scholarly journals Econometric model for forecasting oil production in OECD member states

2020 ◽  
Vol 159 ◽  
pp. 02005
Author(s):  
Carmen Valentina Rădulescu ◽  
Dumitru Alexandru Bodislav ◽  
Sorin Burlacu ◽  
Florina Bran ◽  
Lyaman Karimova
Keyword(s):  
Financial Markets ◽  
Econometric Model ◽  
Oil Production ◽  
Decision Makers ◽  
Production Forecast ◽  
Member States ◽  
Proposed Model ◽  
Oil And Natural Gas ◽  
Social Environmental ◽  
The Ideal

In this article we present an econometric model of oil production forecast at OECD member level that will allow decision makers but also other oil product stakeholders to be responsible for oil production in OECD member states. This responsibility can be perceived from several perspectives: economic, social, environmental, political, military etc. In order to be able to find the ideal formula for our calculation, we went through the specialized literature and brought elements of analysis during the research through several econometric paths traveled by other researchers and who provided us with support for our research. Before proceeding technically, in order to understand the urgency of this approach and of this study, we also discussed how oil and natural gas are explored, exploited and extracted from the underground deposits. We considered that the proposed model could be improved in the future so as to portray certain geopolitical or economic factors, determinants for oil production, such as embargoes, periods of armed conflict in the main extraction areas or times of financial crisis and the decline of financial markets.

scholarly journals Application of fuzzy technique for order preference by similarity to the ideal solution in the selection of candidates

2012 ◽  
Vol 9 (1) ◽  
pp. 35
Author(s):  
Mohd Ariff Ahmad Taharim ◽  
Liew Kee Kor
Keyword(s):  
Real Life ◽  
Fuzzy Topsis ◽  
Decision Makers ◽  
Mathematical Form ◽  
Selection Processes ◽  
Proposed Model ◽  
Order Preference ◽  
The Right ◽  
Selection Of ◽  
The Ideal

Selecting the right candidate for the right cause is similar to identifying the most compromising solution of multi-criteria decision making (MCDM) problem. In real life the selection criteriamay involve vague and incomplete data which cannot be expressed in precise mathematical form or numerical values. Apparently fuzzy-based technique can be applied to describe and represent these data in fuzzy numbers. This paper presents a MCDM fuzzy TOPSIS based model designed to solve the selection problemfor allocation of government staff quarters. Result shows that the proposed model is suitable and appropriate. It was also found that the MCDM model which uses single decision maker rating process can also be applied to multiple decision makers. It is recommended that the application of fuzzy TOPSIS can be extended to other selection processes such as vendor selection, training evaluation or group marking of project works.

Decision making based on intuitionistic fuzzy preference relations with additive approximate consistency

2020 ◽  
Vol 39 (3) ◽  
pp. 4041-4058
Author(s):  
Fang Liu ◽  
Xu Tan ◽  
Hui Yang ◽  
Hui Zhao
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Real Interval ◽  
Preference Relations ◽  
Intuitionistic Fuzzy ◽  
Proposed Model ◽  
Fuzzy Preference Relations ◽  
Novel Concept ◽  
Fuzzy Weights ◽  
Fuzzy Preference

Intuitionistic fuzzy preference relations (IFPRs) have the natural ability to reflect the positive, the negative and the non-determinative judgements of decision makers. A decision making model is proposed by considering the inherent property of IFPRs in this study, where the main novelty comes with the introduction of the concept of additive approximate consistency. First, the consistency definitions of IFPRs are reviewed and the underlying ideas are analyzed. Second, by considering the allocation of the non-determinacy degree of decision makers’ opinions, the novel concept of approximate consistency for IFPRs is proposed. Then the additive approximate consistency of IFPRs is defined and the properties are studied. Third, the priorities of alternatives are derived from IFPRs with additive approximate consistency by considering the effects of the permutations of alternatives and the allocation of the non-determinacy degree. The rankings of alternatives based on real, interval and intuitionistic fuzzy weights are investigated, respectively. Finally, some comparisons are reported by carrying out numerical examples to show the novelty and advantage of the proposed model. It is found that the proposed model can offer various decision schemes due to the allocation of the non-determinacy degree of IFPRs.

Equivariant Adversarial Network for Image-to-image Translation

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-14
Author(s):  
Masoumeh Zareapoor ◽  
Jie Yang
Keyword(s):  
State Of The Art ◽  
Generative Models ◽  
Generative Model ◽  
Target Domain ◽  
Adversarial Network ◽  
Proposed Model ◽  
Image Translation ◽  
Great Performance ◽  
Representative Model ◽  
The Ideal

Image-to-Image translation aims to learn an image from a source domain to a target domain. However, there are three main challenges, such as lack of paired datasets, multimodality, and diversity, that are associated with these problems and need to be dealt with. Convolutional neural networks (CNNs), despite of having great performance in many computer vision tasks, they fail to detect the hierarchy of spatial relationships between different parts of an object and thus do not form the ideal representative model we look for. This article presents a new variation of generative models that aims to remedy this problem. We use a trainable transformer, which explicitly allows the spatial manipulation of data within training. This differentiable module can be augmented into the convolutional layers in the generative model, and it allows to freely alter the generated distributions for image-to-image translation. To reap the benefits of proposed module into generative model, our architecture incorporates a new loss function to facilitate an effective end-to-end generative learning for image-to-image translation. The proposed model is evaluated through comprehensive experiments on image synthesizing and image-to-image translation, along with comparisons with several state-of-the-art algorithms.

