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In order to provide a reliable basis for the cost management of photovoltaic power generation, it is necessary to accurately predict the depreciation expense of photovoltaic power generation. Therefore, a hierarchical quantitative prediction method of photovoltaic power generation depreciation expense based on matrix task prioritization considering uncertain risks is proposed. Based on the conditional value-at-risk theory, a more comprehensive risk measure than VaR is provided, and the uncertainty risk value of photovoltaic power generation is calculated by considering the average loss exceeding this loss value. According to the calculated risk value, a double-layer photovoltaic power generation cost planning model is constructed, the upper and lower objective functions of the model are determined, and the constraint conditions are designed; Obtain a cost planning objective function solution base on a matrix task prioritization method, and generating a prioritization table; Prediction of photovoltaic power generation depreciation expense based on long-short memory neural network for each solution in the sorting table. In practical application, the test results show that this method can complete the risk quantitative analysis of uncertain factors, and the tracking ability and fitting degree of prediction are good; An ordered list of solutions of each objective function can be generated; The method in this paper is used to predict the depreciation expense of photovoltaic power generation in the first 10 solutions of priority ranking, and the maximum deviation of the prediction result is -0.65 million yuan. [ABSTRACT FROM AUTHOR]

In this paper, a robust fuzzy multi-objective framework is performed to optimize the dispersed and hybrid renewable photovoltaic-wind energy resources in a radial distribution network considering uncertainties of renewable generation and network demand. A novel multi-objective improved gradient-based optimizer (MOIGBO) enhanced with Rosenbrock's direct rotational technique to overcome premature convergence is proposed to determine the problem optimal decision variables. The deterministic optimization framework without uncertainty minimizes active energy loss, unmet customer energy, and renewable generation costs. The study also examines the impact of dispersed and hybrid renewable resources on solving the problem. In the robust optimization framework considering the deterministic obtained results, the focus is on determining the maximum uncertainty radius (MUR) of renewable resource generation and network demand based on the uncertainty risk. The MURs and system robustness are optimally determined using information gap decision theory (IGDT) and the MOIGBO, considering various uncertainty budgets under worst-case scenarios. The deterministic results indicate that the MOIGBO effectively balances the objectives and identifies the final solution within the Pareto front, according to fuzzy decision-making. The results also reveal that the dispersed case yields better objective values than the hybrid case. Furthermore, the MOIGBO outperforms MOGBO and multi-objective particle swarm optimization (MOPSO) in improving distribution network operations. The robust results show that maximum system robustness is achieved at 30% uncertainty risk due to forecasting errors, with MUR values of 0.54% for resource production and 12.56% for load demand. [ABSTRACT FROM AUTHOR]

Abstract In order to provide a reliable basis for the cost management of photovoltaic power generation, it is necessary to accurately predict the depreciation expense of photovoltaic power generation. Therefore, a hierarchical quantitative prediction method of photovoltaic power generation depreciation expense based on matrix task prioritization considering uncertain risks is proposed. Based on the conditional value-at-risk theory, a more comprehensive risk measure than VaR is provided, and the uncertainty risk value of photovoltaic power generation is calculated by considering the average loss exceeding this loss value. According to the calculated risk value, a double-layer photovoltaic power generation cost planning model is constructed, the upper and lower objective functions of the model are determined, and the constraint conditions are designed; Obtain a cost planning objective function solution base on a matrix task prioritization method, and generating a prioritization table; Prediction of photovoltaic power generation depreciation expense based on long-short memory neural network for each solution in the sorting table. In practical application, the test results show that this method can complete the risk quantitative analysis of uncertain factors, and the tracking ability and fitting degree of prediction are good; An ordered list of solutions of each objective function can be generated; The method in this paper is used to predict the depreciation expense of photovoltaic power generation in the first 10 solutions of priority ranking, and the maximum deviation of the prediction result is -0.65 million yuan.

