model, two types of formulations are typically adopted: the so-called stage-scenario formulation named also formulation with explicit non-anticipativity constraints and the so-called nodal formulation named also formulation with implicit non-anticipativity constraints. Both of them have advantages and disadvantages. This work aims at helping the scholars and practitioners to understand the two types of notation and, in particular, to reformulate with the nodal formulation a model that was originally defined with the stage-scenario formulation presenting this implementation in the algebraic language GAMS. In addition, this work presents an empirical analysis applying the two formulations both without any further decomposition to perform a fair comparison. In this way, we show that the difficulties to implement the model with the nodal formulation are somehow reworded making the problem tractable without any decomposition algorithm. Still, we remark that in some other applications the stage-scenario formulation could be more helpful to understand the structure of the problem since it allows to relax the non-anticipativity constraints.
机译:解决现实生活问题的不确定性经济学、金融、能源、交通和物流、随机优化的使用被广泛接受和欣赏。随机规划导致的性质适应现实之间的冲突温顺。随机模型,两种类型的配方通常采用:所谓的stage-scenario制定命名还与显式公式non-anticipativity约束和所谓的节点制定命名也制定隐式non-anticipativity约束。他们有优点和缺点。旨在帮助学者和实践者理解这两种类型的符号,在具体来说,就是将节点制定一个最初定义的模型stage-scenario配方呈现这个实现代数语言gam。实证分析应用这两个配方都没有任何进一步的分解执行一个公平的比较。困难来实现的模型节点配方是改的问题变得易于处理的没有任何分解算法。应用stage-scenario配方更有助于理解的结构因为它可以放松的问题non-anticipativity约束。

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