JournalTOCs

Grey Systems : Theory and Application
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Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2043-9377 - ISSN (Online) 2043-9385
Published by Emerald

[362 journals]

An optimal wavelet transform grey multivariate convolution model to
forecast electricity demand: a novel approach
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  Authors: Flavian Emmanuel Sapnken, Mohammed Hamaidi, Mohammad M. Hamed, Abdelhamid Issa Hassane, Jean Gaston Tamba
  Abstract: 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)). 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. 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. 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.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-11-14
  DOI: 10.1108/GS-09-2023-0090
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Memory-dependent derivative grey Bernoulli model and its application in
electricity generation forecast
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  Authors: Yonghong Zhang, Shouwei Li, Jingwei Li, Xiaoyu Tang
  Abstract: This paper aims to develop a novel grey Bernoulli model with memory characteristics, which is designed to dynamically choose the optimal memory kernel function and the length of memory dependence period, ultimately enhancing the model's predictive accuracy. This paper enhances the traditional grey Bernoulli model by introducing memory-dependent derivatives, resulting in a novel memory-dependent derivative grey model. Additionally, fractional-order accumulation is employed for preprocessing the original data. The length of the memory dependence period for memory-dependent derivatives is determined through grey correlation analysis. Furthermore, the whale optimization algorithm is utilized to optimize the cumulative order, power index and memory kernel function index of the model, enabling adaptability to diverse scenarios. The selection of appropriate memory kernel functions and memory dependency lengths will improve model prediction performance. The model can adaptively select the memory kernel function and memory dependence length, and the performance of the model is better than other comparison models. The model presented in this article has some limitations. The grey model is itself suitable for small sample data, and memory-dependent derivatives mainly consider the memory effect on a fixed length. Therefore, this model is mainly applicable to data prediction with short-term memory effect and has certain limitations on time series of long-term memory. In practical systems, memory effects typically exhibit a decaying pattern, which is effectively characterized by the memory kernel function. The model in this study skillfully determines the appropriate kernel functions and memory dependency lengths to capture these memory effects, enhancing its alignment with real-world scenarios. Based on the memory-dependent derivative method, a memory-dependent derivative grey Bernoulli model that more accurately reflects the actual memory effect is constructed and applied to power generation forecasting in China, South Korea and India.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-11-10
  DOI: 10.1108/GS-06-2023-0048
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
An intuitionistic fuzzy grey-Markov method with application to demand
forecasting for emergency supplies during major epidemics
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  Authors: Zhiying Wang, Hongmei Jia
  Abstract: Forecasting demand of emergency supplies under major epidemics plays a vital role in improving rescue efficiency. Few studies have combined intuitionistic fuzzy set with grey-Markov method and applied it to the prediction of emergency supplies demand. Therefore, this article aims to establish a novel method for emergency supplies demand forecasting under major epidemics. Emergency supplies demand is correlated with the number of infected cases in need of relief services. First, a novel method called the Intuitionistic Fuzzy TPGM(1,1)-Markov Method (IFTPGMM) is proposed, and it is utilized for the purpose of forecasting the number of people. Then, the prediction of demand for emergency supplies is calculated using a method based on the safety inventory theory, according to numbers predicted by IFTPGMM. Finally, to demonstrate the effectiveness of the proposed method, a comparative analysis is conducted between IFTPGMM and four other methods. The results show that IFTPGMM demonstrates superior predictive performance compared to four other methods. The integration of the grey method and intuitionistic fuzzy set has been shown to effectively handle uncertain information and enhance the accuracy of predictions. The main contribution of this article is to propose a novel method for emergency supplies demand forecasting under major epidemics. The benefits of utilizing the grey method for handling small sample sizes and intuitionistic fuzzy set for handling uncertain information are considered in this proposed method. This method not only enhances existing grey method but also expands the methodologies used for forecasting demand for emergency supplies. An intuitionistic fuzzy TPGM(1,1)-Markov method (IFTPGMM) is proposed.The safety inventory theory is combined with IFTPGMM to construct a prediction method.Asymptomatic infected cases are taken to forecast the demand for emergency supplies.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-11-06
  DOI: 10.1108/GS-07-2023-0062
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Enterprise blockchain solutions for vibrant construction ecosystem: Grey
Ordinal Priority Approach
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  Authors: Mahsa Sadeghi, Amin Mahmoudi, Xiaopeng Deng, Leila Moslemi Naeni
  Abstract: The aim of this article states that in each stage of the industrial revolution, only a few initiatives have been real game changers. In Industry 3.0, “Internet of Information” has transformed the business landscape via connectivity and communications. Enterprises could come together to spur innovation in a cooperative or competitive manner. In Industry 4.0, the “Internet of Value” has shown considerable benefits; and, blockchain technology is expected to touch all layers of a business ecosystem, and the construction industry is not an exception. This study aims to answer the “How do enterprise blockchain solutions contribute to the vibrancy of the construction ecosystem from social, economic, and environmental aspects'” Following a comprehensive literature review, the Grey Ordinal Priority Approach (OPA-G) is employed in multiple criteria decision analysis (MCDA). OPA-G can select functionally rich enterprise blockchain solutions that meet the needs of the future construction industry, while there is uncertainty in the input data. The results from the case study show that organization under observation welcomes an enterprise blockchain solution that delivers services related to “renewable energy certificates” in the context of “smart cities and built environment”. Employing high-ranked blockchain solutions brings vibracy and sustainability to construction ecosystem in terms of “C6. decentralized finance and investment,” “C3. multi-party and cross-industry collaboration,” and “C8. data-driven value creation”. At the micro level, blockchain solutions automate processes, streamline operations, and build new capacities on a new business model. At the macro level, blockchain creates a vibrant ecosystem based on transparency, decentralization, consensus-based democracy, interoperability, etc. Indeed, the capability of blockchain solutions at an enterprise scale (enterprise blockchain solutions) can shape a new construction ecosystem. The practical implications of current research are preparing executives for a fundamentally different next normal in construction.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-11-02
