Starting with the meanings of the terms “risk” and “uncertainty,””he paper compares uncertainty management with risk management in project management. We bring some doubt to the use of “risk” and “uncertainty...Starting with the meanings of the terms “risk” and “uncertainty,””he paper compares uncertainty management with risk management in project management. We bring some doubt to the use of “risk” and “uncertainty” interchangeably in project management and deem their scope, methods, responses, monitoring and controlling should be different too. Illustrations are given covering terminology, description, and treatment from different perspectives of uncertainty management and risk management. Furthermore, the paper retains that project risk management (PRM) processes might be modified to facilitate an uncertainty management perspective, and we support that project uncertainty management (PUM) can enlarge its contribution to improving project management performance, which will result in a significant change in emphasis compared with most risk management.展开更多
In this paper, a data-driven prognostic model capable to deal with different sources of uncertainty is proposed. The main novelty factor is the application of a mathematical framework, namely a Random Fuzzy Variable (...In this paper, a data-driven prognostic model capable to deal with different sources of uncertainty is proposed. The main novelty factor is the application of a mathematical framework, namely a Random Fuzzy Variable (RFV) approach, for the representation and propagation of the different uncertainty sources affecting </span><span style="font-family:Verdana;">Prognostic Health Management (PHM) applications: measurement, future and model uncertainty. </span><span style="font-family:Verdana;">In this way, it is possible to deal not only with measurement noise and model parameters uncertainty due to the stochastic nature of the degradation process, but also with systematic effects, such as systematic errors in the measurement process, incomplete knowledge of the degradation process, subjective belief about model parameters. Furthermore, the low analytical complexity of the employed prognostic model allows to easily propagate the measurement and parameters uncertainty into the RUL forecast, with no need of extensive Monte Carlo loops, so that low requirements in terms of computation power are needed. The model has been applied to two real application cases, showing high accuracy output, resulting in a potential</span></span><span style="font-family:Verdana;">ly</span><span style="font-family:Verdana;"> effective tool for predictive maintenance in different industrial sectors.展开更多
The construction projects’ dynamic and interconnected nature requires a comprehensive understanding of complexity during pre-construction. Traditional tools such as Gantt charts, CPM, and PERT often overlook uncertai...The construction projects’ dynamic and interconnected nature requires a comprehensive understanding of complexity during pre-construction. Traditional tools such as Gantt charts, CPM, and PERT often overlook uncertainties. This study identifies 20 complexity factors through expert interviews and literature, categorising them into six groups. The Analytical Hierarchy Process evaluated the significance of different factors, establishing their corresponding weights to enhance adaptive project scheduling. A system dynamics (SD) model is developed and tested to evaluate the dynamic behaviour of identified complexity factors. The model simulates the impact of complexity on total project duration (TPD), revealing significant deviations from initial deterministic estimates. Data collection and analysis for reliability tests, including normality and Cronbach alpha, to validate the model’s components and expert feedback. Sensitivity analysis confirmed a positive relationship between complexity and project duration, with higher complexity levels resulting in increased TPD. This relationship highlights the inadequacy of static planning approaches and underscores the importance of addressing complexity dynamically. The study provides a framework for enhancing planning systems through system dynamics and recommends expanding the model to ensure broader applicability in diverse construction projects.展开更多
The rapid growth of distributed generator(DG)capacities has introduced additional controllable assets to improve the performance of distribution systems in terms of service restoration.Renewable DGs are of particular ...The rapid growth of distributed generator(DG)capacities has introduced additional controllable assets to improve the performance of distribution systems in terms of service restoration.Renewable DGs are of particular interest to utility companies,but the stochastic nature of intermittent renewable DGs could have a negative impact on the electric grid if they are not properly handled.In this study,we investigate distribution system service restoration using DGs as the primary power source,and we develop an effective approach to handle the uncertainty of renewable DGs under extreme conditions.The distribution system service restoration problem can be described as a mixed-integer second-order cone programming model by modifying the radial topology constraints and power flow equations.The uncertainty of renewable DGs will be modeled using a chance-constrained approach.Furthermore,the forecast errors and noises in real-time operation are solved using a novel model-free control algorithm that can automatically track the trajectory of real-time DG output.The proposed service restoration strategy and model-free control algorithm are validated using an IEEE 123-bus test system.展开更多
