Engineering system design, viewed as a decision-making process, faces challenges due to complexity and uncertainty. In this paper, we present a framework proposing the use of the Deep Q-learning algorithm to optimize the design of engineering systems. We outline a step-by-step framework for optimizing engineering system designs. The goal is to find policies that maximize the output of a simulation model given multiple sources of uncertainties. The proposed algorithm handles linear and non-linear multi-stage stochastic problems, where decision variables are discrete, and the objective function and constraints are assessed via a Monte Carlo simulation. We demonstrate the effectiveness of our proposed framework by solving two engineering system design problems in the presence of multiple uncertainties, such as price and demand.

翻译：工程系统设计被视为一个决策过程，由于复杂性和不确定性而面临挑战。在本文中，我们提出一个框架，建议使用深度Q学习算法来优化工程系统的设计。我们概述了一个逐步优化工程系统设计的框架。其目标是在存在多种不确定性来源的情况下，找到能够最大化仿真模型输出的策略。所提出的算法处理线性和非线性的多阶段随机问题，其中决策变量是离散的，目标函数和约束条件通过蒙特卡洛模拟进行评估。我们通过解决两个存在多种不确定性（如价格和需求）的工程系统设计问题，展示了所提框架的有效性。