71 results found
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
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
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
Zhao S, Haskell WB, Cardin M-A, 2020, A Flexible Multi-Capacity Expansion Problem with Risk Aversion, New Orleans, LA, United States, IISE Conference and Expo
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.
Blume S, Sansavini G, Cardin M-A, 2018, Measuring the Fragility of Large-Scale Transport Systems, Singapore, 6th International Symposium on Reliability Engineering and Risk Management
Jiang Y, Caunhye AM, Cardin M-A, 2018, Development of a simulation game platform for flexible generation expansion planning and design of power grid systems, IISE Annual Conference and Expo 2018, Pages: 1830-1836
© 2018 Institute of Industrial Engineers (IIE). All rights reserved. This paper reports on the development of a simulation game platform for planning the generation expansion of power grid systems. It addresses possible inadequacies in strategic planning of existing facilities and new systems. In particular, it considers the reliability, resilience, uncertainty, and flexibility over the long term. This platform was developed to provide optimal planning solutions for power grid systems so that the potential impacts of unexpected events and operational conditions are mitigated at the least cost. It also provides a game-based decision-making environment that assesses human performance within an experimental setting, and evaluates training techniques under various uncertainty scenarios and in a more realistic setting. This platform is a strategic planning and interactive training tool that uses real-world data to integrate data analytics and visualization with optimization and simulation. For better urban sustainability, this paper has focused on interconnecting the optimization module to provide flexibility strategies, i.e., real options. Such a tool can be used by a variety of stakeholders in diverse application settings, including planning, management, training, and policy-making. Controlled experiments are being devised that primarily analyze decision rules-based real options. This approach should improve the expected life-cycle performance of power grid systems under uncertainty.
Zhou Y, Cardin M-A, Zhang S, 2018, Strategic flexibility analysis in emergency medical service systems using decision rules, ICNSC 2018 - 15th IEEE International Conference on Networking, Sensing and Control, Pages: 1-6
© 2018 IEEE. The capacity deployment of emergency medical service systems has been studied over decades. The design problem is still challenging due to the long-term uncertainty like the change of demographics. A novel approach in the form of multi-stage stochastic programming that considers flexibility in engineering design could be a valuable alternative to EMS infrastructure systems design. This paper proposes a hybrid algorithm to the stochastic model to improve its computational efficiency. The new algorithm outperforms other alternative methods in the numerical analysis by 3-6% in terms of quality of the solution, and is at least four times faster than the default methods.
Caunhye AM, Cardin M-A, 2018, Towards more resilient integrated power grid capacity expansion: A robust optimization approach with operational flexibility, Energy Economics, Vol: 72, Pages: 20-34, ISSN: 0140-9883
This paper proposes a multi-period two-stage adaptive robust optimization model for long term power grid capacity expansion in an environmentally conscious way under generator output uncertainties due to natural variations and generator disruptions. In the first stage, the model plans generator addition and transmission line setup prior to, and robust to, uncertainty realization. In the second stage, it plans power generation and dispatch after, and flexible to, uncertainty realization. The model exploits the idea of strategic robustness and operational flexibility as a way to improve performance in the face of uncertainty. The robust optimization framework uses deterministic uncertainty sets, with parameters that permit control over the level of conservatism of the solution. Because the resulting two-stage model is semi-infinite and, therefore, intractable, it is reformulated as an affinely adjustable counterpart. The reformulation uses affine decision rules on second-stage variables and converts, after constraint processing, the semi-infinite model into a finite single-stage mixed integer programming model. The resulting model is tested on the IEEE 30-bus system and value gains are shown by comparing the performance of the model with a deterministic model and a stochastic programming model with varying risk aversions.