In the pharmaceutical industry,model-based prediction is a crucial stage in process development that allows pharmaceutical companies to simulate different scenarios toward improving process efficiency,reducing costs,a...In the pharmaceutical industry,model-based prediction is a crucial stage in process development that allows pharmaceutical companies to simulate different scenarios toward improving process efficiency,reducing costs,and enhancing product quality.Nevertheless,ensuring the quality of formulated pharmaceutical products through the management of raw material variations has always been a challenging task.In this work,data-driven chance-constrained recurrent neural networks(CCRNNs)are developed to address the issue arising from raw material uncertainty.Our goal is to explore how,by proactively incorporating uncertainty into the model training process,more accurate predictions and enhanced robustness can be realized.The proposed approach is tested on a fluid bed dryer(FBD)from a continuous pharmaceutical manufacturing pilot plant.The results demonstrate that CCRNN models offer more robust and accurate predictions for the critical quality attribute(CQA)-in this case,moisture content-when material variations occur,compared with conventional recurrent neural network-based models.展开更多
The concept of value of information(VOI)has been widely used in the oil industry when making decisions on the acquisition of new data sets for the development and operation of oil fields.The classical approach to VOI ...The concept of value of information(VOI)has been widely used in the oil industry when making decisions on the acquisition of new data sets for the development and operation of oil fields.The classical approach to VOI assumes that the outcome of the data acquisition process produces crisp values,which are uniquely mapped onto one of the deterministic reservoir models representing the subsurface variability.However,subsurface reservoir data are not always crisp;it can also be fuzzy and may correspond to various reservoir models to different degrees.The classical approach to VOI may not,therefore,lead to the best decision with regard to the need to acquire new data.Fuzzy logic,introduced in the 1960 s as an alternative to the classical logic,is able to manage the uncertainty associated with the fuzziness of the data.In this paper,both classical and fuzzy theoretical formulations for VOI are developed and contrasted using inherently vague data.A case study,which is consistent with the future development of an oil reservoir,is used to compare the application of both approaches to the estimation of VOI.The results of the VOI process show that when the fuzzy nature of the data is included in the assessment,the value of the data decreases.In this case study,the results of the assessment using crisp data and fuzzy data change the decision from"acquire"the additional data(in the former)to"do not acquire"the additional data(in the latter).In general,different decisions are reached,depending on whether the fuzzy nature of the data is considered during the evaluation.The implications of these results are significant in a domain such as the oil and gas industry(where investments are huge).This work strongly suggests the need to define the data as crisp or fuzzy for use in VOI,prior to implementing the assessment to select and define the right approach.展开更多
This article considers threats to a project slipping on budget,schedule and fit-for-purpose.Threat is used here as the collective for risks（quantifiable bad things that can happen）and uncertainties（poorly or not qu...This article considers threats to a project slipping on budget,schedule and fit-for-purpose.Threat is used here as the collective for risks（quantifiable bad things that can happen）and uncertainties（poorly or not quantifiable bad possible events）.Based on experience with projects in developing countries this review considers that（a）project slippage is due to uncertainties rather than risks,（b）while eventuation of some bad things is beyond control,managed execution and oversight are stil the primary means to keeping within budget,on time and fit-for-purpose,（c）improving project delivery is less about bigger and more complex and more about coordinated focus,effectiveness and developing thought-out heuristics,and（d）projects take longer and cost more partly because threat identification is inaccurate,the scope of identified threats is too narrow,and the threat assessment product is not integrated into overall project decision-making and execution.Almost by definition,what is poorly known is likely to cause problems.Yet it is not just the unquantifiability and intangibility of uncertainties causing project slippage,but that they are insufficiently taken into account in project planning and execution that cause budget and time overruns.Improving project performance requires purpose-driven and managed deployment of scarce seasoned professionals.This can be aided with independent oversight by deeply experienced panelists who contribute technical insights and can potentially show that diligence is seen to be done.展开更多
