Publications
94 results found
Bigestans D, Cardin M-A, Kazantzis N, 2023, Economic performance evaluation of flexible centralised and decentralised blue hydrogen production systems design under uncertainty, Applied Energy, Vol: 352, ISSN: 0306-2619
Blue hydrogen is viewed as an important energy vector in a decarbonised global economy, but its large-scale and capital-intensive production displays economic performance vulnerabities in the face of increased market and regulatory uncertainty. This study analyses flexible (modular) blue hydrogen production plant designs and evaluates their effectiveness to enhance economic performance under uncertainty. The novelty of this work lies in the development of a comprehensive techno-economic evaluation framework that considers flexible centralised and decentralised blue hydrogen plant design alternatives in the presence of irreducible uncertainty, whilst explicitly considering the time value of money, economies of scale and learning effects. A case study of centralised and decentralised blue hydrogen production for the transport sector in the San Francisco area is developed to highlight the underlying value of flexibility. The proposed methodological framework considers various blue hydrogen plant designs (fixed, phased, and flexible) and compares them using relevant economic indicators (net present value (NPV), capex, value-at-risk/gain, etc.) through a detailed Monte Carlo simulation framework. Results indicate that flexible centralised hydrogen production yields greater economic value than alternative designs, despite the associated cost-premium of modularity. It is also shown that the value of flexibility increases under greater uncertainty, higher learning rates and weaker economies of scale. Moreover, sensitivity analysis reveals that flexible design remains the preferred investment option over a wide range of market and regulatory conditions except for high initial hydrogen demand. Finally, this study demonstrates that major regulatory and market uncertainties surrounding blue hydrogen production can be effectively managed through the application of flexible engineering system design that protects the investment from major downside risks whilst allowing access to
Malone L, Cardin M-A, Cilliers JJ, et al., 2023, Exploring Novel Architectures in Lunar In-Situ Resource Utilisation, Brisbane, Australia, 26th World Mining Congress
Caputo C, Cardin M-A, Ge P, et al., 2023, Design and planning of flexible mobile Micro-Grids using Deep Reinforcement Learning, Applied Energy, Vol: 335, ISSN: 0306-2619
Ongoing risks from climate change have significantly impacted the livelihood of global nomadic communities and are likely to lead to increased migratory movements in coming years. As a result, mobility considerations are becoming increasingly important in energy systems planning, particularly to achieve energy access in developing countries. Advanced “Plug and Play” control strategies have been recently developed with such a decentralized framework in mind, allowing easier interconnection of nomadic communities, both to each other and to the main grid. Considering the above, the design and planning strategy of a mobile multi-energy supply system for a nomadic community is investigated in this work. Motivated by the scale and dimensionality of the associated uncertainties, impacting all major design and decision variables over the 30-year planning horizon, Deep Reinforcement Learning (DRL) Flexibility Analysis is implemented for the design and planning problem. DRL based solutions are benchmarked against several rigid baseline design options to compare expected performance under uncertainty. The results on a case study for ger communities in Mongolia suggest that mobile nomadic energy systems can be both technically and economically feasible, particularly when considering flexibility, although the degree of spatial dispersion among households is an important limiting factor. Additionally, the DRL based policies lead to the development of dynamic evolution and adaptability strategies, which can be used by the targeted communities under a very wide range of potential scenarios. Key economic, sustainability and resilience indicators such as Cost, Equivalent Emissions and Total Unmet Load are measured, suggesting potential improvements compared to available baselines of up to 25%, 67% and 76%, respectively. Finally, the decomposition of values of flexibility and plug and play operation is presented using a variation of real options theory, with important impl
Zhao S, Haskell WB, Cardin M-A, 2023, A flexible system design approach for multi-facility capacity expansion problems with risk aversion, IISE Transactions, Vol: 55, Pages: 187-200, ISSN: 2472-5854
This paper studies a model for risk aversion when designing a flexible capacity expansion plan for a multi-facility system. In this setting, the decision maker can dynamically expand the capacity of each facility given observations of uncertain demand. We model this situation as a multi-stage stochastic programming problem, and we express risk aversion through the conditional value-at-risk (CVaR) and a mean-CVaR objective. We optimize the multi-stage problem over a tractable family of if–then decision rules using a decomposition algorithm. This algorithm decomposes the stochastic program over scenarios and updates the solutions via the subgradients of the function of cumulative future costs. To illustrate the practical effectiveness of this method, we present a numerical study of a decentralized waste-to-energy system in Singapore. The simulation results show that the risk-averse model can improve the tail risk of investment losses by adjusting the weight factors of the mean-CVaR objective. The simulations also demonstrate that the proposed algorithm can converge to high-performance policies within a reasonable time, and that it is also more scalable than existing flexible design approaches.
