论文标题
参数成本函数近似：一种多阶段随机编程的新方法
The Parametric Cost Function Approximation: A new approach for multistage stochastic programming
论文作者
论文摘要
解决研究文献中多阶段随机编程问题的最常见方法是使用价值功能（“动态编程”）或场景树（“随机编程”），以近似现在的决定对未来的影响。相比之下，共同的行业实践是使用对未来的确定性近似，这更容易理解和解决，但由于忽略不确定性而受到批评。我们表明，确定性优化模型的参数化版本可以是处理不确定性的有效方法，而无需随机编程或动态编程的复杂性。我们介绍了一个参数化的确定性优化模型，尤其是确定性的LookAhead模型的想法，是许多复杂随机决策问题的有力策略。这种方法可以处理复杂的高维状态变量，并避免与方案树相关的通常近似值或值函数近似值。相反，它引入了设计和调整参数化的离线挑战。我们通过使用一系列应用程序设置来说明这个想法，并证明其在滚动预测中的非平稳储能问题中的用途。
The most common approaches for solving multistage stochastic programming problems in the research literature have been to either use value functions ("dynamic programming") or scenario trees ("stochastic programming") to approximate the impact of a decision now on the future. By contrast, common industry practice is to use a deterministic approximation of the future which is easier to understand and solve, but which is criticized for ignoring uncertainty. We show that a parameterized version of a deterministic optimization model can be an effective way of handling uncertainty without the complexity of either stochastic programming or dynamic programming. We present the idea of a parameterized deterministic optimization model, and in particular a deterministic lookahead model, as a powerful strategy for many complex stochastic decision problems. This approach can handle complex, high-dimensional state variables, and avoids the usual approximations associated with scenario trees or value function approximations. Instead, it introduces the offline challenge of designing and tuning the parameterization. We illustrate the idea by using a series of application settings, and demonstrate its use in a nonstationary energy storage problem with rolling forecasts.