Developing an anthropogenic carbon dioxides(CO_(2))emissions monitoring and verification support(MVS)capacity is essential to support the Global Stocktake(GST)and ratchet up Nationally Determined Contributions(NDCs).T...Developing an anthropogenic carbon dioxides(CO_(2))emissions monitoring and verification support(MVS)capacity is essential to support the Global Stocktake(GST)and ratchet up Nationally Determined Contributions(NDCs).The 2019 IPCC refinement proposes top-down inversed CO_(2)emissions,primarily from fossil fuel(FFCO_(2)),as a viable emission dataset.Despite substantial progress in directly inferring FFCO_(2)emissions from CO_(2)observations,substantial challenges remain,particularly in distinguishing local CO_(2)enhancements from the high background due to the long atmospheric lifetime.Alternatively,using short-lived and co-emitted nitrogen dioxide(NO_(2))as a proxy in FFCO_(2)emission inversion has gained prominence.This methodology is broadly categorized into plume-based and emission ratios(ERs)-based inversion methods.In the plume-based methods,NO_(2)observations act as locators,constraints,and validators for deciphering CO_(2)plumes downwind of sources,typically at point source and city scales.The ERs-based inversion approach typically consists of two steps:inferring NO_(2)-based nitrogen oxides(NO_(x))emissions and converting NO_(x)to CO_(2)emissions using CO_(2)-to-NO_(x)ERs.While integrating NO_(2)observations into FFCO_(2)emission inversion offers advantages over the direct CO_(2)-based methods,uncertainties persist,including both structural and data-related uncertainties.Addressing these uncertainties is a primary focus for future research,which includes deploying nextgeneration satellites and developing advanced inversion systems.Besides,data caveats are necessary when releasing data to users to prevent potential misuse.Advancing NO_(2)-based CO_(2)emission inversion requires interdisciplinary collaboration across multiple communities of remote sensing,emission inventory,transport model improvement,and atmospheric inversion algorithm development.展开更多
Rockbursts occur as a direct consequence of underground mining or civil excavation.The general scale of their seismic disturbance and consequences depend upon known factors.However,uncertainty remains as to exactly wh...Rockbursts occur as a direct consequence of underground mining or civil excavation.The general scale of their seismic disturbance and consequences depend upon known factors.However,uncertainty remains as to exactly when and where rockbursts will occur,as well as the effectiveness of ground support measures to fully mitigate their consequences.While the uncertainty in when and where is a dilemma shared with earthquake prediction,that associated with ground support capability is both a design and a management concern.Following a brief review of the known mechanisms that produce rockbursts,the paper explores the sources and scales of energy demands that characterize the risk of their damaging consequences upon underground excavations.We note that some of this risk continues to be associated with uncertainty with respect to rockmass properties and in situ stress,particularly in the context of deep mining.A review is presented of all available yielding ground support systems and their necessary design requirements,identifying practical weaknesses and limitations where these are known.The paper concludes with some suggested areas where further study and development could provide the ways and means to reduce the design uncertainty in managing rockbursts.展开更多
In this study, interval-parameter programming, two-stage stochastic progranaming （TSP）, and conditional value-at-risk （CVaR） were incorporated into a general optimization framework, leading to an interval-paramete...In this study, interval-parameter programming, two-stage stochastic progranaming （TSP）, and conditional value-at-risk （CVaR） were incorporated into a general optimization framework, leading to an interval-parameter CVaR-based two-stage programming （ICTP） method. The ICTP method had several advantages： （i） its objective function simultaneously took expected cost and risk cost into consideration, and also used discrete random variables and discrete intervals to reflect uncertain properties; （ii） it quantitatively evaluated the right tail of distributions of random variables which could better calculate the risk of violated environmental standards; （iii） it was useful for helping decision makers to analyze the trade-offs between cost and risk; and （iv） it was effective to penalize the second-stage costs, as well as to capture the notion of risk in stochastic programming. The developed model was applied to sulfur dioxide abatement in an air quality management system. The results indicated that the ICTP method could be used for generating a series of air quality management schemes under different risk-aversion levels, for identifying desired air quality management strategies for decision makers, and for considering a proper balance between system economy and environmental quality.展开更多
文摘Starting with the meanings of the terms “risk” and “uncertainty,””he paper compares uncertainty management with risk management in project management. We bring some doubt to the use of “risk” and “uncertainty” interchangeably in project management and deem their scope, methods, responses, monitoring and controlling should be different too. Illustrations are given covering terminology, description, and treatment from different perspectives of uncertainty management and risk management. Furthermore, the paper retains that project risk management (PRM) processes might be modified to facilitate an uncertainty management perspective, and we support that project uncertainty management (PUM) can enlarge its contribution to improving project management performance, which will result in a significant change in emphasis compared with most risk management.