Zhao S, Haskell WB, Cardin M-A, 2018, Decision rule-based method for flexible multi-facility capacity expansion problem, IISE Transactions, Vol: 50, Pages: 553-569, ISSN: 2472-5854
Strategic capacity planning for multiple-facility systems with flexible designs is an important topic in the area of capacity expansion problems with random demands. The difficulties of this problem lie in the multidimensional nature of its random variables and action space. For a single-facility problem, the decision rule method has been shown to be efficient in deriving desirable solutions, but for a Multiple-facility Capacity Expansion Problem (MCEP), it has not been well studied. This article designs a novel decision rule–based method for the solution of an MCEP with multiple options, discrete capacity, and a concave capacity expansion cost. An if–then decision rule is designed and the original multi-stage problem is thus transformed into a master problem and a multi-period sub-problem. As the sub-problem contains non-binding constraints, we combine a stochastic approximation algorithm with a branch-and-cut technique so that the sub-problem can be further decomposed across scenarios and be solved efficiently. The proposed decision rule–based method is also extended to solving the MCEP with fixed costs. Numerical studies in this article illustrate that the proposed method affords not only improved performance relative to an inflexible design taken as benchmark but also time savings relative to approximate dynamic programming analysis.
Deng Y, Cardin M-A, 2018, Integrating operational decisions into the planning of one-way vehicle-sharing systems under uncertainty, Transportation Research Part C: Emerging Technologies, Vol: 86, Pages: 407-424, ISSN: 0968-090X
This study proposes a systematic approach for planning and operating a one-way vehicle-sharing system (VSS) under demand uncertainty. It investigates the distribution of parking spaces and vehicles considering stochastic demand and interactions with the major operational decisions, namely vehicle redistribution activities. An optimization model is formulated that aims to determine the best deployment strategy for minimizing overall cost while achieving a certain level of service (LoS). Then, a simulation-based solution approach based on a discrete-event simulator (DES), Particle Swarm Optimization (PSO), and Optimal Computing Budget Allocation (OCBA) is devised to solve the mathematical model. The methodology is then applied to a car-sharing system in Singapore. Results demonstrate that considering rebalancing activities is imperative in making deployment strategies. The case study also provides managerial insights regarding designing and operating one-way VSS.
Zhang S, Cardin M-A, 2017, Flexibility and real options analysis in emergency medical services systems using decision rules and multi-stage stochastic programming, Transportation Research Part E: Logistics and Transportation Review, Vol: 107, Pages: 120-140, ISSN: 1366-5545
A novel approach to EMS infrastructure systems design, planning, and operations under long-term uncertainty is introduced based on multi-stage stochastic programming and decision rules, accounting for strategic flexibility (also known as real options – RO). Different from standard RO analysis, the approach mimics real-world decision-making by exercising flexibility based on conditional-go decision rules. The objective is to minimize the expected total costs over the system’s life cycle, and the outputs are the optimal initial configuration and decision rules. A flexible solution provides lower expected cost than stochastically optimal rigid solutions, especially valuable when required incident coverage rate is >95%.
Cardin M-A, Deng Y, Sun C, 2017, Real options and flexibility analysis in design and management of one-way mobility on-demand systems using decision rules, Transportation Research Part C: Emerging Technologies, Vol: 84, Pages: 265-287, ISSN: 0968-090X
This study explores the concepts of real options and flexibility analysis as an approach to address uncertain demand growth in mobility on-demand (MoD) vehicle-sharing systems, with the goal of improving expected lifecycle performance. As MoD systems are gaining popularity worldwide, they inevitably face significant uncertainty in terms of needs and customer demands. Designing, planning capacity deployment, and operating such system can be challenging, and require significant capital investments for companies and cities. Two distinct real options analysis (ROA) models are developed to evaluate and optimize flexible strategies for these systems, relying on a novel methodological approach to value flexibility based on decision rules. The decision-rule-based approach differs from standard ROA approaches used to quantify the value of flexibility in irreversible investment projects, typically based on dynamic programming. It emulates the decision-making process by capturing mathematically a triggering mechanism that determines when it is best to exercise the flexibilities embedded in the system design. Two prevalent types of MoD systems are studied in this paper as demonstration of the methodological framework: a station-based system where customers must pick up and return the vehicle at specific locations, and a free-floating system, where customers may pick up and drop the car anywhere within a certain area. A simulation-based approach is used to analyze the station-based system, which models the rebalancing operations from a micro-level perspective. The approach consists of a discrete event simulator for performance estimation, and an optimization algorithm for design space exploration that integrates a population-based search algorithm with Optimal Computing Budget Allocation (OCBA). For the free-floating system, an analytical model is developed where the decision rule is formulated into and solved using stochastic mixed integer programming (MIP). The study provides
Xie Q, Cardin M-A, 2017, A framework for designing and managing flexibility and real options in engineering systems based on decision rules, Proceedings of the ASME Design Engineering Technical Conference
Copyright © 2017 ASME. 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) wasteto- 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.
