Search results for: intolerance of uncertainty

Authors: Hamed Afrasiab, Saeed Khodaygan

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Prediction of machining process capability in the design stage plays a key role to reach the precision design and manufacturing of mechanical products. Inaccuracies in machining process lead to errors in position and orientation of machined features on the part, and strongly affect the process capability in the final quality of the product. In this paper, an efficient systematic approach is given to investigate the machining errors to predict the manufacturing errors of the parts and capability prediction of corresponding machining processes. A mathematical formulation of fixture locators modeling is presented to establish the relationship between the part errors and the related sources. Based on this method, the final machining errors of the part can be accurately estimated by relating them to the combined dimensional and geometric tolerances of the workpiece – fixture system. This method is developed for uncertainty analysis based on the Worst Case and statistical approaches. The application of the presented method is illustrated through presenting an example and the computational results are compared with the Monte Carlo simulation results.

Keywords: process capability, machining error, dimensional and geometrical tolerances, uncertainty analysis

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Authors: Abdessalem Abbassi, Ahlem Dakhlaoui, Lota D. Tamini

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This article analyses management of hydropower dams within two different industrial structures: monopolistic and oligopolistic; when hydroelectricity producers are risk averse and face demand uncertainty. In each type of market structure we determine the water release path in closed-loop equilibrium. We show how a monopoly can manage its hydropower dams by additional pumping or storage depending on the relative abundance of water between different regions to smooth the effect of uncertainty on electricity prices. In the oligopolistic case with symmetric rates of risk aversion, we determine the conditions under which the relative scarcity (abundance) of water in the dam of a hydroelectric operator can favor additional strategic pumping (storage) in its competitor’s dams. When there is asymmetry of the risk aversion coefficient, the firm’s hydroelectricity production increases as its competitor’s risk aversion increases, if and only if the average recharge speed of the competitor’s dam exceeds a certain threshold, which is an increasing function of its average water inflows.

Keywords: asymmetric risk aversion, closed-loop Cournot competition, electricity wholesale market, hydropower dams

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Authors: Iulia E. Falcan

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The transition of the European power sector towards a clean, renewable energy (RE) system faces the challenge of meeting power demand in times of low wind speed and low solar radiation, at a reasonable cost. This is likely to be achieved through a combination of 1) energy storage technologies, 2) development of the cross-border power grid, 3) installed overcapacity of RE and 4) dispatchable power sources – such as biomass. This paper uses NASA; derived hourly data on weather patterns of sixteen European countries for the past twenty-five years, and load data from the European Network of Transmission System Operators-Electricity (ENTSO-E), to develop a stochastic optimization model. This model aims to understand the synergies between the four classes of technologies mentioned above and to determine the optimal configuration of the energy technologies portfolio. While this issue has been addressed before, it was done so using deterministic models that extrapolated historic data on weather patterns and power demand, as well as ignoring the risk of an unbalanced grid-risk stemming from both the supply and the demand side. This paper aims to explicitly account for the inherent uncertainty in the energy system transition. It articulates two levels of uncertainty: a) the inherent uncertainty in future weather patterns and b) the uncertainty of fully meeting power demand. The first level of uncertainty is addressed by developing probability distributions for future weather data and thus expected power output from RE technologies, rather than known future power output. The latter level of uncertainty is operationalized by introducing a Conditional Value at Risk (CVaR) constraint in the portfolio optimization problem. By setting the risk threshold at different levels – 1%, 5% and 10%, important insights are revealed regarding the synergies of the different energy technologies, i.e., the circumstances under which they behave as either complements or substitutes to each other. The paper concludes that allowing for uncertainty in expected power output - rather than extrapolating historic data - paints a more realistic picture and reveals important departures from results of deterministic models. In addition, explicitly acknowledging the risk of an unbalanced grid - and assigning it different thresholds - reveals non-linearity in the cost functions of different technology portfolio configurations. This finding has significant implications for the design of the European energy mix.

Keywords: cross-border grid extension, energy storage technologies, energy system transition, stochastic portfolio optimization

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Authors: I. Inthanongsone, C. Drebenstedt, J. C. Bongaerts, P. Sontamino

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The major concern in evaluating the value of mining projects related to the deficiency of the traditional discounted cash flow (DCF) method. This method does not take uncertainties into account and, hence it does not allow for an economic assessment of managerial flexibility and operational adaptability, which are increasingly determining long-term corporate success. Such an assessment can be performed with the real options valuation (ROV) approach, since it allows for a comparative evaluation of unforeseen uncertainties in a project life cycle. This paper presents an economic evaluation model for open pit mining projects based on real options valuation approach. Uncertainties in the model are caused by metal prices and cost uncertainties and the system dynamics (SD) modeling method is used to structure and solve the real options model. The model is applied to a case study. It can be shown that that managerial flexibility reacting to uncertainties may create additional value to a mining project in comparison to the outcomes of a DCF method. One important insight for management dealing with uncertainty is seen in choosing the optimal time to exercise strategic options.