文摘In this paper, a data-driven prognostic model capable to deal with different sources of uncertainty is proposed. The main novelty factor is the application of a mathematical framework, namely a Random Fuzzy Variable (RFV) approach, for the representation and propagation of the different uncertainty sources affecting </span><span style="font-family:Verdana;">Prognostic Health Management (PHM) applications: measurement, future and model uncertainty. </span><span style="font-family:Verdana;">In this way, it is possible to deal not only with measurement noise and model parameters uncertainty due to the stochastic nature of the degradation process, but also with systematic effects, such as systematic errors in the measurement process, incomplete knowledge of the degradation process, subjective belief about model parameters. Furthermore, the low analytical complexity of the employed prognostic model allows to easily propagate the measurement and parameters uncertainty into the RUL forecast, with no need of extensive Monte Carlo loops, so that low requirements in terms of computation power are needed. The model has been applied to two real application cases, showing high accuracy output, resulting in a potential</span></span><span style="font-family:Verdana;">ly</span><span style="font-family:Verdana;"> effective tool for predictive maintenance in different industrial sectors.
文摘The construction projects’ dynamic and interconnected nature requires a comprehensive understanding of complexity during pre-construction. Traditional tools such as Gantt charts, CPM, and PERT often overlook uncertainties. This study identifies 20 complexity factors through expert interviews and literature, categorising them into six groups. The Analytical Hierarchy Process evaluated the significance of different factors, establishing their corresponding weights to enhance adaptive project scheduling. A system dynamics (SD) model is developed and tested to evaluate the dynamic behaviour of identified complexity factors. The model simulates the impact of complexity on total project duration (TPD), revealing significant deviations from initial deterministic estimates. Data collection and analysis for reliability tests, including normality and Cronbach alpha, to validate the model’s components and expert feedback. Sensitivity analysis confirmed a positive relationship between complexity and project duration, with higher complexity levels resulting in increased TPD. This relationship highlights the inadequacy of static planning approaches and underscores the importance of addressing complexity dynamically. The study provides a framework for enhancing planning systems through system dynamics and recommends expanding the model to ensure broader applicability in diverse construction projects.
基金the National Renewable Energy Laboratory(NREL)operated by Alliance for Sustainable Energy,LLC,for the U.S.Department of Energy(DOE)under Contract No.DE-AC36-08GO28308the U.S.Department of Energy Office of Electricity AOP Distribution Grid Resilience Project.The views expressed in the article do not necessarily represent the views of the DOE or the U.S.Government.The U.S.Government retains and the publisher,by accepting the article for publication,acknowledges that the U.S.Government retains a nonexclusive,paid-up,irrevocable,worldwide license to publish or reproduce the published form of this work,or allow others to do so,for U.S.Government purposes.
