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 Perceived risk of lock-in in the front-end phase of major transportation projects

2021 ◽  
Author(s):  
Chantal C. Cantarelli ◽  
David Oglethorpe ◽  
Bert van Wee
Keyword(s):  
Perceived Risk ◽  
Risk Perceptions ◽  
Decision Makers ◽  
Project Proposal ◽  
Implementation Phase ◽  
Perceptions Of Risk ◽  
Transportation Projects ◽  
Front End ◽  
Mediating Factor ◽  
Lock In

AbstractLock-in is defined as the tendency to continue with an inefficient decision or project proposal. The front-end phase is critical to project success, yet most studies have focused on lock-in in the implementation phase. Moreover, little is known about the way in which decision-makers perceive the risk of lock-in. In this paper we identify determinants of lock-in in the front-end phase and we reveal decision-makers’ perceptions of risk of lock-in. Our findings show that risk attitudes towards lock-in vary with the level of risk aversion. However, this is not sufficiently acute to drive the level of regret needed to avoid lock-in. This implies that decision-makers do not accurately assess the risk of lock-in and as such their risk perceptions are a mediating factor in the formation of lock-in. Based on escalation of commitment, path dependency, and prospect theory, the main contribution lies in providing a more comprehensive understanding of lock-in in the front-end phase.

The role of agency in project initiation decisions

2014 ◽  
Vol 7 (3) ◽  
pp. 518-535 ◽  
Author(s):  
Mark Mullaly
Keyword(s):  
Decision Making ◽  
Decision Rules ◽  
Personal Characteristics ◽  
Decision Makers ◽  
Organizational Dynamics ◽  
Substantive Theory ◽  
Content Type ◽  
The Subject ◽  
Board Level

Purpose – The purpose of this paper is to explore the role of decision rules and agency in supporting project initiation decisions, and the influences of agency on decision-making effectiveness. Design/methodology/approach – The study this paper is based upon used grounded theory methodology, and sought to understand the influences of individual decision makers on project initiation decisions within organizations. Data collection involved 28 participants who were involved in project initiation decisions within their organizations, who discussed the process of project initiation in their organization and their role within that process. Findings – The study demonstrates that the overall effectiveness of project initiation decisions is a product of agency, process effectiveness or rule effectiveness. The employment of agency can have a direct influence on decision-making effectiveness, it can compensate for organizational inadequacies of a process or political nature, and it can be constrained in the evidence of formal and effective organizational practices. Research limitations/implications – While agency was recognized by all participants, there are clearly circumstances where actors perceive the ability to exercise agency to be externally constrained. The study is exploratory, contributing to the development of substantive theory. Theory testing as well as a more in-depth investigation of the underlying drivers of agency would be valuable. Practical implications – The study provides executives and individuals supporting the initiation of projects with insights on how to effectively influence the effectiveness of project initiation decisions, and the degree to which personal characteristics influence organizational dynamics. Originality/value – Most discussions of agency has been framed the subject as an executive- or board-level phenomenon. The current study demonstrates that agency is in fact being perceived and operationalized at all levels. Those demonstrating agency in the majority of instances in this study do so in exercising stewardship behaviours. This has important implications for how agency is perceived by executives, and by how agency is exercised by actors at all levels of the organization.

Group Decision Model Based on Ordered Weighted Distance to Aid Decisions on Logistics

2018 ◽  
Vol 26 (02) ◽  
pp. 233-254 ◽  
Author(s):  
Jose Leao E Silva Filho ◽  
Danielle Costa Morais
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Decision Model ◽  
Group Decision ◽  
Decision Rules ◽  
Decision Makers ◽  
Weighted Distance ◽  
Model Based ◽  
Decision Making Model

This paper presents a group decision-making model using a distance aggregator based on Ordered Weighted Distance (OWD) which offers a solution that can reduce disagreement between decision makers (DMs). This paper discusses decision rules and sets out measures to evaluate compensatory effects that have a bearing on DMs’ opinions. The model uses formulations of distances to reveal the differences in opinion among DMs and discusses the meanings of distance and the information presented by each DM. Finally, a case study of a logistics problem is used to illustrate how the model is applied.

Sign in / Sign up

Export Citation Format

Share Document