Rahmat M, Caunhye AM, Cardin M-A, 2017, Flexibility and real options analysis in design for long-term generation expansion planning of power grids, Proceedings of the ASME Design Engineering Technical Conference
© 2017 ASME. In recent years, the electricity industry has seen a drive towards the integration of renewable and environmentally friendly generation resources to power grids. These resources have highly variable availabilities. This work proposes a stochastic programming approach to optimize generation expansion planning (GEP) under generator supply capacity uncertainty. To better capture upside opportunities and reduce exposure to downside risks, flexibility is added to the GEP problem through real options on generator addition, which are to be exercised after uncertainty realizations. In addition, with the end goal of providing decision makers with easy-To-use guidelines, a conditional-go decision rule, akin to an if-Thenelse statement in programming, is proposed whereby the decision maker is provided with a threshold of excess total generator capacity from the previous time period, below which a predetermined generator addition plan (the option) is exercised. The proposed methodology and its decision rule are implemented in a real-world study of Midwest U.S. Comparisons are made to quantify the value of flexibility and to showcase the usefulness of the proposed approach.
Zhao S, Haskell WB, Cardin M-A, 2017, An approximate dynamic programming approach for multi-facility capacity expansion problem with flexibility design, 67th Annual Conference and Expo of the Institute of Industrial Engineers 2017, Pages: 440-445
Strategic capacity planning for multiple-facility systems with flexibility designs is an important field of the capacity expansion problem with uncertainty. The flexibilities of a multi-facility system are two-folded: the system has not only the capacity adjustment options which enables the capacity to be expanded after the realizations of uncertain demands, but also the switching options allowing the allocation of demands among different facilities. To evaluate the performance of the system, this study models the problem as a finite horizon Markov decision process (MDP) with a finite action space and employs an approximate dynamic programming (ADP) approach in solving it. Analytical results on the choice of approximation functions are provided, and bounds for the action space are also designed to decrease the searching space of optimal actions. Numerical studies illustrate that the ADP is capable of solving a realistic scale problem within a reasonable time, and the performance of the ADP given different functional approximators (i.e. polynomial function, neural networks) are also compared. At the end of this paper, limitations of this method and potential improvements are discussed.
Diao M, Cardin M-A, Zhang S, et al., 2017, Development of a Waste-to-Energy Decision Support System (WTEDSS), 67th Annual Conference and Expo of the Institute of Industrial Engineers 2017, Pages: 452-457
Rapid increase in urban population has created the need for the development of efficient Decision Support Systems (DSS) guiding municipal planners to mitigate urban sprawl, pollution and waste generation, unsustainable production and consumption patterns. To ensure sustainable urban planning, a DSS must provide not only an optimal planning solution based on input assumptions, but must also help to identify concrete city challenges, determine available resources (e.g., land and energy sources) and highlight any implementation constraints. It must support the creation of flexible interactive scenarios for urban development and their realistic representation in an urban context. This paper presents a Waste-to-Energy Decision Support System (WTEDSS) that identifies the optimal long-term deployment strategy for waste-to-energy infrastructures under future uncertain operational conditions and then directly assesses its feasibility and integration into an urban environment using 3D visualization. The WTEDSS is designed as an interactive and analytical waste management planning tool integrating four modules: data analytics, optimization, simulation and a user-friendly graphical interface. Emphasis is placed on the development and integration of the optimization module and 3D urban simulation, which provides users with decision support based on 3D visualized optimum facilities deployment plans. The optimization module receives calibrated data and solves a model based on inputs obtained from the user interface. The simulation platform developed in Unity 3D provides a friendly real-world environment for studying and understanding the facility deployment process over time and space, while also considering uncertainty.