文摘The rapid growth of distributed generator(DG)capacities has introduced additional controllable assets to improve the performance of distribution systems in terms of service restoration.Renewable DGs are of particular interest to utility companies,but the stochastic nature of intermittent renewable DGs could have a negative impact on the electric grid if they are not properly handled.In this study,we investigate distribution system service restoration using DGs as the primary power source,and we develop an effective approach to handle the uncertainty of renewable DGs under extreme conditions.The distribution system service restoration problem can be described as a mixed-integer second-order cone programming model by modifying the radial topology constraints and power flow equations.The uncertainty of renewable DGs will be modeled using a chance-constrained approach.Furthermore,the forecast errors and noises in real-time operation are solved using a novel model-free control algorithm that can automatically track the trajectory of real-time DG output.The proposed service restoration strategy and model-free control algorithm are validated using an IEEE 123-bus test system.
基金Financial support from the Engineering and Physical Sciences Research Council grant EP/V034723/1(RiFTMaP)is gratefully acknowledged.
文摘In the pharmaceutical industry,model-based prediction is a crucial stage in process development that allows pharmaceutical companies to simulate different scenarios toward improving process efficiency,reducing costs,and enhancing product quality.Nevertheless,ensuring the quality of formulated pharmaceutical products through the management of raw material variations has always been a challenging task.In this work,data-driven chance-constrained recurrent neural networks(CCRNNs)are developed to address the issue arising from raw material uncertainty.Our goal is to explore how,by proactively incorporating uncertainty into the model training process,more accurate predictions and enhanced robustness can be realized.The proposed approach is tested on a fluid bed dryer(FBD)from a continuous pharmaceutical manufacturing pilot plant.The results demonstrate that CCRNN models offer more robust and accurate predictions for the critical quality attribute(CQA)-in this case,moisture content-when material variations occur,compared with conventional recurrent neural network-based models.
文摘The concept of value of information(VOI)has been widely used in the oil industry when making decisions on the acquisition of new data sets for the development and operation of oil fields.The classical approach to VOI assumes that the outcome of the data acquisition process produces crisp values,which are uniquely mapped onto one of the deterministic reservoir models representing the subsurface variability.However,subsurface reservoir data are not always crisp;it can also be fuzzy and may correspond to various reservoir models to different degrees.The classical approach to VOI may not,therefore,lead to the best decision with regard to the need to acquire new data.Fuzzy logic,introduced in the 1960 s as an alternative to the classical logic,is able to manage the uncertainty associated with the fuzziness of the data.In this paper,both classical and fuzzy theoretical formulations for VOI are developed and contrasted using inherently vague data.A case study,which is consistent with the future development of an oil reservoir,is used to compare the application of both approaches to the estimation of VOI.The results of the VOI process show that when the fuzzy nature of the data is included in the assessment,the value of the data decreases.In this case study,the results of the assessment using crisp data and fuzzy data change the decision from"acquire"the additional data(in the former)to"do not acquire"the additional data(in the latter).In general,different decisions are reached,depending on whether the fuzzy nature of the data is considered during the evaluation.The implications of these results are significant in a domain such as the oil and gas industry(where investments are huge).This work strongly suggests the need to define the data as crisp or fuzzy for use in VOI,prior to implementing the assessment to select and define the right approach.
文摘This article considers threats to a project slipping on budget,schedule and fit-for-purpose.Threat is used here as the collective for risks（quantifiable bad things that can happen）and uncertainties（poorly or not quantifiable bad possible events）.Based on experience with projects in developing countries this review considers that（a）project slippage is due to uncertainties rather than risks,（b）while eventuation of some bad things is beyond control,managed execution and oversight are stil the primary means to keeping within budget,on time and fit-for-purpose,（c）improving project delivery is less about bigger and more complex and more about coordinated focus,effectiveness and developing thought-out heuristics,and（d）projects take longer and cost more partly because threat identification is inaccurate,the scope of identified threats is too narrow,and the threat assessment product is not integrated into overall project decision-making and execution.Almost by definition,what is poorly known is likely to cause problems.Yet it is not just the unquantifiability and intangibility of uncertainties causing project slippage,but that they are insufficiently taken into account in project planning and execution that cause budget and time overruns.Improving project performance requires purpose-driven and managed deployment of scarce seasoned professionals.This can be aided with independent oversight by deeply experienced panelists who contribute technical insights and can potentially show that diligence is seen to be done.
