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نتیجه جستجو - Risk evaluation

تعداد مقالات یافته شده: 17
ردیف عنوان نوع
1 Evaluation on risks of sustainable supply chain based on optimized BP neural networks in fresh grape industry
ارزیابی خطرات زنجیره تأمین پایدار بر اساس شبکه های عصبی بهینه شده BP در صنعت انگور تازه-2021
In order to improve the risk evaluation and management in fresh grape supply chain and enhance the sustainable level of the supply chain, this study applied neural network to evaluate the risk of fresh grape supply chain from the perspective of sustainable development. Firstly, the possible risk factors in the supply chain were identified and the risk evaluation index system were proposed; then risk evaluation models based on single BP and optimized BP (GABP and PSO-BP) neural network were established; and then the models were trained, tested and evaluated using data set from supply chain survey. The survey and analysis results showed that the risk of fresh grape supply chain was at a low level but the risk in each link was discrepant, the biggest risk was the risks among the links in the chain (R0), and the high risk dimensions were the economic risk, social risk and cooperation risk; most risky events were located in the second quadrant (small probability & high damage risk events). The results of models training and testing indicated that the optimized model was superior to single BP neural network for risk assessment in grape sustainable supply chain, and the PSO-BP model was more accurate and suitable with less evaluation errors and a bigger R2. The results also extracted the risk factors that contributed most to the overall risk of grape sustainable supply chain. This paper enriches the method of supply chain risk assessment theoretically, and provides practical suggestions for risk prevention, stable operation and sustainability improvement of fresh grape supply chain.
Keywords: Sustainable supply chain | Supply chain risk | GA-BP neural network | PSO-BP neural network | Risk evaluation
مقاله انگلیسی
2 Ontology knowledge base combined with Bayesian networks for integrated corridor risk warning
پایگاه دانش هستی شناسی همراه با شبکه های بیزی برای هشدار خطر یکپارچه راهرو-2021
With the accelerated urbanization process, the emergence of urban underground integrated pipeline corridors is the trend for cities, especially large and medium-sized cities. However, due to the complexity of the internal system of the integrated corridor, there are various risks in the process of its construction and operation and maintenance, and the risk factors are complex and diverse. In this paper, we introduce ontology technology and knowledge base construction into the risk management of integrated pipeline corridor, build an ontology-based knowledge base of integrated pipeline corridor risk, and construct a Bayesian network based on the established risk knowledge base for risk evaluation of identified risk factors. The combination of ontology knowledge base construction and Bayesian network method of integrated pipeline corridor risk makes the risk identification system completer and more effective, and the method can effectively evaluate the disaster risk level of integrated pipeline corridor operation and maintenance, which can meet the practical needs of integrated pipeline corridor operation and maintenance risk management and disaster prevention and mitigation work.
Keywords: Integrated corridor | Risk warning | Ontology knowledge | Bayesian networks
مقاله انگلیسی
3 Imbalanced credit risk evaluation based on multiple sampling, multiple kernel fuzzy self-organizing map and local accuracy ensemble
ارزیابی ریسک اعتباری نامتوازن بر اساس نمونه گیری چندگانه ، نقشه خود سازماندهی فازی چند هسته ای و گروه دقت محلی-2020
Credit risk evaluation model is generally regarded as a valid method for business risk management. Although the most of literatures about credit risk evaluation always use class-balanced data as sample sets, the study on class-imbalanced datasets is more suitable for actual situation. This paper proposes a new ensemble model to evaluate class-imbalanced credit risk, which integrates multiple sampling, multiple kernel fuzzy self-organizing map and local accuracy ensemble. To preprocess imbalanced sample sets of credit risk evaluation, multiple sampling approaches (synthetic minority over-sampling technique, under sampling and hybrid sampling) are improved and integrated to acquire balanced datasets. To construct more suitable base classifiers, multiple kernel functions (Gaussian, Polynomial and Sigmoid) respectively are used to improve fuzzy self-organizing map. Then, the balanced sample sets are respectively processed by the improved base classifiers to acquire different prediction results. The local accuracy ensemble method is employed to dynamically synthesize these prediction results to obtain final result. The new ensemble model can further avoid over-fitting and information loss, be more suitable to handle the dataset including different financial indicators, and acquire the stable and satisfactory prediction result for imbalanced credit risk evaluation In the empirical research, this paper adopts the financial data from Chinese listed companies, and makes the comparative analysis with the relative models step by step. The results can prove that the new ensemble model presented by this article has better performance than other methods in terms of evaluating the imbalanced credit risk.© 2020 Elsevier B.V. All rights reserved.
