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1 – 10 of 345Aim to the limitations of grey relational analysis of interval grey number, based on the generalized greyness of interval grey number, this paper tries to construct a grey angle…
Abstract
Purpose
Aim to the limitations of grey relational analysis of interval grey number, based on the generalized greyness of interval grey number, this paper tries to construct a grey angle cosine relational degree model from the perspective of proximity and similarity.
Design/methodology/approach
Firstly, the algorithms of the generalized greyness of interval grey number and interval grey number vector are given, and its properties are analyzed. Then, based on the grey relational theory, the grey angle cosine relational model is proposed based on the generalized greyness of interval grey number, and the relationship between the classical cosine similarity model and the grey angle cosine relational model is analyzed. Finally, the validity of the model in this paper is illustrated by the calculation examples and an application example of related factor analysis of maize yield.
Findings
The results show that the grey angle cosine relational degree model has strict theoretical basis, convenient calculation and is easy to program, which can not only fully utilize the information of interval grey numbers but also overcome the shortcomings of greyness relational degree model. The grey angle cosine relational degree is an extended form of cosine similarity degree of real numbers. The calculation examples and the related factor analysis of maize yield show that the model proposed in this paper is feasible and valid.
Practical implications
The research results not only further enrich the grey system theory and method but also provide a basis for the grey relational analysis of the sequences in which the interval grey numbers coexist with the real numbers.
Originality/value
The paper succeeds in realizing the algorithms of the generalized greyness of interval grey number and interval grey number vector, and the grey angle cosine relational degree, which provide a new method for grey relational analysis.
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Yong Liu, Xue-ge Guo, Qin Jiang and Jing-yi Zhang
We attempt to construct a grey three-way conflict analysis model with constraints to deal with correlated conflict problems with uncertain information.
Abstract
Purpose
We attempt to construct a grey three-way conflict analysis model with constraints to deal with correlated conflict problems with uncertain information.
Design/methodology/approach
In order to address these correlated conflict problems with uncertain information, considering the interactive influence and mutual restraints among agents and portraying their attitudes toward the conflict issues, we utilize grey numbers and three-way decisions to propose a grey three-way conflict analysis model with constraints. Firstly, based on the collected information, we introduced grey theory, calculated the degree of conflict between agents and then analyzed the conflict alliance based on the three-way decision theory. Finally, we designed a feedback mechanism to identify key agents and key conflict issues. A case verifies the effectiveness and practicability of the proposed model.
Findings
The results show that the proposed model can portray their attitudes toward conflict issues and effectively extract conflict-related information.
Originality/value
By employing this approach, we can provide the answers to Deja’s fundamental questions regarding Pawlak’s conflict analysis: “what are the underlying causes of conflict?” and “how can a viable consensus strategy be identified?”
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Sifeng Liu, Ningning Lu, Zhongju Shang and R.M. Kapila Tharanga Rathnayaka
The purpose of this paper is to explore a new approach to solve the problem of positive and negative offset in the calculation process of integral elements, then propose a series…
Abstract
Purpose
The purpose of this paper is to explore a new approach to solve the problem of positive and negative offset in the calculation process of integral elements, then propose a series of new grey relational degree model for cross sequences.
Design/methodology/approach
The definitions of cross sequences and area elements have been proposed at first. Then the concept of difference degree between sequences has been put forward. Based on the definition of difference degree between sequences, various modified grey relational degree models for cross sequences have been proposed to solve the measurement problem of cross sequence correlation relationships.
Findings
(1) The new definition of cross sequences; (2) The area element; (3) Various modified grey relational degree models for cross sequences based on the definition of difference degree between sequences.
Practical implications
The grey relational analysis model of cross sequences is a difficult problem in grey relational analysis. The new model proposed in this article can effectively avoid the calculation deviation of grey relational analysis model for cross sequences, and reasonably measure the correlation between cross sequences. The new model was used to analyse the food consumer price index in Shaanxi Province, clarifying the relationship between different types of food consumer price indices, some interesting results that are not completely consistent with general economic theory were obtained.
Originality/value
The new definition of cross sequences, the area element and various modified grey relational degree models for cross sequences were proposed.
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Marcin Nowak, Marta Pawłowska-Nowak, Małgorzata Kokocińska and Piotr Kułyk
With the use of the grey incidence analysis (GIA), indicators such as the absolute degree of grey incidence (εij), relative degree of grey incidence (rij) or synthetic degree of…
Abstract
Purpose
With the use of the grey incidence analysis (GIA), indicators such as the absolute degree of grey incidence (εij), relative degree of grey incidence (rij) or synthetic degree of grey incidence (ρij) are calculated. However, it seems that some assumptions made to calculate them are arguable, which may also have a material impact on the reliability of test results. In this paper, the authors analyse one of the indicators of the GIA, namely the relative degree of grey incidence. The aim of the article was to verify the hypothesis: in determining the relative degree of grey incidence, the method of standardisation of elements in a series significantly affects the test results.
Design/methodology/approach
To achieve the purpose of the article, the authors used the numerical simulation method and the logical analysis method (in order to draw conclusions from our tests).
Findings
It turned out that the applied method of standardising elements in series when calculating the relative degree of grey incidence significantly affects the test results. Moreover, the manner of standardisation used in the original method (which involves dividing all elements by the first element) is not the best. Much more reliable results are obtained by a standardisation that involves dividing all elements by their arithmetic mean.
Research limitations/implications
Limitations of the conducted evaluation involve in particular the limited scope of inference. This is since the obtained results referred to only one of the indicators classified into the GIA.
