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

Matrix representations of the inverse problem in the graph model for conflict resolution

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.ejor.2018.03.007 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Given the final individual stability for each decision maker or an equilibrium of interest, a matrix-based method for an inverse analysis is developed in order to calculate all of the possible preferences for each decision maker creating the stability results based on the Nash, general metarationality, symmetric meta rationality, or sequential stability definition of possible human interactions in a conflict. The matrix representations are furnished for the relative preferences, unilateral movements and improvements, as well as joint movements and joint improvements for a conflict having two or more decision makers. Theoretical conditions are derived for specifying required preference relationships in an inverse graph model. Under each of the four solution concepts, a matrix relationship is established to obtain all the available preferences for each decision maker causing the specific state to be an equilibrium. To explain how it can be employed in practice, this new approach to inverse analysis is applied to the Elsipogtog First Nation fracking dispute which took place in the Canadian Province of New Brunswick.National Natural Science Foundation of China [71371098, 71471087, 71301060, 71071077]Nanjing University of Aeronautics and Astronautics [BCXJ15-10]Natural Sciences and Engineering Research Council of CanadaJiangsu Innovation Program for Graduate Education [KYZZ15_0093

Multiattribute Grey Target Decision Method Based on Soft Set Theory

With respect to the Multiattribute decision-making problems in which the evaluation attribute sets are different and the evaluating values of alternatives are interval grey numbers, a multiattribute grey target decision-making method in which the attribute sets are different was proposed. The concept of grey soft set was defined, and its “AND” operation was assigned by combining the intersection operation of grey number. The expression approach of new grey soft set of attribute sets considering by all decision makers were gained by applying the “AND” operation of grey soft set, and the weights of synthesis attribute were proved. The alternatives were ranked according to the size of distance of bull’s eyes of each alternative under synthetic attribute sets. The green supplier selection was illustrated to demonstrate the effectiveness of proposed model

A Decision-Making Method with Grey Multi-Source Heterogeneous Data and Its Application in Green Supplier Selection

In view of the multi-attribute decision-making problem that the attribute values are grey multi-source heterogeneous data, a decision-making method based on kernel and greyness degree is proposed. The definitions of kernel and greyness degree of an extended grey number in a grey multi-source heterogeneous data sequence are given. On this basis, we construct the kernel vector and greyness degree vector of the sequence to whiten the multi-source heterogeneous information, then a grey relational bi-directional projection ranking method is presented. Considering the multi-attribute multi-level decision structure and the causalities between attributes in decision-making problem, the HG-DEMATEL method is proposed to determine the hierarchical attribute weights. A green supplier selection example is provided to demonstrate the rationality and validity of the proposed method

The Choice and Mathematical Model of Leading Industries in Jiangsu Province *

Abstract: The author provides an analysis of the regional leading industries to establish an evaluating index system, which is based on interrelationship index among industries, index of income elasticity of demand, index of rate of increase and employment index of labor force. Also he develops an evaluating model of grey system theory cluster to do an empirical research of the choice of leading industries in Jiangsu province, and finds that the results obtained conform to the actual situation so relatively that can provide the policy decision basis for the government department. Key words: leading industries evaluating index grey system mathematical model 1

A novel energy consumption forecasting model combining an optimized DGM (1, 1) model with interval grey numbers

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.Since energy consumption (EC) is becoming an important issue for sustainable development in the world, it has a practical significance to predict EC effectively. However, there are two main uncertainty factors affecting the accuracy of a region's EC prediction. Firstly, with the ongoing rapid changes in society, the consumption amounts can be non-smooth or even fluctuating during a long time period, which makes it difficult to investigate the sequence's trend in order to forecast. Secondly, in a given region, it is difficult to express the consumption amount as a real number, as there are different development levels in the region, which would be more suitably described as interval numbers. Most traditional prediction models for energy consumption forecasting deal with long-term real numbers. It is seldom found to discover research that focuses specifically on uncertain EC data. To this end, a novel energy consumption forecasting model has been established by expressing ECs in a region as interval grey numbers combining with the optimized discrete grey model (DGM(1,1)) in Grey System Theory (GST). To prove the effectiveness of the method, per capita annual electricity consumption in southern Jiangsu of China is selected as an example. The results show that the proposed model reveals the best accuracy for the short data sequences (the average fitting error is only 2.19% and the average three-step forecasting error is less than 4%) compared with three GM models and four classical statistical models. By extension, any fields of EC, such as petroleum consumption, natural gas consumption, can also be predicted using this novel model. As the sustained growth in EC of China's, it is of great significance to predict EC accurately to manage serious energy security and environmental pollution problems, as well as formulating relevant energy policies by the government