Keywords: Credit risk evaluation | Class-imbalanced data | Multiple sampling | Multiple kernel fuzzy self-organizing map | Local accuracy ensemble
مقاله انگلیسی
4 Risk assessment of agricultural supermarket supply chain in big data environment
ارزیابی ریسک زنجیره تأمین سوپرمارکت های کشاورزی در محیط داده های کلان-2020
Article history:Received 20 November 2019Received in revised form 30 June 2020 Accepted 14 July 2020Available online 16 July 2020Keywords:Big dataAgricultural super-docking Supply chainRisk analysisWith the application of big data in all walks of life, big data thinking is effectively improving the cir- culation efficiency of agricultural products supply chain by driving management changes in business decision-making and f¨armer-supermarket dockinga¨ s an innovative mode of agricultural products circu- lation. Based on the current situation of China’s agricultural supermarket supply chain development, this paper makes an in-depth study on the supply chain risk of agricultural products of large retail enterprises under the mode of a¨ gricultural supermarket docking¨, and then introduces the agricultural supermarket docking supply chain under the big data environment. This paper uses big data to analyze the risks that may arise in the supply chain of a¨ gricultural supermarket docking¨in large retail enterprises. This paper from the aspects of production, processing, distribution, retail and consumption, introduces the new risks of agricultural supermarket supply chain after introducing big data. Secondly, Qualitative analysis and quantitative calculation are combined to conduct risk assessment. Through empirical analysis, the ranking of all risk factors is obtained, and the relevant fuzzy evaluation grade and risk evaluation criteria are given. Through expert evaluation, a new risk ranking is also obtained, which is not much different from the results of empirical analysis, and the empirical results are also verified. Therefore, develop this study is helpful to prevent the risk of agricultural supermarket supply chain connection. At the same time, the information integration, sharing and feedback of the big database provide a new idea for the optimization of the supply chain connecting agricultural production.it also has reference significance for other supply chain risk management.© 2020 Elsevier Inc. All rights reserved.
Keywords: Big data | Agricultural super-docking | Supply chain | Risk analysis
مقاله انگلیسی
5 The Importance of the Social License to Operate at the Investment and Operations Stage of Coal Mining Projects: Application using a Decision Support System
اهمیت پروانه اجتماعی برای فعالیت در مرحله سرمایه گذاری و عملیات پروژه های استخراج زغال سنگ: درخواست با استفاده از سیستم پشتیبانی تصمیم-2020
The Social License to Operate (SLO) and the Value Chain business model are basic elements that need to be considered both at the planning and operation stages of mining operations and in particular in coal mining projects. If a coal mining enterprise loses its SLO, it may face risks in operations, which may lead to value chain risks. One of the causes of enterprise failure as related to coal mining operations is the inability to reliably assess/ manage risk holistically and the inability to understand that lack of SLO is a critical risk. Although financial risks are typically assessed for mining projects, lack of SLO risk should also be taken into account starting as early as the bankable feasibility study. Furthermore, as it is difficult to establish a proactive decision-making policy for SLO risk in coal mining operations, the Operational Risk Management (ORM) methodology is probably a good tool to apply towards that goal. For this reason, a Mining Operational Risk Management Model (MORMM) was developed to incorporate risk probabilities and risk severities evaluated by experts. The final risk assessment is coded using Risk Assessment Codes (RACs). A hypothetical scenario was developed utilizing the MORMM model in order to illustrate how risks can be managed during the SLO granting process. This scenario describes a hypothetical coal mining project evaluated by virtual risk evaluators under specific hazard categories. Risk evaluation involves the assessment of risk probability and risk severity. Through this scenario this paper presents ways: (i) to establish a baseline ORM process that will be applicable to any coal mining operation environment, and (ii) to provide a theoretical example to demonstrate how the method can be applied to coal mining operations. The resulting RACs can provide critical information to decision makers regarding the rejection, acceptance or re-engineering of the mining business plan.