Originality/value
In this article, the authors have evaluated the model of GIA in which the relative degree of grey incidence is determined. As a result of the research, the authors have proposed a recommendation regarding a change in the method of standardising variables, which will contribute to obtaining more reliable results in relational tests using the grey system theory.
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Liyi Zhang, Mingyue Fu, Teng Fei, Ming K. Lim and Ming-Lang Tseng
This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.
Abstract
Purpose
This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.
Design/methodology/approach
This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.
Findings
The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.
Originality/value
This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.
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Xiaomei Liu, Bin Ma, Meina Gao and Lin Chen
A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey…
Abstract
Purpose
A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.
Design/methodology/approach
The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.
Findings
The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPEF and MAPET are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).
Originality/value
The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.
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The damping accumulated discrete MGM(1, m) power model is proposed for the problem of forecasting the share of agricultural output value and the share of employment in China.
Abstract
Purpose
The damping accumulated discrete MGM(1, m) power model is proposed for the problem of forecasting the share of agricultural output value and the share of employment in China.
Design/methodology/approach
In this study, the damping accumulated discrete MGM(1, m) power model was developed based on the idea of discrete modelling by introducing a damping accumulated generating operator and power index. The new model can better identify the non-linear characteristics existing between different factors in the multivariate system and can accurately describe and forecast the trend of changes between data series and each of them.
Findings
The validity and rationality of the new model are verified through numerical experiment. It is forecasted that in 2023, the share of agricultural output value in China will be 7.14% and the share of agricultural employment will be 21.98%, with an overall decreasing trend.
Practical implications
The simultaneous decline in the share of agricultural output value and the share of employment is a common feature of countries that have achieved agricultural modernisation. Accurate forecasts of the share of agricultural output value and the share of employment can provide an important scientific basis for formulating appropriate agricultural development targets and policies in China.
Originality/value
The new model proposed in this study fully considers the importance of new information and has higher stability. The differential evolutionary algorithm was used to optimise the model parameters.
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Flavian Emmanuel Sapnken, Mohammed Hamaidi, Mohammad M. Hamed, Abdelhamid Issa Hassane and Jean Gaston Tamba
For some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic…
Abstract
Purpose
For some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic growth and the ambitious projects underway. Therefore, one of the state's priorities is the mastery of electricity demand. In order to get there, it would be helpful to have reliable forecasting tools. This study proposes a novel version of the discrete grey multivariate convolution model (ODGMC(1,N)).
Design/methodology/approach
Specifically, a linear corrective term is added to its structure, parameterisation is done in a way that is consistent to the modelling procedure and the cumulated forecasting function of ODGMC(1,N) is obtained through an iterative technique.
Findings
Results show that ODGMC(1,N) is more stable and can extract the relationships between the system's input variables. To demonstrate and validate the superiority of ODGMC(1,N), a practical example drawn from the projection of electricity demand in Cameroon till 2030 is used. The findings reveal that the proposed model has a higher prediction precision, with 1.74% mean absolute percentage error and 132.16 root mean square error.
Originality/value
These interesting results are due to (1) the stability of ODGMC(1,N) resulting from a good adequacy between parameters estimation and their implementation, (2) the addition of a term that takes into account the linear impact of time t on the model's performance and (3) the removal of irrelevant information from input data by wavelet transform filtration. Thus, the suggested ODGMC is a robust predictive and monitoring tool for tracking the evolution of electricity needs.
Details
Keywords
Weiliang Zhang, Sifeng Liu, Junliang Du, Liangyan Tao and Wenjie Dong
The purpose of this study is to advance a novel evaluation index system and evaluation approach for ability of older adults in China.
Abstract
Purpose
The purpose of this study is to advance a novel evaluation index system and evaluation approach for ability of older adults in China.
Design/methodology/approach
This study constructed a comprehensive older adult ability evaluation index system with 4 primary indicators and 17 secondary indicators. Grey clustering analysis and entropy weight method are combined into a robust evaluation model for the ability of older adults.
Findings
The result demonstrates that the proposed grey clustering model is readily available to calculate the disability level of elderly individuals. The constructed index system more comprehensively considers all aspects of the disability of the elderly.
Originality/value
This study provides a quantitative method and a more reasonable index system for the determination of the disability level of the elderly.
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Keywords
Haider Jouma, Muhamad Mansor, Muhamad Safwan Abd Rahman, Yong Jia Ying and Hazlie Mokhlis
This study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand…
Abstract
Purpose
This study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand is a residential area that includes 20 houses.
Design/methodology/approach
The daily operational strategy of the proposed MG allows to vend and procure utterly between the main grid and MG. The smart metre of every consumer provides the supplier with the daily consumption pattern which is amended by demand side management (DSM). The daily operational cost (DOC) CO2 emission and other measures are utilized to evaluate the system performance. A grey wolf optimizer was employed to minimize DOC including the cost of procuring energy from the main grid, the emission cost and the revenue of sold energy to the main grid.
Findings
The obtained results of winter and summer days revealed that DSM significantly improved the system performance from the economic and environmental perspectives. With DSM, DOC on winter day was −26.93 ($/kWh) and on summer day, DOC was 10.59 ($/kWh). While without considering DSM, DOC on winter day was −25.42 ($/kWh) and on summer day DOC was 14.95 ($/kWh).
Originality/value
As opposed to previous research that predominantly addressed the long-term operation, the value of the proposed research is to investigate the short-term operation (24-hour) of MG that copes with vital contingencies associated with selling and procuring energy with the main grid considering the environmental cost. Outstandingly, the proposed research engaged the consumers by smart meters to apply demand-sideDSM, while the previous studies largely focused on supply side management.
Details