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Engineering system design, viewed as a decision-making process, faceschallenges due to complexity and uncertainty. In this paper, we present aframework proposing the use of the Deep Q-learning algorithm to optimize thedesign of engineering systems. We outline a step-by-step framework foroptimizing engineering system designs. The goal is to find policies thatmaximize the output of a simulation model given multiple sources ofuncertainties. The proposed algorithm handles linear and non-linear multi-stagestochastic problems, where decision variables are discrete, and the objectivefunction and constraints are assessed via a Monte Carlo simulation. Wedemonstrate the effectiveness of our proposed framework by solving twoengineering system design problems in the presence of multiple uncertainties,such as price and demand.

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