From risk management to uncertainty management: a significant change in project management

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.

A Possibilistic Approach for Uncertainty Representation and Propagation in Similarity-Based Prognostic Health Management Solutions

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 Prognostic Health Management (PHM) applications: measurement, future and model uncertainty. 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 potentially effective tool for predictive maintenance in different industrial sectors.

Distributed generator-based distribution system service restoration strategy and model-free control methods

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.

Fuzzy logic applied to value of information assessment in oil and gas projects

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.

Managing project risks and uncertainties

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.

Hardrock burst mechanisms and management strategies

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.

Risk management for sulfur dioxide abatement under multiple uncertainties

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.