Cardin MA, Mijic A, Whyte J, 2023, Data-driven infrastructure systems design for uncertainty, sustainability, and resilience, Pages: 2565-2572
There are currently many discussions around the need to design infrastructure systems that are more resilient and sustainable in the future, especially considering growing uncertainties from climate change, pandemics, geopolitical conflicts, and cyber/physical terrorism. It is widely recognized that infrastructure systems provide vital functions for society e.g., power generation, transportation, water management, and that they face much uncertainty and variability over their operating lifetime (+20 years). Yet, standard engineering methods provide limited guidance on how to best design such systems to make them more adaptable, evolvable, and reconfigurable to deal with future uncertainty and risks. The field of Flexibility in Engineering Design that emerged from the theory of real options provide systematic and innovative computational tools, algorithms, and digital processes to help designers and engineers better account for uncertainty and risks in early conceptual design activities. This paper provides an overview of the latest developments and future directions in this rapidly growing field. It discusses how flexibility provides the foundations for a unifying conceptual framework to create infrastructure systems that are both more sustainable and resilient. It introduces cutting edge techniques to support the design process based on principles from stochastic programming, robust optimization, deep reinforcement learning, and simulation games, including examples in energy and transportation systems.
Ge P, Caputo C, Teng F, et al., 2022, A Wireless-Assisted Hierarchical Framework to Accommodate Mobile Energy Resources, Singapore, IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
Malone L, Cardin M-A, Cilliers J, et al., 2022, Development of a Comprehensive Lunar Mining Simulator to Study Design and Decision-Making under Uncertainty, Paris, France, International Astronautical Congress
Caunhye AM, Cardin M-A, Rahmat M, 2022, Flexibility and real options analysis in power system generation expansion planning under uncertainty, IISE Transactions, Vol: 54, Pages: 832-844, ISSN: 2472-5854
Over many years, there has been a drive in the electricity industry towards better integration of environmentally friendly and renewable generation resources for power systems. Such resources show highly variable availability, impacting the design and performance of power systems. In this paper, we propose using a stochastic programming approach to optimize generation expansion planning (GEP), with explicit consideration of generator output capacity uncertainty. Flexibility implementation - via real options exercised in response to uncertainty realizations - is considered as an important design approach to the GEP problem. It more effectively captures upside opportunities, while reducing exposure to downside risks. A decision-rule based approach to real options modeling is used, combining conditional-go and finite adaptability principles. The solutions provide decision makers with easy-to-use guidelines with threshold values from which to exercise the options in operations. To demonstrate application of the proposed methodologies and decision rules, a case study situated in the Midwest United States is used. The case study demonstrates how to quantify the value of flexibility, and showcases the usefulness of the proposed approach.
