Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach

• URL: http://arxiv.org/abs/2312.17284v1
• Date: Thu, 28 Dec 2023 06:11:34 GMT
• Title: Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach
• Authors: Ramin Giahi, Cameron A. MacKenzie, Reyhaneh Bijari
• Abstract summary: We present a framework proposing the use of the Deep Q-learning algorithm to optimize the design of engineering systems. The goal is to find policies that maximize the output of a simulation model given multiple sources of uncertainties. We demonstrate the effectiveness of our proposed framework by solving two engineering system design problems in the presence of multiple uncertainties.
• Score: 1.3812010983144802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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