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Compared with the traditional operation mode of emergency vehicles, the mobile emergency vehicle is regarded as a new type of emergency facility carrier with the features of variable locations, flexible mobility, and intelligent decision-making. It can provide an effective solution to reasonably respond to the uncertain risks of sudden disasters. Focusing on meeting the maximum demand for materials and services in disaster areas, this paper proposes a scheduling model of mobile emergency vehicles with dual uncertainty of path and demand. The model, solved by an integer-coding hybrid genetic algorithm, aims to obtain minimum mobile emergency scheduling cost and time by transforming the multi-objective problem into a single-objective problem. The "5.12" Wenchuan earthquake is used as an example to validate the model and solving method. The results show that the model can reduce the impact of uncertain risks and improve the scientific logic of emergency strategies and deployments based on the actual crisis scenario. It benefits from introducing mobile emergency vehicles and optimizing their scheduling process. [ABSTRACT FROM AUTHOR]

Abstract Sudden and uncertain events often cause cross-contagion of risk among various sectors of the macroeconomy. This paper introduces the stochastic volatility shock that follows a thick-tailed Student’s t-distribution into a high-order approximate dynamic stochastic general equilibrium (DSGE) model with Epstein–Zin preference to better analyze the dynamic effect of uncertainty risk on macroeconomics. Then, the high-dimensional DSGE model (DSGE-SV-t) is developed to examine the impact of uncertainty risk on the transmission mechanism among macroeconomic sectors. The empirical research found that uncertainty risk generates heterogeneous impacts on macroeconomic dynamics under different inflation levels and economic states. Among them, a technological shock has the strongest impact on employment and consumption channels. The crowding-out effect of a fiscal policy stimulus on consumption and private investments is relatively weakened when considering uncertainty risk but is more pronounced during periods of high inflation. Uncertainty risk can partly explain the decline in investments and the increase in interest rates and employment rates, given the impact of an agent’s risk preferences. Compared with external economic conditions, the inflation factor has a stronger impact on the macro transmission mechanism caused by uncertainty risk.

Compared with the traditional operation mode of emergency vehicles, the mobile emergency vehicle is regarded as a new type of emergency facility carrier with the features of variable locations, flexible mobility, and intelligent decision-making. It can provide an effective solution to reasonably respond to the uncertain risks of sudden disasters. Focusing on meeting the maximum demand for materials and services in disaster areas, this paper proposes a scheduling model of mobile emergency vehicles with dual uncertainty of path and demand. The model, solved by an integer-coding hybrid genetic algorithm, aims to obtain minimum mobile emergency scheduling cost and time by transforming the multi-objective problem into a single-objective problem. The “5.12” Wenchuan earthquake is used as an example to validate the model and solving method. The results show that the model can reduce the impact of uncertain risks and improve the scientific logic of emergency strategies and deployments based on the actual crisis scenario. It benefits from introducing mobile emergency vehicles and optimizing their scheduling process.

The uncertainty of distributed energy generation and the uncertainty risk of system components failure are important means to ameliorate the economy and reliability of Active Distribution Network (ADN). In this paper, a two-stage optimal scheduling method for ADN considering uncertainty risk is proposed. Considering the uncertainty risk of renewable resources, load and electricity price, and the uncertainty risk of system component failure, the two-stage optimal dispatching process of ADN with energy storage system is investigated. Firstly, the first stage model is established to minimize the cost of operation of ADN under normal operation conditions, and then the second stage model is established to minimize the load reduction under emergency operation. Particle Swarm Optimization (PSO) algorithm is proposed for model optimization. The simulation outcomes on IEEE-33 bus system simulation demonstrated high effectiveness of the proposed method.

Sudden and uncertain events often cause cross-contagion of risk among various sectors of the macroeconomy. This paper introduces the stochastic volatility shock that follows a thick-tailed Student's t-distribution into a high-order approximate dynamic stochastic general equilibrium (DSGE) model with Epstein–Zin preference to better analyze the dynamic effect of uncertainty risk on macroeconomics. Then, the high-dimensional DSGE model (DSGE-SV-t) is developed to examine the impact of uncertainty risk on the transmission mechanism among macroeconomic sectors. The empirical research found that uncertainty risk generates heterogeneous impacts on macroeconomic dynamics under different inflation levels and economic states. Among them, a technological shock has the strongest impact on employment and consumption channels. The crowding-out effect of a fiscal policy stimulus on consumption and private investments is relatively weakened when considering uncertainty risk but is more pronounced during periods of high inflation. Uncertainty risk can partly explain the decline in investments and the increase in interest rates and employment rates, given the impact of an agent's risk preferences. Compared with external economic conditions, the inflation factor has a stronger impact on the macro transmission mechanism caused by uncertainty risk. [ABSTRACT FROM AUTHOR]