Anderson J, Cardin M-A, Grogan P, 2022, Design and analysis of flexible multi-layer staged deployment for satellite mega-constellations under demand uncertainty, Acta Astronautica, Vol: 198, ISSN: 0094-5765
Internet satellite constellations are expected to play an important role in accommodating the rising global demand for internet access. Such rise in demand, however, is highly uncertain. Staged deployment is an approach that provides flexibility to tackle demand uncertainty by enabling the real option to reconfigure a constellation if demand changes. Advancements in satellite technology have led to the emergence of multi-layered constellations. This opens the opportunity to enhance staged deployment by enabling an additional real option: adding a new layer to a constellation. This real option has no associated reconfiguration costs, and therefore has the potential to reduce the cost of staged systems deployment. This paper proposes a framework to design multi-layer staged deployment systems and analyse their effectiveness in modern mega-constellations under global demand uncertainty. The framework is applied to four case studies based on market projections. Results show that multi-layer staged deployment decreases the expected life-cycle cost (ELCC) by 42.8% compared to optimal traditional single-layer deployment. Multi-layer staged deployment is more cost effective than single-layer staged deployment in all practical cases, which decreases ELCC by 22.9% compared to traditional deployment. Several cost altering mechanisms in staged deployment are identified. The results and analysis provide improved economic performance and better resource utilization, thus contributing in the long term to improved sustainability and market resilience. An accompanying decision support system provides system engineers with valuable insights on how to reduce deployment costs using the proposed multi-layered staged strategy.
Caputo C, Cardin M-A, Korre A, et al., 2022, Energy System Evolution Strategies for Mobile Micro-grids using Deep Reinforcement Learning Flexibility Analysis, Espoo, Finland, 32nd European Conference on Operational Research (EURO 2022)
Cardin M-A, Mijic A, Whyte J, 2022, Flexibility and real options in engineering systems design, Handbook of Engineering Systems Design, Editors: Maier, Oehmen, Vermaas, Publisher: Springer, Pages: 1-29, ISBN: 978-3-030-46054-9
Designing engineering systems for flexibility is of utmost importance for future generations of systemsdesigners and operators. As a core system property, flexibility provides systems owners and operators with the abilityto respond easily and cost-effectively to future changes. It contributes to improved economic value, sustainability andresilience by enabling systems to adapt and reconfigure in the face of uncertainty in operations, markets, regulations,and technology. The field of Flexibility in Design has steadily evolved over the last two decades, emerging from thearea of Real Options Analysis, which focuses on quantifying the value of flexibility in large-scale, irreversibleinvestment projects. Flexibility in Design goes further by developing and evaluating novel design methods andcomputational procedures to enable flexibility as a systematic value enhancement mechanism in engineering systems.This chapter provides an overview of how the field has developed over time as well as design frameworks, methodsand procedures to support such design activities in practice. It discusses important challenges and limitations withsupporting case studies in aerospace, automotive, energy, real estate, transportation, and water management. Thechapter highlights key future directions for research, involving sustainability and resilience, data-driven real options,empirical studies and simulation games, machine learning, digital twin modelling, and 3D virtualization.
Allison JT, Cardin MA, McComb C, et al., 2022, Special Issue: Artificial intelligence and engineering design, Journal of Mechanical Design, Transactions of the ASME, Vol: 144, ISSN: 1050-0472
Caputo C, Cardin MA, 2022, Analyzing Real Options and Flexibility in Engineering Systems Design Using Decision Rules and Deep Reinforcement Learning, Journal of Mechanical Design, Vol: 144, ISSN: 1050-0472
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 (WTE) 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 (ANN). They show improved expected economic value up to 69% compared with 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.