Keywords: Social License to Operate | Operational risk management | coal mining | Value Chain
مقاله انگلیسی
6 Multi-criteria decision-making considering risk and uncertainty in physical asset management
تصمیم گیری چند معیار با توجه به ریسک و عدم اطمینان در مدیریت دارایی های فیزیکی-2020
In this work we present a method for risk-informed decision-making in the physical asset management context whereby risk evaluation and cost-benefit analysis are considered in a common framework. The methodology uses quantitative risk measures to prioritize projects based on a combination of risk tolerance criteria, cost-benefit analysis and uncertainty reduction metrics. There is a need in the risk and asset management literature for a unified framework through which quantitative risk can be evaluated against tolerability criteria and trade-off decisions can be made between risk treatment options. The methodology uses quantitative risk measures for loss of life, loss of production and loss of property. A risk matrix is used to classify risk as intolerable, As Low As Reasonably Practicable (ALARP) or broadly tolerable. Risks in the intolerable and ALARP region require risk treatment, and risk treatment options are generated. Risk reduction benefit of the treatment options is quantified, and cost-benefit analysis is performed using discounted cashflow analysis. The Analytic Hierarchy Process is used to derive weights for prioritization criteria based on decision-maker preferences. The weights, along with prioritization criteria for risk reduction, tolerance criteria and project cost, are used to prioritize projects using the Technique for Order Preference by Similarity to Ideal Solution. The usefulness of the methodology for improved decision-making is illustrated using a numerical example.
Keywords: Risk analysis | Uncertainty | Asset management | Multi-criteria decision analysis | Risk matrix | Cost-benefit analysis
مقاله انگلیسی
7 Risk evaluation and retail electricity pricing using downside risk constraints method
ارزیابی ریسک و قیمت گذاری برق خرده فروشی با استفاده از روش محدودیت های خطر نزولی-2020
Electricity in the retail market has a different value for different types of consumers. Therefore, different retail prices are usually determined for various consumers in the retail market. However, imposed risks from uncertain parameters are a big challenge in the real-time retail market pricing process. This paper proposed a real-time pricing (RTP) framework for various users including residential, commercial, and industrial consumers by the electricity retailer. In addition, uncertainties of various input parameters such as output power of renewable energy resources, electricity demand, and pool market price are modeled using scenario-based stochastic approach while downside risk constraints method is proposed to model risk associated with uncertainties. By implementing this method, electricity retailer will be able to select various risk-based strategies. Furthermore, numerical results illustrate the various risks versus various profits by the occurring of each scenario which helps the retailer for decisions-making in different scenarios. According to obtained results, retailer by choosing of zero risk strategy can reduce its risk by 100% while expected profit is reduced by 2.07%. In addition, offered RTP by the retailer is higher for industrial, commercial, and residential customers, respectively. Finally, risk-averse and risk-neutral strategies of electricity retailer are determined in the power procurement problem.© 2019 Elsevier Ltd. All rights reserved.
