Identifying Real Options to Improve the Design of Engineering Systems

2009 ◽  
pp. 75-98 ◽  
Author(s):  
Richard de Neufville
Keyword(s):  
Real Options ◽  
Engineering Systems

Prospect Theory-based Real Options Analysis for Noncommercial Assets

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Joshua T. Knight ◽  
David J. Singer
Keyword(s):  
Real Options ◽  
Prospect Theory ◽  
High Speed ◽  
Cash Flows ◽  
Engineering System ◽  
Engineering Systems ◽  
Dynamic Nature ◽  
Real Options Analysis ◽  
Rigorous Approach ◽  
Technological Opportunities

When an engineering system has the ability to change or adapt based on a future choice, then flexibility can become an important component of that system’s total value. However, evaluating noncommercial flexible systems, like those in the defense sector, presents many challenges because of their dynamic nature. Designers intuitively understand the importance of flexibility to hedge against uncertainties. In the naval domain, however, they often do not have the tools needed for analysis. Thus, decisions often rely on engineering experience. As the dynamic nature of missions and new technological opportunities push the limits of current experience, a more rigorous approach is needed. This paper describes a novel framework for evaluating flexibility in noncommercial engineering systems called prospect theory-based real options analysis (PB-ROA). While this research is motivated by the unique needs of the U.S. Navy ship design community, the framework abstracts the principles of real options analysis to suit noncommercial assets that do not generate cash flows. One contribution of PB-ROA is a systematic method for adjusting agent decisions according to their risk tolerances. The paper demonstrates how the potential for loss can dramatically affect decision making through a simplified case study of a multimission variant of a theoretical high-speed connector vessel.

A Framework for Designing and Managing Flexibility and Real Options in Engineering Systems Based on Decision Rules

2017 ◽  
Author(s):  
Qihui Xie ◽  
Michel-Alexandre Cardin
Keyword(s):  
Real Options ◽  
Performance Improvement ◽  
Decision Rules ◽  
Decision Makers ◽  
Waste To Energy ◽  
Engineering Systems ◽  
Triggering Mechanism ◽  
Implementation Phase ◽  
Uncertainty Sources ◽  
Standard Design

This paper introduces a framework to design and manage flexibility in engineering systems based on the concept of decision rules. A decision rule can be described as a heuristic triggering mechanism that is used to determine when it is appropriate to exercise flexibility in systems operations. The proposed framework differs from existing real options analysis (ROA) approaches used in a design and management setting by focusing on the practicability in the implementation phase of engineering systems. By incorporating decision rules in the design process, this framework not only helps generate better performing designs, it also provides intuitive guidance for decision makers (DMs) to manage the system in operations. The proposed framework is applied as demonstration to the design and management of an anaerobic digestion (AD) waste-to-energy (WTE) plant. It demonstrates significant lifecycle performance improvement as compared to a standard design analysis. A comparison with existing ROA approaches shows that another advantage of the proposed framework is the ability to analyze systems facing multiple uncertainty sources and relying on multiple flexibility strategies as a way to improve expected lifecycle performance.

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.

Respect-PTSD: Re-Engineering Systems for the Primary Care Treatment of PTSD

2012 ◽  
Author(s):  
Paula Schnurr ◽  
Matthew Friedman ◽  
Thomas Oxman ◽  
Allen Dietrich ◽  
Mark Smith ◽  
...  
Keyword(s):  
Primary Care ◽  
Engineering Systems ◽  
Care Treatment
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