Keywords: DCF methods, ROV approach, system dynamics modeling methods, uncertainty

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Authors: Myungjin Lee, Daegun Han, Jongsung Kim, Soojun Kim, Hung Soo Kim

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Recently, the localized heavy rainfall and typhoons are frequently occurred due to the climate change and the damage is becoming bigger. Therefore, we may need a more accurate prediction of the rainfall and runoff. However, the gauge rainfall has the limited accuracy in space. Radar rainfall is better than gauge rainfall for the explanation of the spatial variability of rainfall but it is mostly underestimated with the uncertainty involved. Therefore, the ensemble of radar rainfall was simulated using error structure to overcome the uncertainty and gauge rainfall. The simulated ensemble was used as the input data of the rainfall-runoff models for obtaining the ensemble of runoff hydrographs. The previous studies discussed about the accuracy of the rainfall-runoff model. Even if the same input data such as rainfall is used for the runoff analysis using the models in the same basin, the models can have different results because of the uncertainty involved in the models. Therefore, we used two models of the SSARR model which is the lumped model, and the Vflo model which is a distributed model and tried to simulate the optimum runoff considering the uncertainty of each rainfall-runoff model. The study basin is located in Han river basin and we obtained one integrated runoff hydrograph which is an optimum runoff hydrograph using the blending methods such as Multi-Model Super Ensemble (MMSE), Simple Model Average (SMA), Mean Square Error (MSE). From this study, we could confirm the accuracy of rainfall and rainfall-runoff model using ensemble scenario and various rainfall-runoff model and we can use this result to study flood control measure due to climate change. Acknowledgements: This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 18AWMP-B083066-05).

Keywords: radar rainfall ensemble, rainfall-runoff models, blending method, optimum runoff hydrograph

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Authors: Hamideh S. Shahidi

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This research examines destination image and its impact on tourist revisit intention. Destination images can evolve over time, depending on a number of factors. Due to the multidimensional nature of destination image, the full extent of what might influence that change is not yet fully understood. As a result, the destination image should be measured with a heavy consideration of the variables used. Depending on the time and circumstances, these variables should be adjusted based on the research’s objectives. The aim of this research is to evaluate the image of destinations that may be perceived as risky, such as Iran, from the perspective of European cultural travellers. Further to the goal of understanding the effects of an image on tourists’ decision-making, the research will assess the impact of destination image on the revisit intention using push and pull factors and perceived risks with the potential moderating effect of cultural contact (the direct interaction between the host and the tourists with different culture). In addition, the moderating effect of uncertainty avoidance on revisit intention after Iran’s crisis in 2022 will be measured. Furthermore, the level of uncertainty avoidance between gender and age will be compared.

Keywords: destination image, Iran’s 2022 crisis, revisit intention, uncertainty avoidance

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Authors: Rafael Alvarenga, Hubert Herbaux, Laurent Linguet

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The uncertainty concerning the power production of intermittent renewable energy is one of the main barriers to the integration of such assets into the power grid. Efforts have thus been made to develop methods to quantify this uncertainty, allowing producers to ensure more reliable and profitable engagements related to their future power delivery. Even though a diversity of probabilistic approaches was proposed in the literature giving promising results, the added value of adopting such methods for scheduling intermittent power plants is still unclear. In this study, the profits obtained by a decision-making model used to optimally schedule an existing PV power plant connected to batteries are compared when the model is fed with deterministic and probabilistic forecasts generated with two of the most recent methods proposed in the literature. Moreover, deterministic forecasts with different accuracy levels were used in the experiments, testing the utility and the capability of probabilistic methods of modeling the progressively increasing uncertainty. Even though probabilistic approaches are unquestionably developed in the recent literature, the results obtained through a study case show that deterministic forecasts still provide the best performance if accurate, ensuring a gain of 14% on final profits compared to the average performance of probabilistic models conditioned to the same forecasts. When the accuracy of deterministic forecasts progressively decreases, probabilistic approaches start to become competitive options until they completely outperform deterministic forecasts when these are very inaccurate, generating 73% more profits in the case considered compared to the deterministic approach.