Purpose: Project deviations are so significant that various methods have been proposed to measure them over the years. Earned Duration Management (EDM) is a method that measures project evaluation indexes only by considering the past trend of project execution compared to the baseline program. It ignores all the other factors, which cause much information to be lost to guide managers. Therefore, in this study, a process is proposed. During this process, it is possible to develop EDM indicators using key performance indicators of project quality in a fuzzy manner and based on the identified substantial risks. Design/methodology/approach: A list of risks was initially identified based on the PMBOK standard's category, and project managers selected the most important ones. In the next step, based on the most critical risks, two key indicators of project equipment and execution performance were defined. Such indicators were presented to managers again to determine their future impact on the project. By defining a triangular fuzzy number, the definite evaluations became fuzzy, since experts expressed the effect in linguistic variables. Finally, the indicators entered into the EDM, and the method was developed. Findings: In this study, Key Performance Indicators were used to increase EDM’s accuracy. First, the three risks of delay in receiving project resources, lack of timely allocation of financial resources, and disruption in materials preparation were identified as the main risks. Then, to study the status of the risks, two indicators were defined (PSI, PEI). These two indicators were estimated, up to 50% of the simulated project. Concerning the first indicator, the more the value, the more acceptable project performance was. Considering another indicator, the situation was contrariwise. Then, the future status of the indicators was examined by consulting managers and making the indicators in a fuzzy manner. The effect of the indicators was determined through the EDTC variable in the EDM. It should be noted that the final estimated time was used to measure the accuracy of the proposed method. The studied project was simulated using the prevailing situation in Matlab software. The end time of this simulated project was estimated at 1300 days. Then, the project was evaluated day by day till its 50% completion using EDM, and the end time of 1225 days was estimated. The same evaluation was performed for the proposed method, and the final time (1270, 1247, 1240) was determined. Because this time was closer to the simulated time, it was evident that the proposed method had the highest accuracy. Research limitations/implications: In this study, to prevent information disclosure, project simulation was used instead of the actual project to evaluate the indicators and advance the project. Therefore, although the accuracy was estimated in this paper, its more exact value might be requested in future research. Consequently, the proposed method can be applied in an actual project, and the accuracy of the model can be measured, consequently. Practical implications: The proposed process and method in this research can be used in the implementation and planning stages of any other project. The process should be localized because the risks vary from project to project. Besides, the critical risks of each project cannot be compared with the other projects. Therefore, after examining each project's risks and defining indicators related to the selected risks, the proposed method can be applied in all project-based organizations and on any project type. Social implications: Today, the construction and operation of projects are among the factors that affect the countries' growth and development. Deviations from the budgets allocated to the project are an integral part of project implementation and are not dependent upon a specific area or even a particular management and implementation method. Therefore, proper planning and control of projects are essential. This paper provides an effective model for senior project managers to complete projects on time. Successful implementation of projects leads to economic prosperity. Also, improving the quality and performance provides a calmer social environment for people and creates many environmental benefits. Originality/value: In this study, the indicators of the EDM method were developed using the key performance indexes. Such indexes were associated with the quality of project performance execution and were estimated under uncertainty conditions; thereby, more information can be transferred to the project management. The defined performance indicators are measurable during the process, and their impact on the ongoing project is monitored. The innovations of this study are twofold: Applying the performance index developed the EDM method; and defining the quality performance index in fuzzy terms. [ABSTRACT FROM AUTHOR]

If it is considered that the management process focuses on the development, adoption and implementation of the decisions, and the efficiency of the economic agents' activity depends on the quality of these, it can be said that the decision is the essential moment of the management. The manifestation of the management functions, in all their complexity, involves the elaboration and implementation of a large number of decisions in all fields of activity that take place in an economic entity. A manager’s entire activity consists of a chain of decisions, with the indication that their effectiveness depends on the extent to which their development is scientifically based, taking into account many factors which condition the application in practice. Hence the need for full understanding of the entire decision-making process. Based on these considerations, the current paper approaches the concept of decision, emphasizing its importance in the management activity.