基金supported by the National Natural Science Foundation of China(No.42105094).
文摘Developing an anthropogenic carbon dioxides(CO_(2))emissions monitoring and verification support(MVS)capacity is essential to support the Global Stocktake(GST)and ratchet up Nationally Determined Contributions(NDCs).The 2019 IPCC refinement proposes top-down inversed CO_(2)emissions,primarily from fossil fuel(FFCO_(2)),as a viable emission dataset.Despite substantial progress in directly inferring FFCO_(2)emissions from CO_(2)observations,substantial challenges remain,particularly in distinguishing local CO_(2)enhancements from the high background due to the long atmospheric lifetime.Alternatively,using short-lived and co-emitted nitrogen dioxide(NO_(2))as a proxy in FFCO_(2)emission inversion has gained prominence.This methodology is broadly categorized into plume-based and emission ratios(ERs)-based inversion methods.In the plume-based methods,NO_(2)observations act as locators,constraints,and validators for deciphering CO_(2)plumes downwind of sources,typically at point source and city scales.The ERs-based inversion approach typically consists of two steps:inferring NO_(2)-based nitrogen oxides(NO_(x))emissions and converting NO_(x)to CO_(2)emissions using CO_(2)-to-NO_(x)ERs.While integrating NO_(2)observations into FFCO_(2)emission inversion offers advantages over the direct CO_(2)-based methods,uncertainties persist,including both structural and data-related uncertainties.Addressing these uncertainties is a primary focus for future research,which includes deploying nextgeneration satellites and developing advanced inversion systems.Besides,data caveats are necessary when releasing data to users to prevent potential misuse.Advancing NO_(2)-based CO_(2)emission inversion requires interdisciplinary collaboration across multiple communities of remote sensing,emission inventory,transport model improvement,and atmospheric inversion algorithm development.
文摘Rockbursts occur as a direct consequence of underground mining or civil excavation.The general scale of their seismic disturbance and consequences depend upon known factors.However,uncertainty remains as to exactly when and where rockbursts will occur,as well as the effectiveness of ground support measures to fully mitigate their consequences.While the uncertainty in when and where is a dilemma shared with earthquake prediction,that associated with ground support capability is both a design and a management concern.Following a brief review of the known mechanisms that produce rockbursts,the paper explores the sources and scales of energy demands that characterize the risk of their damaging consequences upon underground excavations.We note that some of this risk continues to be associated with uncertainty with respect to rockmass properties and in situ stress,particularly in the context of deep mining.A review is presented of all available yielding ground support systems and their necessary design requirements,identifying practical weaknesses and limitations where these are known.The paper concludes with some suggested areas where further study and development could provide the ways and means to reduce the design uncertainty in managing rockbursts.
文摘In this study, interval-parameter programming, two-stage stochastic progranaming （TSP）, and conditional value-at-risk （CVaR） were incorporated into a general optimization framework, leading to an interval-parameter CVaR-based two-stage programming （ICTP） method. The ICTP method had several advantages： （i） its objective function simultaneously took expected cost and risk cost into consideration, and also used discrete random variables and discrete intervals to reflect uncertain properties; （ii） it quantitatively evaluated the right tail of distributions of random variables which could better calculate the risk of violated environmental standards; （iii） it was useful for helping decision makers to analyze the trade-offs between cost and risk; and （iv） it was effective to penalize the second-stage costs, as well as to capture the notion of risk in stochastic programming. The developed model was applied to sulfur dioxide abatement in an air quality management system. The results indicated that the ICTP method could be used for generating a series of air quality management schemes under different risk-aversion levels, for identifying desired air quality management strategies for decision makers, and for considering a proper balance between system economy and environmental quality.
