Analyzing real options and flexibility in engineering systems design using decision rules and deep reinforcement learning
File(s)
Accepted version
Author(s)
Caputo, C
Cardin, M-A
Type
Journal Article
Abstract
Engineering systems provide essential services to society e.g., power generation,
transportation. Their performance, however, is directly affected by their ability to cope with
uncertainty, especially given the realities of climate change and pandemics. Standard design
methods often fail to recognize uncertainty in early conceptual activities, leading to rigid
systems that are vulnerable to change. Real Options and Flexibility in Design are important
paradigms to improve a system’s ability to adapt and respond to unforeseen conditions.
Existing approaches to analyze flexibility, however, do not leverage sufficiently recent
developments in machine learning enabling deeper exploration of the computational design
space. There is untapped potential for new solutions that are not readily accessible using
existing methods. Here, a novel approach to analyze flexibility is proposed based on Deep
Reinforcement Learning (DRL). It explores available datasets systematically and considers a
wider range of adaptability strategies. The methodology is evaluated on an example waste-toenergy system. Low and high flexibility DRL models are compared against stochastically
optimal inflexible and flexible solutions using decision rules. The results show highly dynamic
solutions, with action space parametrized via artificial neural network. They show improved
expected economic value up to 69% compared to previous solutions. Combining information
from action space probability distributions along expert insights and risk tolerance helps make
better decisions in real-world design and system operations. Out of sample testing shows that
the policies are generalizable, but subject to tradeoffs between flexibility and inherent
limitations of the learning process.
transportation. Their performance, however, is directly affected by their ability to cope with
uncertainty, especially given the realities of climate change and pandemics. Standard design
methods often fail to recognize uncertainty in early conceptual activities, leading to rigid
systems that are vulnerable to change. Real Options and Flexibility in Design are important
paradigms to improve a system’s ability to adapt and respond to unforeseen conditions.
Existing approaches to analyze flexibility, however, do not leverage sufficiently recent
developments in machine learning enabling deeper exploration of the computational design
space. There is untapped potential for new solutions that are not readily accessible using
existing methods. Here, a novel approach to analyze flexibility is proposed based on Deep
Reinforcement Learning (DRL). It explores available datasets systematically and considers a
wider range of adaptability strategies. The methodology is evaluated on an example waste-toenergy system. Low and high flexibility DRL models are compared against stochastically
optimal inflexible and flexible solutions using decision rules. The results show highly dynamic
solutions, with action space parametrized via artificial neural network. They show improved
expected economic value up to 69% compared to previous solutions. Combining information
from action space probability distributions along expert insights and risk tolerance helps make
better decisions in real-world design and system operations. Out of sample testing shows that
the policies are generalizable, but subject to tradeoffs between flexibility and inherent
limitations of the learning process.
Date Issued
2022-02-01
Date Acceptance
2021-08-04
Citation
Journal of Mechanical Design - Transactions of the ASME, 2022, 144 (2)
ISSN
1050-0472
Publisher
American Society of Mechanical Engineers
Journal / Book Title
Journal of Mechanical Design - Transactions of the ASME
Volume
144
Issue
2
Copyright Statement
© 2021 by ASME.
Subjects
Design Practice & Management
0913 Mechanical Engineering
1203 Design Practice and Management
Publication Status
Published
Article Number
ARTN 021705
Date Publish Online
2021-09-21