Keywords: PV power forecasting, uncertainty quantification, optimal scheduling, power systems

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Authors: Anita Lazurko, Laszlo Pinter, Jeremy Richardson

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Uncertain climate projections, multiple possible development futures, and a financing gap create challenges for water infrastructure decision making. In contrast to conventional predict-plan-act methods, an emerging decision paradigm that enables social-ecological resilience supports decisions that are appropriate for uncertainty and leverage social, ecological, and economic multifunctionality. Concurrently, water infrastructure project finance plays a powerful role in sustainable infrastructure development but remains disconnected from discourse in socio-ecological resilience. At the time of research, a project to transfer water from Lesotho to Botswana through South Africa in the Orange-Senqu River Basin was at the pre-feasibility stage. This case was analysed through documents and interviews to investigate how uncertainty and multifunctionality are conceptualised and considered in decisions for the resilience of water infrastructure and to explore bridging concepts that might allow project finance to better enable socio-ecological resilience. Interviewees conceptualised uncertainty as risk, ambiguity and ignorance, and multifunctionality as politically-motivated shared benefits. Numerous efforts to adopt emerging decision methods that consider these terms were in use but required compromises to accommodate the persistent, conventional decision paradigm, though a range of future opportunities was identified. Bridging these findings to finance revealed opportunities to consider a more comprehensive scope of risk, to leverage risk mitigation measures, to diffuse risks and benefits over space, time and to diverse actor groups, and to clarify roles to achieve multiple objectives for resilience. In addition to insights into how multiple decision paradigms interact in real-world decision contexts, the research highlights untapped potential at the juncture between socio-ecological resilience and project finance.

Keywords: socio-ecological resilience, finance, multifunctionality, uncertainty

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Authors: Muhammad A. Alsubaie, Mubarak K. H. Alhajri, Tarek S. Altowaim

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A repetitive controller designed to accommodate periodic disturbances via state feedback is discussed. Periodic disturbances can be represented by a time delay model in a positive feedback loop acting on system output. A direct use of the small gain theorem solves the periodic disturbances problem via 1) isolating the delay model, 2) finding the overall system representation around the delay model and 3) designing a feedback controller that assures overall system stability and tracking error convergence. This paper addresses uncertainty conditions for the repetitive controller designed in state feedback in either past error feedforward or current error feedback using singular values. The uncertainty investigation is based on the overall system found and the stability condition associated with it; depending on the scheme used, to set an upper/lower limit weighting parameter. This creates a region that should not be exceeded in selecting the weighting parameter which in turns assures performance improvement against system uncertainty. Repetitive control problem can be described in lifted form. This allows the usage of singular values principle in setting the range for the weighting parameter selection. The Simulation results obtained show a tracking error convergence against dynamic system perturbation if the weighting parameter chosen is within the range obtained. Simulation results also show the advantage of weighting parameter usage compared to the case where it is omitted.

Keywords: model mismatch, repetitive control, singular values, state feedback

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Authors: Hossein Afzali

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Increasingly, decision analytic models are used to inform decisions about whether or not to publicly fund new health technologies. It is well noted that the accuracy of model predictions is strongly influenced by the appropriateness of model structuring. However, there is relatively inadequate methodological guidance surrounding this issue in guidelines developed by national funding bodies such as the Australian Pharmaceutical Benefits Advisory Committee (PBAC) and The National Institute for Health and Care Excellence (NICE) in the UK. This presentation aims to discuss issues around model structuring within decision making with a focus on (1) the need for a transparent and evidence-based model structuring process to inform the most appropriate set of structural aspects as the base case analysis; (2) the need to characterise structural uncertainty (If there exist alternative plausible structural assumptions (or judgements), there is a need to appropriately characterise the related structural uncertainty). The presentation will provide an opportunity to share ideas and experiences on how the guidelines developed by national funding bodies address the above issues and identify areas for further improvements. First, a review and analysis of the literature and guidelines developed by PBAC and NICE will be provided. Then, it will be discussed how the issues around model structuring (including structural uncertainty) are not handled and justified in a systematic way within the decision-making process, its potential impact on the quality of public funding decisions, and how it should be presented in submissions to national funding bodies. This presentation represents a contribution to the good modelling practice within the decision-making process. Although the presentation focuses on the PBAC and NICE guidelines, the discussion can be applied more widely to many other national funding bodies that use economic evaluation to inform funding decisions but do not transparently address model structuring issues e.g. the Medical Services Advisory Committee (MSAC) in Australia or the Canadian Agency for Drugs and Technologies in Health.