scholarly journals Optimizing a Reverse Supply Chain Network for Electronic Waste under Risk and Uncertain Factors

2021 ◽  
Vol 11 (4) ◽  
pp. 1946
Author(s):  
Linh Thi Truc Doan ◽  
Yousef Amer ◽  
Sang-Heon Lee ◽  
Phan Nguyen Ky Phuc ◽  
Tham Thi Tran
Keyword(s):  
Supply Chain ◽  
Electronic Waste ◽  
Decision Makers ◽  
Reverse Supply Chain ◽  
Satisfactory Solution ◽  
Level Of Satisfaction ◽  
Proposed Model ◽  
Objective Value ◽  
The Right ◽  
The Impact

Minimizing the impact of electronic waste (e-waste) on the environment through designing an effective reverse supply chain (RSC) is attracting the attention of both industry and academia. To obtain this goal, this study strives to develop an e-waste RSC model where the input parameters are fuzzy and risk factors are considered. The problem is then solved through crisp transformation and decision-makers are given the right to choose solutions based on their satisfaction. The result shows that the proposed model provides a practical and satisfactory solution to compromise between the level of satisfaction of constraints and the objective value. This solution includes strategic and operational decisions such as the optimal locations of facilities (i.e., disassembly, repairing, recycling facilities) and the flow quantities in the RSC.

Matching Returning Donors to Projects on Philanthropic Crowdfunding Platforms

2021 ◽  
Author(s):  
Yicheng Song ◽  
Zhuoxin Li ◽  
Nachiketa Sahoo
Keyword(s):  
Unique Feature ◽  
Econometric Model ◽  
Current System ◽  
Personalized Recommendation ◽  
Data Set ◽  
Proposed Model ◽  
K 12 ◽  
Structural Econometric Model ◽  
Structural Econometric ◽  
Data Period

We propose an approach to match returning donors to fundraising campaigns on philanthropic crowdfunding platforms. It is based on a structural econometric model of utility-maximizing donors who can derive both altruistic (from the welfare of others) and egoistic (from personal motivations) utilities from donating—a unique feature of philanthropic giving. We estimate our model using a comprehensive data set from DonorsChoose.org—the largest crowdfunding platform for K–12 education. We find that the proposed model more accurately identifies the projects that donors would like to donate to on their return in a future period, and how much they would donate, than popular personalized recommendation approaches in the literature. From the estimated model, we find that primarily egoistic factors motivate over two-thirds of the donations, but, over the course of the fundraising campaign, both motivations play a symbiotic role: egoistic motivations drive the funding in the early stages of a campaign when the viability of the project is still unclear, whereas altruistic motivations help reach the funding goal in the later stages. Finally, we design a recommendation policy using the proposed model to maximize the total funding each week considering the needs of all projects and the heterogeneous budgets and preferences of donors. We estimate that over the last 14 weeks of the data period, such a policy would have raised 2.5% more donation, provided 9% more funding to the projects by allocating them to more viable projects, funded 17% more projects, and provided 15% more utility to the donors from the donations than the current system. Counterintuitively, we find that the policy that maximizes total funding each week leads to higher utility for the donors over time than a policy that maximizes donors’ total utility each week. The reason is that the funding-maximizing policy focuses donations on more viable projects, leading to more funded projects, and, ultimately, higher realized donors’ utility. This paper was accepted by Kartik Hosanagar, information systems.

Fuzzy multicriteria decision-making-based optimal Zn–Al alloy selection in corrosive environment

2020 ◽  
Vol 111 (11) ◽  
pp. 953-963
Author(s):  
Ankur V. Bansod ◽  
Awanikumar P. Patil ◽  
Kanak Kalita ◽  
B. D. Deshmukh ◽  
Nilay Khobragade
Keyword(s):  
Decision Making ◽  
Weight Loss ◽  
Multicriteria Decision Making ◽  
Decision Makers ◽  
Al Alloy ◽  
Ideal Solution ◽  
Corrosive Environment ◽  
Multicriteria Decision ◽  
Al Content ◽  
The Ideal

Abstract Suitable material selection with emphasis on a specific property or application is an indispensable part of engineering sciences. It is a complex process that involves multiple criteria and often multiple decision makers. The tendency of decision makers to specify their preference in terms of imprecise qualitative statements like ‘good’, ‘bad’ etc. poses a further challenge. Thus, in this research, a comprehensive multicriteria decision-making study was conducted to select the optimal Zn-Al alloy based on performance in a corrosive environment. Four variants of technique for order of preference by similarity to the ideal solution were used to perform the multicriteria decision-making analysis. Group decision and imprecise decision making is handled by incorporating the fuzzy theory concept in a technique for order of preference by similarity to the ideal solution. The effect of addition of aluminium to zinc was studied by examination of microstructure, hardness, and corrosion behaviour. The result indicates that an increase in Al content increases the formation of dendrites. The dendrites were rich in the α phase, which results in an increase in hardness. An increase in Al content in Zn (Zn-22Al and Zn-55Al) results in the uniform distribution of the a phase in the microstructure and reduction of non-equilibrium phases. The potentiodynamic polarisation test revealed that an increase in Al in the alloy decreases the corrosion current density. The weight loss test carried out to validate the potentiodynamic test findings exhibited higher weight loss in pure Zn and lowest in Zn-55Al. Similar results were observed in the salt spray test. The multicriteria decision-making analysis revealed that Zn-55Al is the most suitable alloy in a corrosive environment among the tested alloys.

