Process Dynamics and Operations Group, Department of Biochemical and Chemical Engineering, Technische Universität Dortmund, Dortmund, 44227, Germany.
Evol Comput. 2009 Winter;17(4):511-26. doi: 10.1162/evco.2009.17.4.17404.
In this contribution, we consider decision problems on a moving horizon with significant uncertainties in parameters. The information and decision structure on moving horizons enables recourse actions which correct the here-and-now decisions whenever the horizon is moved a step forward. This situation is reflected by a mixed-integer recourse model with a finite number of uncertainty scenarios in the form of a two-stage stochastic integer program. A stage decomposition-based hybrid evolutionary algorithm for two-stage stochastic integer programs is proposed that employs an evolutionary algorithm to determine the here-and-now decisions and a standard mathematical programming method to optimize the recourse decisions. An empirical investigation of the scale-up behavior of the algorithms with respect to the number of scenarios exhibits that the new hybrid algorithm generates good feasible solutions more quickly than a state of the art exact algorithm for problem instances with a high number of scenarios.
在本贡献中，我们考虑了具有显著参数不确定性的移动时窗上的决策问题。移动时窗上的信息和决策结构能够在移动时窗向前移动一步时，对当前决策进行校正。这种情况反映在一个混合整数回扫模型中，该模型具有有限数量的不确定性情景，表现为两阶段随机整数规划的形式。针对两阶段随机整数规划问题，提出了一种基于阶段分解的混合进化算法，该算法使用进化算法来确定当前决策，并用标准的数学规划方法来优化回扫决策。对算法的扩展行为进行了实证研究，结果表明，在具有大量情景的问题实例中，新的混合算法生成了更好的可行解，而且速度比最新的精确算法更快。