Keywords: decision-making process, economic evaluation, good modelling practice, structural uncertainty

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Authors: Anik Sarminingsih, Krishna V. Pradana

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The failure of flood control system can be caused by various factors, such as not considering the uncertainty of designed flood causing the capacity of the flood control system is exceeded. The presence of the uncertainty factor is recognized as a serious issue in hydrological studies. Uncertainty in hydrological analysis is influenced by many factors, starting from reading water elevation data, rainfall data, selection of method of analysis, etc. In hydrological modeling selection of models and parameters corresponding to the watershed conditions should be evaluated by the hydraulic model in the river as a drainage channel. River cross-section capacity is the first defense in knowing the reliability of the flood control system. Reliability of river capacity describes the potential magnitude of flood risk. Case study in this research is Wulan River in Central Java. This river occurring flood almost every year despite some efforts to control floods such as levee, floodway and diversion. The flood-affected areas include several sub-districts, mainly in Kabupaten Kudus and Kabupaten Demak. First step is analyze the frequency of discharge observation from Klambu weir which have time series data from 1951-2013. Frequency analysis is performed using several distribution frequency models such as Gumbel distribution, Normal, Normal Log, Pearson Type III and Log Pearson. The result of the model based on standard deviation overlaps, so the maximum flood discharge from the lower return periods may be worth more than the average discharge for larger return periods. The next step is to perform a hydraulic analysis to evaluate the reliability of river capacity based on the flood discharge resulted from several methods. The selection of the design flood discharge of flood control system is the result of the method closest to bankfull capacity of the river.

Keywords: design flood, hydrological model, reliability, uncertainty, Wulan river

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Authors: A. Dehghani, S. Shirin

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We propose in this article a new configuration of quantum states, |α, β> := |α>×|β>. Which are composed of vector products of two different copies of spin coherent states, |α> and |β>. Some mathematical as well as physical properties of such states are discussed. For instance, it has been shown that the cross products of two coherent vectors remain coherent again. They admit a resolution of the identity through positive definite measures on the complex plane. They represent packets similar to the true coherent states, in other words we would not expect to take spin squeezing in any of the field quadratures Lˆx, Lˆy and Lˆz. Depending on the particular choice of parameters in the above scenarios, they can be converted into the so-called Dicke states which minimize the uncertainty relations of each pair of the angular momentum components.

Keywords: vector (Cross-)products, minimum uncertainty, angular momentum, measurement, Dicke states

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Authors: Li-Wei Wu, Chung-Yu Wang

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Recently, interest has been generated in the search for a new reference framework for value creation that is centered on the co-creation process. Co-creation implies cooperative value creation between service firms and customers and requires the building of experiences as well as the resolution of problems through the combined effort of the parties in the relationship. For customers, values are always co-created through their participation in services. Customers can ultimately determine the value of the service in use. This new approach emphasizes that a customer’s participation in the service process is considered indispensable to value co-creation. An important feature of service in the context of exchange is co-production, which implies that a certain amount of participation is needed from customers to co-produce a service and hence co-create value. Co-production no doubt helps customers better understand and take charge of their own roles in the service process. Thus, this proposal is to encourage co-production, thus facilitating value co-creation of that is reflected in both customers and service firms. Four determinants of co-production are identified in this study, namely, commitment, trust, asset specificity, and decision-making uncertainty. Commitment is an essential dimension that directly results in successful cooperative behaviors. Trust helps establish a relational environment that is fundamental to cross-border cooperation. Asset specificity motivates co-production because this determinant may enhance return on asset investment. Decision-making uncertainty prompts customers to collaborate with service firms in making decisions. In other words, customers adjust their roles and are increasingly engaged in co-production when commitment, trust, asset specificity, and decision-making uncertainty are enhanced. Although studies have examined the preceding effects, to our best knowledge, none has empirically examined the simultaneous effects of all the curvilinear relationships in a single study. When these determinants are excessive, however, customers will not engage in co-production process. In brief, we suggest that the relationships of commitment, trust, asset specificity, and decision-making uncertainty with co-production are curvilinear or are inverse U-shaped. These new forms of curvilinear relationships have not been identified in existing literature on co-production; therefore, they complement extant linear approaches. Most importantly, we aim to consider both the bright and the dark sides of the determinants of co-production.

Keywords: co-production, commitment, trust, asset specificity, decision-making uncertainty

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Authors: Shawn Thompson

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Socrates and Zen Buddhism are separated by time, distance, and cultures in a way that it is unlikely that they influenced each other. And yet the two have an amazing similarity in the principle that paradoxical and unanswerable questions can be a form of wisdom that produces a healthy psyche. Both have a sense that the limit of human awareness is a wisdom of this uncertainty. Both are at odds with the dogma of answers and of a western rationality that prioritizes the answer. Both have enigmatic answers that perpetuate the question. Both use the form of a dialogue of interaction with mutual illumination rather than the form of a lecture to passive recipients. If these premises are true, Socrates and Zen Buddhism has elements in common that reflect basic human needs for a good life. It can be argued that there is a joint archetypal experience of the wisdom of uncertainty and unanswerable questions in Socrates and Zen Buddhism.

