61,084 research outputs found
Method of Group Decision Making with Interval Grey Numbers based on Grey Correlation and Relative Close Degree
With the development of society, big data\u27s concept is mentioned more and more. However, in the numerous and complex mass data, the effective information is often very limited, and the data often show the complex characteristics of "close and orderly short-term correlation and large long-term disturbance", which is difficult to be expressed by a long sequence composed of a single real value. This paper takes the interval grey number as the research object for the uncertain system of "less data and poor information". According to the multi-attribute group decisions problem with attributeâs values of interval grey number and the weights of decision-makers and attributes that are completely unknown, a method of group decision making with interval grey numbers based on grey correlation and relative close degree is proposed. For the purpose of making effective decisions, the weights of decision-makers and attributes are calculated by correlation degree of decision information which is measured by the model of grey correlation analysis based on interval grey number deviation degree. On this basis, the correlation degree between the scheme and the positive and negative ideal schemes are computed, and then the grey correlation and relative close degree of the scheme is calculated. Finally, the scheme is sorted according to the value of the grey correlation and relative close degree. The rationality and feasibility of the proposed method are verified by numerical examples
Grey Systems Analysis
This open access book is the 2nd edition involving the update on data, methods and models of Grey Systems. It covers up-to-date theoretical and applied advances in grey systems from across the world, and vividly presents the reader with the overall picture of this new theory and its frontier research. Many of the concepts, models and methods in the book are original by the author, including kernel, degree of greyness of grey number, simplified form of grey number, general grey number and the operation system; the axiomatic system of buffer operators and a series of weakening and strengthening buffer operators; a series of grey relational analysis models, including grey absolute, relative, synthetic, similarity, closeness, negative, three dimension, and grey relational analysis model for cross-sequences, etc.; grey fixed weight clustering model, grey evaluation models based on center-point and end-point mixed possibility functions; original difference grey model (ODGM), even difference grey model (EDGM), discrete grey model (DGM), fractional grey models, self-memory grey models; multi-attribute weighted intelligent grey target decision models, kernel weight vector group and the weighted comprehensive clustering coefficient vector, and spectrum analysis of sequence operators, etc. The revision includes: (1) Added new achievements made in recent years, such as the moving average denoise operator, a series of negative grey relational models, grey relational model for cross-sequences, standard uncertainty numbers and their operations, adaptive Grey Prediction Models and so on; (2) Important data related to the development of grey system theory has been updated; (3) Research reviews have been added to each chapter, and a large number of references have been added; (4)Updated application examples of commonly used models and methods. This book will be appropriate as a reference and/or textbook for courses of grey system theory for graduate students or high level undergraduate students, majoring in various fields of natural sciences, social sciences and engineering technology. It can also be utilized by researchers and technicians in research institutions, business entities, and government agencies
Forecasting the multifactorial interval grey number sequences using grey relational model and GM (1, N) model based on effective information transformation
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In the context of data eruption, the data often shows a short-term pattern and changes rapidly which makes it difficult to use a single real value to express. For this kind of small-sample and interval data, how to analyze and predict muti-factor sequences efficiently becomes a problem. By this means, grey system theory (GST) is developed in which the interval grey numbers, as a
typical object of GST, characterize the range of data and the grey relational and prediction models analyze the relations of multiple grey numbers and forecast the future. However, traditional grey relative relational model has some limitations: the results obtained always show low resolution and there are no extractions for the interval feature information from the interval grey number sequence. In this paper, the grey relational analysis model (GRA) based on effective information transformation of interval grey numbers is established, which contains comprehensive information of area differences and slope variances and optimizes the resolution of traditional grey degree. Then, according to the relational results, the multivariable GM model (GM(1,N)) is proposed to forecast the interval grey number sequence. To verify the effectiveness of this novel model, it is established to analyze the relationship between the degree of traffic congestion and its relevant factors in the Yangtze River Delta of China and predict the development of urban traffic congestion degrees in this area over the next five years. In addition, some traditional statistical methods (principal component analysis, multiple linear regression models and curve regression models) are established for comparisons. The results show high performances of the novel GRA model and GM(1,N) model, which means the models proposed in this paper are suitable for interval grey numbers from regional data. The strengths which recommend the use of this novel method lie in its high recognition mechanism and muti-angle information transformation for interval grey numbers as well as its characteristic of timeliness in information processing
A hybrid method of GRA and DEA for evaluating and selecting efficient suppliers plus a novel ranking method for grey numbers
Purpose: Evaluation and selection of efficient suppliers is one of the key issues in supply chain
management which depends on wide range of qualitative and quantitative criteria. The aim of
this research is to develop a mathematical model for evaluating and selecting efficient suppliers
when faced with supply and demand uncertainties.
Design/methodology/approach: In this research Grey Relational Analysis (GRA) and Data
Envelopment Analysis (DEA) are used to evaluate and select efficient suppliers under
uncertainties. Furthermore, a novel ranking method is introduced for the units that their
efficiencies are obtained in the form of interval grey numbers.