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Abdin AF, Caunhye A, Zio E, et al., 2022, Optimizing generation expansion planning with operational uncertainty: A multistage adaptive robust approach, Applied Energy, Vol: 306, Pages: 1-18, ISSN: 0306-2619
This paper presents a multistage adaptive robust generation expansion planning model, which accounts for short-term unit commitment and ramping constraints, considers multi-period and multi-regional planning, and maintains the integer representation of generation units. The uncertainty of electricity demand and renewable power generation is taken into account through bounded intervals, with parameters that permit control over the level of conservatism of the solution. The multistage robust optimization model allows the sequential representation of uncertainty realization as they are revealed over time. It also guarantees the non-anticipativity of future uncertainty realizations at the time of decision-making, which is the case in practical real-world applications, as opposed to two-stage robust and stochastic models. To render the resulting multistage robust problem tractable, decision rules are employed to cast the uncertainty-based model into an equivalent mixed integer linear (MILP) problem. The re-formulated MILP problem, while tractable, is computationally prohibitive even for moderately sized systems. We, thus, propose a solution method relying on the reduction of the information basis of the decision rules employed in the model, and validate its adequacy to efficiently solve the problem. The importance of considering multistage robust frameworks for accounting for net-load uncertainties in generation expansion planning is illustrated, particularly under a high share of renewable energy penetration. A number of renewable penetration scenarios and uncertainty levels are considered for a case study covering future generation expansion planning in Europe. The results confirm the effectiveness of the proposed approach in coping with multifold operational uncertainties and for deriving adequate generation investment decisions. Moreover, the quality of the solutions obtained and the computational performance of the proposed solution method is shown to be suitable fo
Cardin MA, Mijic A, Whyte J, 2022, Flexibility and Real Options in Engineering Systems Design, Handbook of Engineering Systems Design: With 178 Figures and 54 Tables, Pages: 623-651, ISBN: 9783030811587
Designing engineering systems for flexibility is of utmost importance for future generations of systems designers and operators. As a core system property, flexibility provides systems owners and operators with the ability to respond easily and cost-effectively to future changes. It contributes to improved economic value, sustainability, and resilience by enabling systems to adapt and reconfigure in the face of uncertainty in operations, markets, regulations, and technology. The field of flexibility in design has steadily evolved over the last two decades, emerging from the area of real options analysis, which focuses on quantifying the value of flexibility in large-scale, irreversible investment projects. Flexibility in design goes further by developing and evaluating novel design methods and computational procedures to enable flexibility as a systematic value enhancement mechanism in engineering systems. This chapter provides an overview of how the field has developed over time as well as design frameworks, computational methods, and algorithmic procedures to support such design activities in practice. It discusses important challenges and limitations with supporting case studies in aerospace, automotive, energy, real estate, transportation, and water management. The chapter highlights future directions for research, involving sustainability and resilience, data-driven real options, empirical studies and simulation games, machine learning, digital twin modelling, and 3D virtualization.
Caputo C, Cardin M-A, 2021, The role of machine learning for flexibility and real options analysis in engineering systems design, International Conference on Engineering Design, Publisher: Cambridge University Press, Pages: 3121-3130
Flexibility 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.
Mijic A, Whyte J, Fisk D, et al., 2021, The Centre for Systems Engineering and Innovation – 2030 vision and 10-year celebration
The 2030 vision of the Centre is to bring Systems Engineering and Innovation to Civil Infrastructure by changing how cross-sector infrastructure challenges are addressedin an integrated way using principles of systems engineering to maximise resilience, safety and sustainability in an increasingly complex world.We want to better understand the environmental and societal impacts of infrastructure interventions under uncertainty. This requires a change in current approaches to infrastructure systems engineering: starting from the natural environmentand its resources, encompassing societaluse of infrastructure and the supporting infrastructure assets and services.We argue for modelling that brings natural as well as built environments within the system boundaries to better understand infrastructure and to better assess sustainability. We seethe work as relevant to both the academic community and to a wide range of industry and policy applications that are working on infrastructure transition pathways towards fair, safe and sustainable society.This vision was developed through discussions between academics in preparation for the Centre for Systems Engineering and Innovation (CSEI) 10 years celebration. These rich discussions about the future of the Centre were inspired by developing themes for a celebration event, through which we have summarised the first 10 years of the Centre’s work and our vision for the future and identified six emerging research areas.
Blume SOP, Sansavini G, Cardin M-A, 2021, A Dynamic Agent-based Transit System Disruption and Recovery Simulation Model, Publisher: Research Publishing, Singapore
Mijic A, Whyte J, Myers R, et al., 2021, Reply to a discussion of ‘a research agenda on systems approaches to infrastructure’ by david elms, Civil Engineering and Environmental Systems, Vol: 38, Pages: 295-297, ISSN: 1028-6608
We thank Prof Elms for his insightful comments and suggestions. The paper was indeed aimed at setting the future direction for the Centre for Systems Engineering and Innovation (CSEI) at Imperial College London, with the hope that the ideas will inspire others who work in the same or similar area of research. We are pleased to see that Prof Elms enjoyed reading our paper.