GIS Based Multi-Criteria decision support system and machine learning for Hospital Site Selection case study Oran - Algeria

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Site Selection ◽  
Decision Makers ◽  
Analysis Method ◽  
Automatic Learning ◽  
Hospital Site ◽  
Set Up ◽  
High Level ◽  
Selection Of ◽  
The Ideal

The selection of hospital sites is one of the most important choice a decision maker has to take so as to resist the pandemic. The decision may considerably affect the outbreak transmission in terms of efficiency , budget, etc. The main targeted objective of this study is to find the ideal location where to set up a hospital in the willaya of Oran Alg. For this reason, we have used a geographic information system coupled to the multi-criteria analysis method AHP in order to evaluate diverse criteria of physiological positioning , environmental and economical. Another objective of this study is to evaluate the advanced techniques of the automatic learning . the method of the random forest (RF) for the patterning of the hospital site selection in the willaya of Oran. The result of our study may be useful to decision makers to know the suitability of the sites as it provides a high level of confidence and consequently accelerate the power to control the COVID19 pandemic.

scholarly journals Interval Forecasting in Supply Chain with Small Sample

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Lifeng Wu ◽  
Yan Chen
Keyword(s):  
Supply Chain ◽  
Small Sample ◽  
Decision Makers ◽  
Small Samples ◽  
Forecasting Model ◽  
Future Trends ◽  
Human Judgment ◽  
Proposed Model ◽  
Grey Models ◽  
Short Time

To deal with the forecasting with small samples in the supply chain, three grey models with fractional order accumulation are presented. Human judgment of future trends is incorporated into the order number of accumulation. The output of the proposed model will provide decision-makers in the supply chain with more forecasting information for short time periods. The results of practical real examples demonstrate that the model provides remarkable prediction performances compared with the traditional forecasting model.

scholarly journals A Cellular Automata Model of the Relationship between Adverse Events and Regional Infrastructure Development in an Active War Theater

Technologies ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 54
Author(s):  
Bozkurt ◽  
Karwowski ◽  
Çakıt ◽  
Ahram
Keyword(s):  
Adverse Event ◽  
Cellular Automata ◽  
Adverse Events ◽  
Decision Makers ◽  
Development Projects ◽  
Infrastructure Development ◽  
Complex Interactions ◽  
Proposed Model ◽  
Ca Model ◽  
Complex Formations

This study presents a cellular automata (CA) model to assist decision-makers in understanding the effects of infrastructure development projects on adverse events in an active war theater. The adverse events are caused by terrorist activities that primarily target the civilian population in countries such as Afghanistan. In the CA-based model, cells in the same neighborhood synchronously interact with one another to determine their next states, and small changes in iteration yield to complex formations of adverse event risks. The results demonstrate that the proposed model can help in the evaluation of infrastructure development projects in relation to changes in the reported adverse events, as well as in the identification of the geographical locations, times, and impacts of such developments. The results also show that infrastructure development projects have different impacts on the reported adverse events. The CA modeling approach can be used to support decision-makers in allocating infrastructure development funds to stabilize active war regions with higher adverse event risks. Such models can also improve the understanding of the complex interactions between infrastructure development projects and adverse events.

scholarly journals Large Is Riskier: The Case of European Commercial Banks

2020 ◽  
Vol 16 (1) ◽  
pp. 19
Author(s):  
Daniela Venanzi
Keyword(s):  
Financial Markets ◽  
Commercial Banks ◽  
Systematic Risk ◽  
Decision Makers ◽  
Financial Accounting ◽  
Growing Up ◽  
Financial Firms ◽  
European Banks ◽  
Accounting Ratios ◽  
Bank Risks

Which factors determine the systematic risk of European banks? The issue is very important for regulators and decision-makers in financial markets. This study follows the Beaver, Kettler and Scholes (1970)’s pioneering approach, which estimates true betas of not-financial firms by correcting the observed market betas through the fundamental financial/accounting ratios that better explain the systematic risk. By extending this approach to commercial banks, the fundamental betas of a sample of more than 100 European banks in 2006-2015 period, are empirically estimated. The emerging findings show that size, diversification, derivatives, and TEXAS ratio increase the systematic risk of banks and that the risk weighting of assets, based on Basel framework, does not correctly catch the bank risks (as perceived by the market), since it influences negatively their beta. This evidence weakens the dominant belief among European supervisory institutions and governments that growing up through M&As is the panacea for European banks.

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