Keywords: zen buddhism, socrates, unanswerable questions, aporia

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Authors: André Jesus, Yanjie Zhu, Irwanda Laory

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Structural health monitoring is one of the most promising technologies concerning aversion of structural risk and economic savings. Analysts often have to deal with a considerable variety of uncertainties that arise during a monitoring process. Namely the widespread application of numerical models (model-based) is accompanied by a widespread concern about quantifying the uncertainties prevailing in their use. Some of these uncertainties are related with the deterministic nature of the model (code uncertainty) others with the variability of its inputs (parameter uncertainty) and the discrepancy between a model/experiment (systematic uncertainty). The actual process always exhibits a random behaviour (observation error) even when conditions are set identically (residual variation). Bayesian inference assumes that parameters of a model are random variables with an associated PDF, which can be inferred from experimental data. However in many Bayesian methods the determination of systematic uncertainty can be problematic. In this work systematic uncertainty is associated with a discrepancy function. The numerical model and discrepancy function are approximated by Gaussian processes (surrogate model). Finally, to avoid the computational burden of a fully Bayesian approach the parameters that characterise the Gaussian processes were estimated in a four stage process (modular Bayesian approach). The proposed methodology has been successfully applied on fields such as geoscience, biomedics, particle physics but never on the SHM context. This approach considerably reduces the computational burden; although the extent of the considered uncertainties is lower (second order effects are neglected). To successfully identify the considered uncertainties this formulation was extended to consider multiple responses. The efficiency of the algorithm has been tested on a small scale aluminium bridge structure, subjected to a thermal expansion due to infrared heaters. Comparison of its performance with responses measured at different points of the structure and associated degrees of identifiability is also carried out. A numerical FEM model of the structure was developed and the stiffness from its supports is considered as a parameter to calibrate. Results show that the modular Bayesian approach performed best when responses of the same type had the lowest spatial correlation. Based on previous literature, using different types of responses (strain, acceleration, and displacement) should also improve the identifiability problem. Uncertainties due to parametric variability, observation error, residual variability, code variability and systematic uncertainty were all recovered. For this example the algorithm performance was stable and considerably quicker than Bayesian methods that account for the full extent of uncertainties. Future research with real-life examples is required to fully access the advantages and limitations of the proposed methodology.

Keywords: bayesian, calibration, numerical model, system identification, systematic uncertainty, Gaussian process

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Authors: Yingxiao Ye, Christopher Doehring, Angelos Georghiou, Hugh Robinson, Phebe Vayanos

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Human development is a threat to biodiversity, and conservation organizations (COs) are purchasing land to protect areas for biodiversity preservation. However, COs have limited budgets and thus face hard prioritization decisions that are confounded by uncertainty in future human land use. This research proposes a data-driven sequential planning model to help COs choose land parcels that minimize the uncertain human impact on biodiversity. The proposed model is robust to uncertain development, and the sequential decision-making process is adaptive, allowing land purchase decisions to adapt to human land use as it unfolds. The cellular automata model is leveraged to simulate land use development based on climate data, land characteristics, and development threat index from NASA Socioeconomic Data and Applications Center. This simulation is used to model uncertainty in the problem. This research leverages state-of-the-art techniques in the robust optimization literature to propose a computationally tractable reformulation of the model, which can be solved routinely by off-the-shelf solvers like Gurobi or CPLEX. Numerical results based on real data from the Jaguar in Central and South America show that the proposed method reduces conservation loss by 19.46% on average compared to standard approaches such as MARXAN used in practice for biodiversity conservation. Our method may better help guide the decision process in land acquisition and thereby allow conservation organizations to maximize the impact of limited resources.

Keywords: data-driven robust optimization, biodiversity conservation, uncertainty simulation, adaptive sequential planning

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Authors: Moridi Fatemeh, Dasineh Mehdi, Jafari Narges

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The aim of this study was to investigate the relationship between dividends and investment in a condition of cash flow uncertainty. Previous studies have also found some evidence that there is N-shaped relationship between dividends and investment given different levels of cash uncertainly. Thus, this study examines this relationship over the period 2009-2014 in Tehran Stock Exchange (TSE). Based on our sample and new variables, we found reverse N-shaped relationship in different levels of cash flow uncertainly. This shape was descending in cash flow certainly and uncertainly but it is ascending in medial position.