Heydari B, Szajnfarber Z, Panchal J, et al., 2020, Special issue: Analysis and design of sociotechnical systems, Journal of Mechanical Design, Vol: 142, ISSN: 1050-0472
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Blume SOP, Cardin M-A, Sansavini G, 2020, A Simulation-based Approach to Modelling Disruptions and Quantifying Uncertainties in a Large-scale Urban Transit System, INFORMS Annual Meeting 2019, Publisher: Informs, Pages: 563-563
Zhao S, Haskell WB, Cardin M-A, 2020, A flexible multi-capacity expansion problem with risk aversion, New Orleans, LA, United States, IISE Annual Conference & Expo 2020
This paper studies flexible multi-facility capacity expansion with risk aversion. We express risk aversion inthis problem through conditional value-at-risk (CVaR) and model it as a multi-stage stochastic programming problem with a mean-CVaR objective. To solve the multi-stage problem, we approximate the fullpolicy space of the problem with a tractable family of if-then policies. Subsequently, a decomposition algorithm is proposed to optimize the decision rule. To illustrate the practical effectiveness of this method,a numerical study on the waste-to-energy system in Singapore is presented. These simulation results showthat by adjusting the weight factor of mean and CVaR in the objective function, decision makers are ableto trade off between a risk-averse policy that has a higher expected cost but a lower value-at-risk, and arisk-neutral policy that has a lower expected cost but a higher value-at-risk.
Whyte J, Mijic A, Myers RJ, et al., 2020, A research agenda on systems approaches to infrastructure, Civil Engineering and Environmental Systems, Vol: 37, Pages: 214-233, ISSN: 1028-6608
At a time of system shocks, significant underlying challenges are revealed in current approaches to delivering infrastructure, including that infrastructure users in many societies feel distant from nature. We set out a research agenda on systems approaches to infrastructure, drawing on ten years of interdisciplinary work on operating infrastructure, infrastructure interventions and lifecycles. Research insights and directions on complexity, systems integration, data-driven systems engineering, infrastructure life-cycles, and the transition towards zero pollution are summarised. This work identifies a need to better understand the natural and societal impacts of infrastructure interventions under uncertainty. We argue for a change in current approaches to infrastructure: starting from the natural environment and its resources, encompassing societal use of infrastructure and the supporting infrastructure assets and services. To support such proposed new systems approaches to infrastructure, researchers need to develop novel modelling methods, forms of model integration, and multi-criteria indicators.
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Blume SOP, Cardin M-A, Sansavini G, 2020, Dynamic Disruption Simulation in Large-Scale Urban Rail Transit Systems, Publisher: Springer International Publishing, Pages: 129-140
Caputo C, Cardin M-A, 2020, A machine learning based framework for the design and evaluation of flexible, sustainable and resilient energy systems under uncertainty, Glasgow, United Kingdom, Supergen Energy Networks Hub Risk Day
Kuznetsova E, Cardin M-A, Diao M, et al., 2019, Integrated decision-support methodology for combined centralized-decentralized waste-to-energy management systems design, Renewable and Sustainable Energy Reviews, Vol: 103, Pages: 477-500, ISSN: 1364-0321
The rapid expansion of urban populations and concomitant increase in the generation of municipal solid waste (MSW) exert considerable pressure on the conventional centralized MSW management system and are beginning to exceed disposal capacities. To tackle this issue, the conventional centralized MSW management system is more likely to evolve toward a more decentralized system with smaller capacity waste treatment facilities that are integrated at different levels of the urban environment, e.g., buildings, districts, and municipalities. In addition, MSW can become an important urban resource to address the rising energy consumption through waste-to-energy (WTE) technologies capable of generating electricity, heat, and biogas. This shift toward the combined centralized-decentralized waste-to-energy management system (WtEMS) requires an adapted decision-support methodology (DSM) that can assist decision-makers in analyzing MSW generation across large urban territories and designing optimal long-term WtEMS. The proposed integrated DSM for WtEMS planning relies on: i) an MSW segregation and prediction methodology, ii) an optimization methodology for the deployment of multi-level urban waste infrastructure combining centralized and decentralized facilities, and iii) a multi-criterion sustainability framework for WtEMS assessment. The proposed DSM was tested on a case study that was located in Singapore. The proposed WtEMS not only reduced the total operational expenses by about 50%, but also increased revenues from electricity recovery by two times in comparison with the conventional MSW management system. It also allowed more optimal land use (capacity-land fragmentation was reduced by 74.8%) and reduced the size of the required transportation fleet by 15.3% in comparison with the conventional MSW system. The Global Warming Potential (GWP) was improved by about 18.7%.
