Real Options in Engineering Systems Design

2009 ◽  
pp. 123-149
Author(s):  
Konstantinos Kalligeros
Keyword(s):  
Real Options ◽  
Systems Design ◽  
Engineering Systems

scholarly journals Analyzing Real Options and Flexibility in Engineering Systems Design using Decision Rules and Deep Reinforcement Learning

2021 ◽  
pp. 1-31
Author(s):  
Cesare Caputo ◽  
Michel-Alexandre Cardin
Keyword(s):  
Reinforcement Learning ◽  
Real Options ◽  
Decision Rules ◽  
Systems Design ◽  
Computational Design ◽  
Risk Tolerance ◽  
Action Space ◽  
Waste To Energy ◽  
Engineering Systems ◽  
Standard Design

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-to-energy 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.

scholarly journals THE ROLE OF MACHINE LEARNING FOR FLEXIBILITY AND REAL OPTIONS ANALYSIS IN ENGINEERING SYSTEMS DESIGN

2021 ◽  
Vol 1 ◽  
pp. 3121-3130
Author(s):  
Cesare Caputo ◽  
Michel-Alexandre Cardin
Keyword(s):  
Machine Learning ◽  
Real Options ◽  
Systems Design ◽  
Engineering Systems ◽  
Real Options Analysis ◽  
Domain Experts ◽  
Design Variables ◽  
Parameters Uncertainty ◽  
Infrastructure System ◽  
Computational Intractability

AbstractFlexibility analysis helps improve the expected value of engineering systems under uncertainty (economic and/or social). Designing for flexibility, however, can be challenging as a large number of design variables, parameters, uncertainty drivers, decision making possibilities and metrics must be considered. Many available techniques either rely on assumptions that are not suitable for an engineering setting, or may be limited due to computational intractability. This paper makes the case for an increased integration of Machine Learning into flexibility and real options analysis in engineering systems design to complement existing design methods. Several synergies are found and discussed critically between the fields in order to explore better solutions that may exist by analyzing the data, which may not be intuitive to domain experts. Reinforcement Learning is particularly promising as a result of the theoretical common grounds with latest methodological developments e.g. decision-rule based real options analysis. Relevance to the field of computational creativity is examined, and potential avenues for further research are identified. The proposed concepts are illustrated through the design of an example infrastructure system.

scholarly journals Exploring Natural Strategies for Bio-Inspired Fault Adaptive Systems Design

2018 ◽  
Vol 140 (9) ◽  
Author(s):  
Marvin Arroyo ◽  
Nicholas Huisman ◽  
David C. Jensen
Keyword(s):  
Adaptive Behavior ◽  
Binary Tree ◽  
Adaptive Systems ◽  
Adaptive Design ◽  
Systems Design ◽  
Initial Study ◽  
Design Experiment ◽  
Engineering Systems ◽  
System A ◽  
Specific Challenge

Fault adaptive design seeks to find the principles and properties that enable robustness, reliability, and resilience to implement those features into engineering products. In nature, this characteristic of adaptability is the fundamental trait that enables survival. Utilizing adaption strategy is a new area of research exploration for bio-inspired design (BID). In this paper, we introduce a tool for BID for fault adaption. Further, we discuss insights from using this tool in an undergraduate design experiment. The goal of the tool is to assist designers to develop fault adaptive behaviors in engineering systems using nature as inspiration. This tool is organized as a binary tree where branches that represent the specific details of how an organism achieves an adaptive behavior or characteristic. Results from an initial study indicate, for the specific challenge of designing fault adaption into a system, a strategy-based method can provide designers with innovative analogies and help provide the details needed to bridge the gap between analogy and engineering implementation.

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