Cardin M-A, Zhang S, Nuttall WJ, 2017, Strategic real option and flexibility analysis for nuclear power plants considering uncertainty in electricity demand and public acceptance, Energy Economics, Vol: 64, Pages: 226-237, ISSN: 0140-9883
Nuclear power is an important energy source especially in consideration of CO2 emissions and global warming. Deploying nuclear power plants, however, may be challenging when uncertainty in long-term electricity demand and more importantly public acceptance are considered. This is true especially for emerging economies (e.g., India, China) concerned with reducing their carbon footprint in the context of growing economic development, while accommodating a growing population and significantly changing demographics, as well as recent events that may affect the public's perception of nuclear technology. In the aftermath of the Fukushima Daiichi disaster, public acceptance has come to play a central role in continued operations and deployment of new nuclear power systems worldwide. In countries seeing important long-term demographic changes, it may be difficult to determine the future capacity needed, when and where to deploy it over time, and in the most economic manner. Existing studies on capacity deployment typically do not consider such uncertainty drivers in long-term capacity deployment analyses (e.g., + 40 years). To address these issues, this paper introduces a novel approach to nuclear power systems design and capacity deployment under uncertainty that exploits the idea of strategic flexibility and managerial decision rules. The approach enables dealing more pro-actively with uncertainty and helps identify the most economic deployment paths for new nuclear capacity deployment over multiple sites. One novelty of the study lies in the explicit recognition of public acceptance as an important uncertainty driver affecting economic performance, along with long-term electricity demand. Another novelty is in how the concept of flexibility is exploited to deal with uncertainty and improve expected lifecycle performance (e.g. cost). New design and deployment strategies are developed and analyzed through a multistage stochastic programming framework where decision rules a
Cardin M-A, Cherian J, 2017, Building Uncertainty, Flexibility into Infrastructure Megaprojects, Singapore, Publisher: Singapore Press Holdings
Kuznetsova E, Ng TS, Cardin MA, et al., 2017, A stochastic programming approach for the design of multi-storey recycling facility, Pages: 446-451
A rapid increase in urban population creates major challenges related to urban sprawl, pollution and waste generation, unsustainable production and consumption patterns. These challenges become even more crucial in the case of landconstrained urban territories, such as Singapore and Hong Kong, and require the development of decision-making methodologies for flexible long-term land use planning. The paper explores the possible relocation of decentralized companies with similar work processes to relocate towards centralized Multi-Storey Factories (MSF) for a higher density of land use. The developed decision-making methodologies aim, on the one hand, to maximize land savings and, on the other hand, to decrease each company's operational budget evaluated under uncertainties in future operational conditions, such as transportation costs. The optimization problem addressed has been formulated as a two-stage stochastic problem and tested for the application case of Multi-Storey Recycling Facility (MSRF). Optimization under uncertainty shows a 16.46% increase in estimated land savings in comparison with the solution obtained under deterministic conditions.