Keywords: dividends, investment, nonlinear relationship, uncertainty of cash flow

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Authors: Julius M. Ndambuki, Gislar E. Kifanyi, Samuel N. Odai, Charles Gyamfi

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Conjunctive management of surface and groundwater resources is a challenging task due to the spatial and temporal variability nature of hydrology as well as hydrogeology of the water storage systems. Surface water-groundwater hydrogeology is highly uncertain; thus it is imperative that this uncertainty is explicitly accounted for, when managing water resources. Various methodologies have been developed and applied by researchers in an attempt to account for the uncertainty. For example, simulation-optimization models are often used for conjunctive water resources management. However, direct application of such an approach in which all realizations are considered at each iteration of the optimization process leads to a very expensive optimization in terms of computational time, particularly when the number of realizations is large. The aim of this paper, therefore, is to introduce and apply an efficient approach referred to as Retrospective Optimization Approximation (ROA) that can be used for optimizing conjunctive use of surface water and groundwater over a multiple hydrogeological model simulations. This work is based on stochastic simulation-optimization framework using a recently emerged technique of sample average approximation (SAA) which is a sampling based method implemented within the Retrospective Optimization Approximation (ROA) approach. The ROA approach solves and evaluates a sequence of generated optimization sub-problems in an increasing number of realizations (sample size). Response matrix technique was used for linking simulation model with optimization procedure. The k-means clustering sampling technique was used to map the realizations. The methodology is demonstrated through the application to a hypothetical example. In the example, the optimization sub-problems generated were solved and analysed using “Active-Set” core optimizer implemented under MATLAB 2014a environment. Through k-means clustering sampling technique, the ROA – Active Set procedure was able to arrive at a (nearly) converged maximum expected total optimal conjunctive water use withdrawal rate within a relatively few number of iterations (6 to 7 iterations). Results indicate that the ROA approach is a promising technique for optimizing conjunctive water use of surface water and groundwater withdrawal rates under hydrogeological uncertainty.

Keywords: conjunctive water management, retrospective optimization approximation approach, sample average approximation, uncertainty

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Authors: I. Khutsishvili, G. Sirbiladze, B. Ghvaberidze

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This work proposes a fuzzy methodology to support the investment decisions. While choosing among competitive investment projects, the methodology makes ranking of projects using the new aggregation OWA operator – AsPOWA, presented in the environment of possibility uncertainty. For numerical evaluation of the weighting vector associated with the AsPOWA operator the mathematical programming problem is constructed. On the basis of the AsPOWA operator the projects’ group ranking maximum criteria is constructed. The methodology also allows making the most profitable investments into several of the project using the method developed by the authors for discrete possibilistic bicriteria problems. The article provides an example of the investment decision-making that explains the work of the proposed methodology.

Keywords: expert evaluations, investment decision making, OWA operator, possibility uncertainty

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Authors: Xiaohua Zou, Yongxin Su

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The reasonable operation of heating, ventilation, and air conditioning (HVAC) is of great significance in improving the security, stability, and economy of power system operation. However, the uncertainty of the operating environment, thermal comfort varies by users and rapid decision-making pose challenges for HVAC demand response optimization. In this regard, this paper proposes a reinforcement learning-based method for HVAC demand response optimization considering few-shot personalized thermal comfort (PTC). First, an HVAC DR optimization framework based on few-shot PTC model and DRL is designed, in which the output of few-shot PTC model is regarded as the input of DRL. Then, a few-shot PTC model that distinguishes between awake and asleep states is established, which has excellent engineering usability. Next, based on soft actor criticism, an HVAC DR optimization algorithm considering the user’s PTC is designed to deal with uncertainty and make decisions rapidly. Experiment results show that the proposed method can efficiently obtain use’s PTC temperature, reduce energy cost while ensuring user’s PTC, and achieve rapid decision-making under uncertainty.

Keywords: HVAC, few-shot personalized thermal comfort, deep reinforcement learning, demand response

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Authors: D. S. Gomes, A. T. Silva

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Analysis of the uncertainty quantification related to nuclear safety margins applied to the nuclear reactor is an important concept to prevent future radioactive accidents. The nuclear fuel performance code may involve the tolerance level determined by traditional deterministic models producing acceptable results at burn cycles under 62 GWd/MTU. The behavior of nuclear fuel can simulate applying a series of material properties under irradiation and physics models to calculate the safety limits. In this study, theoretical predictions of nuclear fuel failure under transient conditions investigate extended radiation cycles at 75 GWd/MTU, considering the behavior of fuel rods in light-water reactors under reactivity accident conditions. The fuel pellet can melt due to the quick increase of reactivity during a transient. Large power excursions in the reactor are the subject of interest bringing to a treatment that is known as the Fuchs-Hansen model. The point kinetic neutron equations show similar characteristics of non-linear differential equations. In this investigation, the multivariate logistic regression is employed to a probabilistic forecast of fuel failure. A comparison of computational simulation and experimental results was acceptable. The experiments carried out use the pre-irradiated fuels rods subjected to a rapid energy pulse which exhibits the same behavior during a nuclear accident. The propagation of uncertainty utilizes the Wilk's formulation. The variables chosen as essential to failure prediction were the fuel burnup, the applied peak power, the pulse width, the oxidation layer thickness, and the cladding type.