Findings: The study indicates that the proposed model in addition to providing satisfactory
and acceptable results avoids time-consuming computations and consequently reduces the
solution time. To name another advantage of the proposed model, we can point out that it
enables us to make decision based on different levels of risk.
Originality/value: The paper presents a mathematical model for evaluating and selecting
efficient suppliers in a stochastic environment so that companies can use in order to make
better decisions.Peer Reviewe
Multi-criteria decision analysis towards robust service quality measurement
Importance: The role of airports is critical for a region in which it is viewed as an engine for the economic
development. Facilities, infrastructure, information and in general the services offered by an airport represent the
fuel for this engine. Evidently, customers and travelers expect standard-quality services that need to be framed
and measured. Therefore, services in airports should be quantified and maintained, accordingly.
Objectives: This article reports a case study for evaluating quality of services offered by five main airports located
in Spain. Quality of service was modelled based on a number of factors such as convenience, comfort, courtesy of
staffs, information visibility, prices, security, and transportation facilities. The grey based multi-criteria decision
analysis (MCDA) was employed towards a reliable evaluation process by airport experts and to accommodate the
several qualitative and conflicting evaluation factors with distinct definitions. To this end, Grey Step-wise Weight
Assessment Ratio Analysis (SWARA-G) and grey Measurement of Alternatives and Ranking according to
COmpromise Solution (MARCOS-G) methods were applied for quantifying relative weights of decision factors
and rating airports, respectively. Several sensitivity analysis, simulations, and comparisons were conducted for
verifying the preciseness of the revealed results.
Findings: Research findings demonstrate that the proposed SWARA-G-MARCOS-G-based methodology (i) enables
decision makers to express their preferences clearly; and (ii) attenuates the embedded subjectivity and uncertainty
within the decision-making process. In addition, they revealed that access to the parking and Wi-Fi
connection are amongst the critical factors in evaluating service quality of airport.
Contribution: This paper contributes to related literature in presenting a novel decision-making approach for
measuring service quality of airports, validated via a real-case study. The employed interval and linguistic grey
variables allow experts, in airport operations, to express their opinions with higher flexibility and comfortability.
The presented model could be re-applied for other studies or practical cases as a user-friendly decision support
system
Data Science: Measuring Uncertainties
With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems
The Impact of Global Warming and Anoxia on Marine Benthic Community Dynamics: an Example from the Toarcian (Early Jurassic)
The Pliensbachian-Toarcian (Early Jurassic) fossil record is an archive of natural data of benthic community response to global warming and marine long-term hypoxia and anoxia. In the early Toarcian mean temperatures increased by the same order of magnitude as that predicted for the near future; laminated, organic-rich, black shales were deposited in many shallow water epicontinental basins; and a biotic crisis occurred in the marine realm, with the extinction of approximately 5% of families and 26% of genera. High-resolution quantitative abundance data of benthic invertebrates were collected from the Cleveland Basin (North Yorkshire, UK), and analysed with multivariate statistical methods to detect how the fauna responded to environmental changes during the early Toarcian. Twelve biofacies were identified. Their changes through time closely resemble the pattern of faunal degradation and recovery observed in modern habitats affected by anoxia. All four successional stages of community structure recorded in modern studies are recognised in the fossil data (i.e. Stage III: climax; II: transitional; I: pioneer; 0: highly disturbed). Two main faunal turnover events occurred: (i) at the onset of anoxia, with the extinction of most benthic species and the survival of a few adapted to thrive in low-oxygen conditions (Stages I to 0) and (ii) in the recovery, when newly evolved species colonized the re-oxygenated soft sediments and the path of recovery did not retrace of pattern of ecological degradation (Stages I to II). The ordination of samples coupled with sedimentological and palaeotemperature proxy data indicate that the onset of anoxia and the extinction horizon coincide with both a rise in temperature and sea level. Our study of how faunal associations co-vary with long and short term sea level and temperature changes has implications for predicting the long-term effects of âdead zonesâ in modern oceans
Supplier Selection Model Based on D Numbers and Transformation Function
Selecting reasonable suppliers can effectively improve the efficiency of enterprise supply chain management. Among them, expert evaluation is an important part of supplier selection problem, but the uncertainty, fuzziness and incompleteness of expert opinions make supplier selection problem difficult to solve. In order to systematically and effectively solve the uncertainty, ambiguity and incompleteness in supplier selection problem, this paper presents a new supplier selection method based on D numbers and transformation function. First, fuzzy preference relation is generated based on the decision matrix of pairwise comparisons given by experts. D numbers which can effectively deal with uncertain information extend fuzzy preference relation (D matrix). Second, the D matrix is converted into a crisp matrix form based on the integration representation of D numbers according to different situations whether or not the information in D matrix is complete. Third, the crisp matrix is converted into judgement matrix by using the transformation functions. Finally, analytic hierarchy process (AHP) method is applied based on the judgment matrix to give a priority weights for decision making. Three numerical examples and application of the supplier selection are used to show the feasibility and effectiveness of the proposed method
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