Keywords: Electricity retailer | Energy pricing policy | Social welfare | energy business and management | Downside risk constraints
مقاله انگلیسی
8 Risk evaluation and retail electricity pricing using downside risk constraints method
ارزیابی ریسک و قیمت گذاری برق خرده فروشی با استفاده از روش محدودیت خطر-2020
Electricity in the retail market has a different value for different types of consumers. Therefore, different retail prices are usually determined for various consumers in the retail market. However, imposed risks from uncertain parameters are a big challenge in the real-time retail market pricing process. This paper proposed a real-time pricing (RTP) framework for various users including residential, commercial, and industrial consumers by the electricity retailer. In addition, uncertainties of various input parameters such as output power of renewable energy resources, electricity demand, and pool market price are modeled using scenario-based stochastic approach while downside risk constraints method is proposed to model risk associated with uncertainties. By implementing this method, electricity retailer will be able to select various risk-based strategies. Furthermore, numerical results illustrate the various risks versus various profits by the occurring of each scenario which helps the retailer for decisions-making in different scenarios. According to obtained results, retailer by choosing of zero risk strategy can reduce its risk by 100% while expected profit is reduced by 2.07%. In addition, offered RTP by the retailer is higher for industrial, commercial, and residential customers, respectively. Finally, risk-averse and risk-neutral strategies of electricity retailer are determined in the power procurement problem.
Keywords: Electricity retailer | Energy pricing policy | Social welfare | Energy business and management | Downside risk constraints
مقاله انگلیسی
9 Risk evaluation of large-scale seawater desalination projects based on an integrated fuzzy comprehensive evaluation and analytic hierarchy process method
ارزیابی ریسک پروژه های آب شیرین کن در مقیاس بزرگ بر اساس یک ارزیابی جامع فازی یکپارچه و روش فرآیند سلسله مراتبی تحلیلی-2020
Desalination projects play a vital role in the water supply of coastal regions with scarce water resources. The risks associated with desalination projects are worth investigating, especially for large-scale projects. This paper presents the risk identification and evaluation processes of large-scale desalination projects. Two levels of risk indicators are identified and the first-level risks include water intake and outfall risk, processing risk, financial risk and circumstance risk. With the identified risk indicators, an integrated fuzzy comprehensive evaluation (FCE) and analytic hierarchy process (AHP) method is introduced to conduct quantitative risk evaluations for large-scale desalination projects. Twenty experts in desalination-related fields are invited to vote to determine the weighting vectors for the FCE through the AHP. They also participate in deciding the membership matrixes in the FCE for three practical desalination projects. The evaluation results indicate that the overall risks of all the considered projects are at the “Very low” level. Finally, to diminish the potential risks, several instructions and recommendations are suggested that depend on the evaluation outcomes. It is expected that the current risk evaluation research will make remarkable contributions to the risk management and control of large-scale desalination projects and further promote the development of the desalination industry.
Keywords: Desalination project | Risk identification | Risk evaluation | Fuzzy comprehensive evaluation | Analytic hierarchy process
مقاله انگلیسی
10 Banking credit worthiness: Evaluating the complex relationships
ارزش اعتبار بانکی: ارزیابی روابط پیچیده-2019
In developing economies agriculture and farming play crucial roles for economic sustainable develop- ment. Farmer credit risk evaluation is an important issue when determining financial support to farm- ers, improving agricultural supply chain performance, and ensuring profitability of financial institutions. Credit risk evaluation, or creditworthiness, is not a trivial exercise due to various complexities. Honor- ing complexity is necessary to effectively evaluate and predict farmer creditworthiness. A methodology using fuzzy rough-set theory and fuzzy C-means clustering is used to evaluate and investigate the com- plex relationships between farmer characteristics, competitive environmental factors, and farmer credit level. The methodology is detailed using actual bank data from 2044 farmers within China. This empir- ical methodology generates decision rules that provide insight to more complex relationships than can be found through standard econometric multivariate approaches. A rule-based methodological outcome can be used to predict the creditworthiness of farmers and to aid in agricultural loan decision making. Prediction accuracy of the rule-base was 81.16%. A central finding is that education and skills related characteristics are important for determining farmer credit-worthiness. Other implications are presented along with study limitations and future research directions
Keywords: Or in banking | Credit risk | Fuzzy rough-set | Fuzzy C-means | Farmers | China
مقاله انگلیسی
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