Keywords: logistic regression, reactivity-initiated accident, safety margins, uncertainty propagation

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Authors: Aram Yakoby, Yossef H. Hatzor, Shmulik Pinkert

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This work presents an applied engineering method for evaluating the stability of underground openings under conditions of uncertainty. The developed method is demonstrated by a comprehensive parametric study on a case of large-diameter vertical borehole stability analysis, with uncertainties regarding the in-situ stress distribution. To this aim, a safety factor analysis is performed for the stability of both supported and unsupported boreholes. In the analysis, we used analytic geomechanical calculations and advanced numerical modeling to evaluate the estimated stress field. In addition, the work presents the development of a boundary condition for the numerical model that fits the nature of the problem and yields excellent accuracy. The borehole stability analysis is studied in terms of (1) the stress ratio in the vertical and horizontal directions, (2) the mechanical properties and geometry of the support system, and (3) the parametric sensitivity. The method's results are studied in light of a real case study of an underground waste disposal site. The conclusions of this study focus on the developed method for capturing the parametric uncertainty, the definition of critical geological depths, the criteria for implementing structural support, and the effectiveness of further in-situ investigations.

Keywords: borehole stability, in-situ stress, parametric study, factor of safety

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Authors: Eva Kotlánová

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After recession that began in 2007 in the United States and subsequently spilled over the Europe we could expect recovery of economic growth. According to the last estimation of economic progress of European countries, this recovery is not strong enough. Among others, it will depend on economic policy, where and in which way, the economic indicators will proceed. Economic theories postulate that the economic subjects prefer stably, continual economic policy without repeated and strong fluctuations. This policy is perceived as support of economic growth. Mostly in crises period, when the government must cope with consequences of recession, the economic policy becomes unpredictable for many subjects and economic policy uncertainty grows, which have negative influence on economic growth. The aim of this paper is to use panel regression to prove or disprove this hypothesis on the example of five largest European economies in the period 2008–2012.

Keywords: economic crises in Europe, economic policy, uncertainty, panel analysis regression

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Authors: B. Güney, Ç. Teke

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In recent years, rapid and correct decision making is crucial for both people and enterprises. However, uncertainty makes decision-making difficult. Fuzzy logic is used for coping with this situation. Thus, fuzzy linear programming models are developed in order to handle uncertainty in objective function and the constraints. In this study, a problem of a factory in food industry is investigated, required data is obtained and the problem is figured out as a fuzzy linear programming model. The model is solved using Zimmerman approach which is one of the approaches for fuzzy linear programming. As a result, the solution gives the amount of production for each product type in order to gain maximum profit.

Keywords: food industry, fuzzy linear programming, fuzzy logic, linear programming

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Authors: Okay Gunes

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In this article, for the first time in the literature for this subject we propose a new method for the measuring of well-being inequality through a model composed of superimposing satisfaction waves. The displacement of households’ satisfactory state (i.e. satisfaction) is defined in a satisfaction string. The duration of the satisfactory state for a given period of time is measured in order to determine the relationship between utility and total satisfactory time, itself dependent on the density and tension of each satisfaction string. Thus, individual cardinal total satisfaction values are computed by way of a one-dimensional form for scalar sinusoidal (harmonic) moving wave function, using satisfaction waves with varying amplitudes and frequencies which allow us to measure well-being inequality. One advantage to using satisfaction waves is the ability to show that individual utility and consumption amounts would probably not commute; hence it is impossible to measure or to know simultaneously the values of these observables from the dataset. Thus, we crystallize the problem by using a Heisenberg-type uncertainty resolution for self-adjoint economic operators. We propose to eliminate any estimation bias by correlating the standard deviations of selected economic operators; this is achieved by replacing the aforementioned observed uncertainties with households’ perceived uncertainties (i.e. corrected standard deviations) obtained through the logarithmic psychophysical law proposed by Weber and Fechner.

Keywords: Heisenberg uncertainty principle, superimposing satisfaction waves, Weber–Fechner law, well-being inequality

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Authors: Sukhveer Singh, Sandeep Singh

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A bi-objective fuzzy transportation problem with the objectives to minimize the total fuzzy cost and fuzzy time of transportation without according priorities to them is considered. To the best of our knowledge, there is no method in the literature to find efficient solutions of the bi-objective transportation problem under uncertainty. In this paper, a bi-objective transportation problem in an uncertain environment has been formulated. An algorithm has been proposed to find efficient solutions of the bi-objective transportation problem under uncertainty. The proposed algorithm avoids the degeneracy and gives the optimal solution faster than other existing algorithms for the given uncertain transportation problem.