Cardin M-A, Xie Q, Ng TS, et al., 2017, An approach for analyzing and managing flexibility in engineering systems design based on decision rules and multistage stochastic programming, IISE TRANSACTIONS, Vol: 49, Pages: 1-12, ISSN: 2472-5854
Caunhye AM, Cardin M-A, 2017, An approach based on robust optimization and decision rules for analyzing real options in engineering systems design, IISE TRANSACTIONS, Vol: 49, Pages: 753-767, ISSN: 2472-5854
Cardin M-A, Fong SH, Krob D, et al., 2017, Complex Systems Design & Management Asia. Smart Nations – Sustaining and Designing: Proceedings of the Second Asia-Pacific Conference on Complex Systems Design & Management, CSD&M Asia 2016, Publisher: Springer International Publishing
Cardin M-A, Jiang Y, Lim T, 2016, Empirical studies in decision rule-based flexibility analysis for complex systems design and management, Proceedings of the 7th International Conference on Complex Systems Design and Management, CSDM Paris 2016, Pages: 171-185
© Springer International Publishing AG 2017. This paper presents the results of human subject experiments focusing on the role of decision rules in the study of flexibility and real options analysis (ROA) in design and management of complex engineering systems. Decision rules are heuristics-based triggering mechanisms that help determine the ideal conditions for exercising flexibility in system operations. In contrast to standard ROA based on dynamic programming, decision rules can be parameterized as decision variables, and therefore capture the decision-making process based on specific realizations of the main uncertainty drivers affecting system performance. Similar to standard ROA, a decision rule approach can be used to quantify the benefits of flexibility in early conceptual design studies, and help identifying the best flexible systems design concepts before a more detailed design phase. While many studies demonstrate expected lifecycle performance improvement stemming from a decision-rule based approach as compared to standard design and ROA techniques, very few studies show experimentally their effectiveness in managing flexible engineering systems. This paper presents the results of controlled human-subject experiments involving thirty-two participants evaluating a training procedure in a simulation game environment. The controlled study show that a stochastically optimal flexible strategy combined with an initial policy for the system configuration can improve significantly the expected coverage rate of medical emergencies. These provide insights for further research, development and evaluation of flexible systems design and management strategies for complex engineering systems.
Ranjbar-Bourani M, Cardin M-A, 2016, Strategic, tactical and operational flexibility in design and evaluation of a decentralized on-shore LNG production system, CIE 2016: 46th International Conferences on Computers and Industrial Engineering
The conceptual design phase of capital-intensive and long-lasting engineering systems is very important as crucial decisions need to be made at this stage. These systems generally are subject to various sources of uncertainty throughout the system lifetime. Flexibility is a proactive way to deal with such uncertainty. This paper presents a three-step methodology to quantify flexibility in engineering systems design: 1) developing a deterministic quantitative performance model; 2) developing a quantitative performance model under uncertainty and 3) developing a quantitative performance model for flexibility. The proposed methodology is then applied to a case study in designing and evaluation of a decentralized on-shore Liquefied Natural Gas (LNG) production system. Different types of flexibility are embedded in strategic, tactical and operational level decision-making of the case study. Results show that the value of flexibility increases for strategies that combine different types of flexibility. The analysis provides insight for decision makers to quantify the value of flexibility when there are different types of flexibility and motivate the use of flexibility in engineering design as a paradigm to deal with uncertainty affecting the lifecycle performance of engineering systems.
Xia T, Cardin M-A, Ranjbar-Bourani M, et al., 2016, A Decision-Making Tool to Design a Flexible Liquefied Natural Gas System under Uncertainty, Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, Pages: 789-794
© 2015 IEEE. We formulate an integrated design and evaluation mechanism applying techniques in the field of flexible engineering design and real options analysis to build a liquefied natural gas production and fueling system for a Singapore oil & gas company. The proposed methodology consists of four main steps: 1) design and evaluation under deterministic demand, 2) uncertainty characterization and performance re-evaluation under different uncertainty realization scenarios, 3) optimization under uncertainty, 4) post-optimality sensitivity analysis. Among the highlights of this work are applications of a metaheuristic method to more efficiently search for optimal flexible designs as well as extensive one-way and two-way sensitivity analyses to generate valuable insights. The final results show strong value in adding flexibility in this production and fueling system.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.