Cardin M-A, Hastings D, Jackson P, et al., 2019, Complex Systems Design & Management Asia. Smart Nations – Smart Transportation: Proceedings of the Third Asia-Pacific Conference on Complex Systems Design & Management, CSD&M Asia 2018, Publisher: Springer International Publishing, ISBN: 9783030028855
Caunhye AM, Cardin M-A, 2019, An Approach based on Robust Optimization and Decision Rules for Analyzing Real Options in Engineering Systems Design, Orlando, FL, United States, Best Paper Award Session, IISE Conference and Expo, Publisher: Institute of Industrial and Systems Engineers
de Neufville R, Smet K, Cardin M, et al., 2019, Engineering Options Analysis (EOA): Applications, Decision Making under Deep Uncertainty: From Theory to Practice, Editors: Marchau, Walker, Bloemen, Popper, Cham, Publisher: Springer International Publishing, Pages: 223-252, ISBN: 978-3-030-05252-2
This chapter illustrates the use and value of Engineering Options Analysis (EOA) using two case studies. Each describes the analysis in detail. Each entails the need for plans to monitor projects so that managers know when to exercise options and adapt projects to the future as it develops. The Liquid Natural Gas case (Case Study 1) concerns the development of a liquid natural gas plant in Australia. It:Provides a generic prototype for the analysis of projected innovative developments.Demonstrates the kind of insights that EOA can provide.Highlights the potential advantage of flexibility in size, time, and location of facilities. In particular, it develops the important insight that modular designs may be more profitable than monolithic designs because they enable managers to reduce the significant risk of overdesigned plants, and they increase opportunities by taking advantage of the time and location of increases in demand.The IJmuiden case (Case Study 2) concerns water management and flood control facilities in the Netherlands. It:Demonstrates the application of EOA to cope with uncertainty in natural processes, in contrast to the more traditional context of market uncertainties.Uses diverse scenarios to identify conditions for which a strategy is valid across significant ranges of future conditions, and contrary situations in which a choice depends on belief about the level of risks.Documents how EOA shows which forms of flexibility in design justify their cost (in this case, flexibility in pumping facilities adds significant value, but flexibility in the flood defense height of the structure does not).Shows how the calculation of distributions of possible outcomes provides decisionmakers with useful information concerning worst-case outcomes, unavailable from average outcomes alone.
Mak WH, Cardin M-A, Liu Z, et al., 2018, Towards the design of resilient waste-to-energy systems using Bayesian networks, Proceedings of the ASME Design Engineering Technical Conference
Copyright © 2018 ASME. The concept of resilience has emerged from various domains to address how systems, people and organizations can handle uncertainty. This paper presents a method to improve the resilience of an engineering system by maximizing the system economic lifecycle value, as measured by Net Present Value, under uncertainty. The method is applied to a Waste-to-Energy system based in Singapore and the impact of combining robust and flexible design strategies to improve resilience are discussed. Robust strategies involve optimizing the initial capacity of the system while Bayesian Networks are implemented to choose the flexible expansion strategy that should be deployed given the current observations of demand uncertainties. The Bayesian Network shows promise and should be considered further where decisions are more complex. Resilience is further assessed by varying the volatility of the stochastic demand in the simulation. Increasing volatility generally made the system perform worse since not all demand could be converted to revenue due to capacity constraints. Flexibility shows increased value compared to a fixed design. However, when the system is allowed to upgrade too often, the costs of implementation negates the revenue increase. The better design is to have a high initial capacity, such that there is less restriction on the demand with two or three expansions.
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