Keywords: uncertain transportation problem, efficient solution, ranking function, fuzzy transportation problem

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Authors: Rami A. Maher, Ibraheem K. Ibraheem

Abstract:

This work presents a comparison study between the state-space and polynomial methods for the design of the robust governor for load frequency control of steam turbine power systems. The robust governor is synthesized using the two approaches and the comparison is extended to include time and frequency domains performance, controller order, and uncertainty representation, weighting filters, optimality and sub-optimality. The obtained results are represented through tables and curves with reasons of similarities and dissimilarities.

Keywords: robust control, load frequency control, steam turbine, H∞-norm, system uncertainty, load disturbance

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Authors: Bouharati Lokman, Bouharati Imen, Bouharati Khaoula, Bouharati Oussama, Bouharati Saddek

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Ionizing radiation exposure is an established cancer risk factor. Compared to other common environmental carcinogens, it is relatively easy to determine organ-specific radiation dose and, as a result, radiation dose-response relationships tend to be highly quantified. Nevertheless, there can be considerable uncertainty about questions of radiation-related cancer risk as they apply to risk protection and public policy, and the interpretations of interested parties can differ from one person to another. Examples of tools used in the analysis of the risk of developing cancer due to radiation are characterized by uncertainty. These uncertainties are related to the history of exposure and different assumptions involved in the calculation. We believe that the results of statistical calculations are characterized by uncertainty and imprecision. Having regard to the physiological variation from one person to another. In this study, we develop a tool based on fuzzy logic inference. As fuzzy logic deals with imprecise and uncertain, its application in this area is adequate. We propose a fuzzy system with three input variables (age, sex and body attainable cancer). The output variable expresses the risk of infringement rate of each organ. A base rule is established from recorded actual data. After successful simulation, this will instantly predict the risk of infringement rate of each body following chronic exposure to 0.1 Gy.

Keywords: radiation exposure, cancer, modeling, fuzzy logic

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Authors: Kamyar Tolouei, Ehsan Moosavi

Abstract:

In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework.

Keywords: grade uncertainty, metaheuristic algorithms, open-pit mine, production scheduling optimization

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Authors: Rui Xing, Xiaowen Zhao, Hao Huang, Chang Liu

Abstract:

Creativity is regarded as vital for new ventures' development since the whole process of entrepreneurship is rooted in the creation and exploration of new ideas. The entrepreneurial leader is central to the entrepreneurial team, who plays an especially important role in this process. However, few scholars have studied the impact entrepreneurs' leadership styles on the creativity of entrepreneurial teams. In this study, we integrate the historically disjointed literatures of leadership style and team creativity under entrepreneurship circumstance to understand why and when entrepreneurs' different leadership style relates to team creativity. Focus on answering the following questions: Is humility leadership necessarily better than narcissism leadership at increasing the creativity of entrepreneurial teams? Moreover, in which situations humility leadership or narcissism leadership is more conducive to the entrepreneurial team's creativity? Based on the componential theory of creativity and entrepreneurial cognition theory, we explore the relationship between entrepreneurs' leadership style and team creativity, treating team cognitive diversity and environmental uncertainty as moderators and psychological empowerment as mediators. We tested our hypotheses using data gathered from 64 teams and 256 individual members from 53 new firms in China's first-tier cities such as Beijing and Shanghai. We found that there was a significant positive relation between entrepreneurs' humble leadership and psychological empowerment, and the more significant the positive correlation was when the environmental uncertainty was high. In addition, there was a significant negative relation between entrepreneurs' narcissistic leadership and psychological empowerment, and the negative relation was weaker in teams with a high team cognitive diversity value. Furthermore, both entrepreneurs' humble leadership and team psychological empowerment were significantly positively related to team creativity. While entrepreneurs' narcissistic leadership was negatively related to team creativity, and the negative relationship was weaker in teams with a high team cognitive diversity or a high environmental uncertainty value. This study has some implications for both scholars and entrepreneurs. Firstly, our study enriches the understanding of the role of leadership in entrepreneurial team creativity. Different from previous team creativity literatures, focusing on TMT and R&D team, this study is a significant attempt to demonstrate that entrepreneurial leadership style is particularly relevant to the core requirements of team creativity. Secondly, this study introduces two moderating variables, cognitive diversity and environmental uncertainty, to explore the different boundary conditions under which the two leadership styles play their roles, which is helpful for entrepreneurs to understand how to leverage leadership to improve entrepreneurial team creativity, how to recruit cognitively diverse employees to moderate the effects of inappropriate leadership to the team. Finally, our findings showed that entrepreneurs' humble leadership makes a unique contribution to explaining team creativity through team psychological empowerment.

Keywords: entrepreneurs’ leadership style, entrepreneurial team creativity, team psychological empowerment, team cognitive diversity, environmental uncertainty

Procedia PDF Downloads 104