• There are information spillovers from energy-related uncertainty to the oil market. • Overall and asymmetric changes in the EUI can affect crude oil market volatility. • The positive/negative behaviors of the EUI offer more valuable information for model. • The GARCH-MIDAS model with double asymmetric produces optimal predictive efficacy. Recently, numerous events, such as trade conflicts, the United Kingdom's exit from the European Union, the Russian-Ukrainian war, and the Palestinian-Israeli conflict, have led to increased risks of uncertainty in global economic, political, and energy markets. However, despite the essential role of crude oil markets in national security and economic development, existing studies have paid little attention to how country-level uncertainty affects oil price volatility. Therefore, in this paper, we investigate whether the energy-related uncertainty index (EUI) proposed by Dang et al. (2023) affects oil price volatility through the GARCH-MIDAS framework. We first analyze whether the EUI can play a role in the oil market through parameter estimation of the model and find evidence of information spillovers from the EUI to the oil market. We then use tests to examine the accuracy of the econometric models used in this paper for WTI volatility forecasting. The test results show that the Double Asymmetric GARCH-MIDAS-EUI model has excellent forecasting performance. Therefore, this work is valuable for governments and corporations in formulating energy policies and business strategies to better cope with the risk of market uncertainty. [ABSTRACT FROM AUTHOR]

There are many ambiguous and unknown features in running and management of different projects especially in constructional projects. Which is called uncertainty? This study tries to recognize the uncertainty cases in constructional projects, then categorize them in risk and opportunities to be analyzed so as to design a plan to encounter risks in a proper way and use the opportunities in time. It is a survey and field research. The statistical population was the consulting and construction corporations active in sanandije. The data was gathered through a questionnaire according the standards in risk management (in particular PMBok) and their steps were analyzed. The constructional project dimensions were explained by introducing nine duties of project manager. The findings showed 36 risk items in six categories: technical and technological, work conditions, economical and financial, organizational, sociocultural. The effect of each one of them on project objectives and the importance of each risk was measured.

The mortality dynamics experienced in the latest decades, especially at adult and old ages, has motivated the introduction of major innovations in the modeling of mortality for actuarial applications; such innovations concern, in particular, the representation of the uncertainty relating to aggregate mortality. In this paper, we first provide a description of the traditional mortality model which is deterministic but also allows quite easily for a representation of the uncertainty relating to individual mortality. Then, we discuss a stochastic approach to the modeling of the uncertainty relating to aggregate mortality. Due to the importance of mortality evolution in respect of post-retirement liabilities, we refer to a portfolio of immediate life annuities (or pension annuities). We assume that a (projected) life table which provides a best-estimate assessment of annuitants' future mortality is available. We show that the life table, from which a deterministic description of future mortality can be obtained, can be used as the basic input of appropriate stochastic models. In particular, we consider a Bayesian-inference setting for updating the parameters of the stochastic model according to the experienced mortality. [ABSTRACT FROM AUTHOR]

The exclusion of employment-based health insurance from income and payroll taxes is thought to increase the generosity of insurance coverage and, in turn, increase the overutilization of low-value health care services. We examine this inefficiency of overinsurance by quantifying the change in expected utility across 4 benchmark plans varying in actuarial value (AV) and focus on the distribution of each of these estimates across different groups of people varying in health status. Specifically, we quantify the changes in health care spending due to moral hazard and the changes in uncertainty tied to risk aversion using data from the nationally representative sample of adults with employment-based coverage from the 2007-2016 Medical Expenditure Panel Survey, and produce estimates of expected utility for 24 groups of people based on their age, gender, and preexisting conditions. Our model suggests an average preferred AV of 78% without the tax exclusion, with 29.0% of the population preferring a 60% AV, 6.5% preferring a 70% AV, 18.1% preferring an 80% AV, and 46.4% preferring a 90% AV. When incorporating the distortionary effect of the employment-based tax exclusion, the preferred plan increases to an 83% AV for low-income people (with 71.0% of the population preferring a 90% AV) and an 84% AV for high-income people (with 76.0% of the population preferring a 90% AV). We estimate that policy changes to make subsidies independent of a plan's AV could result in increases in utility equal to about 2.7% of total health care spending, but with those net gains concentrated among the healthy.