  DOI: 10.1108/GS-07-2023-0060
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
A matrixed nonlinear exponential grey Bernoulli model for interval number
prediction of crude oil futures prices
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  Authors: Haoze Cang, Xiangyan Zeng, Shuli Yan
  Abstract: The effective prediction of crude oil futures prices can provide a reference for relevant enterprises to make production plans and investment decisions. To the nonlinearity, high volatility and uncertainty of the crude oil futures price, a matrixed nonlinear exponential grey Bernoulli model combined with an exponential accumulation generating operator (MNEGBM(1,1)) is proposed in this paper. First, the original sequence is processed by the exponential accumulation generating operator to weaken its volatility. The nonlinear grey Bernoulli and exponential function models are combined to fit the preprocessed sequence. Then, the parameters in MNEGBM(1,1) are matrixed, so the ternary interval number sequence can be modeled directly. Marine Predators Algorithm (MPA) is chosen to optimize the nonlinear parameters. Finally, the Cramer rule is used to derive the time recursive formula. The predictive effectiveness of the proposed model is verified by comparing it with five comparison models. Crude oil futures prices in Cushing, OK are predicted and analyzed from 2023/07 to 2023/12. The prediction results show it will gradually decrease over the next six months. Crude oil futures prices are highly volatile in the short term. The use of grey model for short-term prediction is valuable for research. For the data characteristics of crude oil futures price, this study first proposes an improved model for interval number prediction of crude oil futures prices.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-10-23
  DOI: 10.1108/GS-08-2023-0073
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Forecasting Chinese carbon emission intensity based on the interactive
effect GM(1,N) power model
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  Authors: Yuhong Wang, Qi Si
  Abstract: This study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China. In this paper, the Interaction Effect Grey Power Model of N Variables (IEGPM(1,N)) is developed, and the Dragonfly algorithm (DA) is used to select the best power index for the model. Specific model construction methods and rigorous mathematical proofs are given. In order to verify the applicability and validity, this paper compares the model with the traditional grey model and simulates the carbon emission intensity of China from 2014 to 2021. In addition, the new model is used to predict the carbon emission intensity of China from 2022 to 2025, which can provide a reference for the 14th Five-Year Plan to develop a scientific emission reduction path. The results show that if the Chinese government does not take effective policy measures in the future, carbon emission intensity will not achieve the set goals. The IEGPM(1,N) model also provides reliable results and works well in simulation and prediction. The paper considers the nonlinear and interactive effect of input variables in the system's behavior and proposes an improved grey multivariable model, which fills the gap in previous studies.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-10-11
  DOI: 10.1108/GS-02-2023-0015
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Use of AHP and grey fixed weight clustering to assess the maturity level
of strategic communication management in Brazilian startups
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  Authors: Thiago Rodrigues Timóteo, Gustavo Tietz Cazeri, Gustavo Hermínio Salati Marcondes de Moraes, Tiago F.A.C. Sigahi, Lucas Gabriel Zanon, Izabela Simon Rampasso, Rosley Anholon
  Abstract: The aim of this research was to evaluate the maturity level of strategic communication management implemented by Brazilian startups. This study employed the analytic hierarchy process (AHP), survey and Grey Fixed Weight Clustering modeling techniques. Three experts with extensive academic and practical experience in the subject participated in the AHP process, providing their opinions on the relative importance of eight variables associated with the topic under investigation, thus enabling their prioritization. Concurrently, data were collected through a survey from 23 respondents who have extensive knowledge about the realities of Brazilian startups. The weights derived from the AHP and the survey data were utilized in the Grey Fixed Weight Clustering modeling. Based on the opinions of the 23 respondents, the level of implementation of practices related to strategic management, brand management, external image management and internal communication management is superficial. In addition, according to the majority of experts, Brazilian startups exhibited a medium level of maturity to address the key challenges related to communication management. Furthermore, this study reveals that the variables “financial resources allocation,” “stakeholder relationship” and “brand management” were deemed the most significant for the model. The contributions presented herein can be beneficial for both researchers and startup managers seeking to enhance communication strategies in their organizations. This research also contributes by highlighting how grey systems theory can be extremely useful for conducting decision-making analyses in the context of startups, which is characterized by uncertainty and imprecise information.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-10-11
  DOI: 10.1108/GS-06-2023-0052
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Multi-stage skewed grey cloud clustering model and its application
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  Authors: Jie Yang, Manman Zhang, Linjian Shangguan, Jinfa Shi
  Abstract: The possibility function-based grey clustering model has evolved into a complete approach for dealing with uncertainty evaluation problems. Existing models still have problems with the choice dilemma of the maximum criteria and instances when the possibility function may not accurately capture the data's randomness. This study aims to propose a multi-stage skewed grey cloud clustering model that blends grey and randomness to overcome these problems. First, the skewed grey cloud possibility (SGCP) function is defined, and its digital characteristics demonstrate that a normal cloud is a particular instance of a skewed cloud. Second, the border of the decision paradox of the maximum criterion is established. Third, using the skewed grey cloud kernel weight (SGCKW) transformation as a tool, the multi-stage skewed grey cloud clustering coefficient (SGCCC) vector is calculated and research items are clustered according to this multi-stage SGCCC vector with overall features. Finally, the multi-stage skewed grey cloud clustering model's solution steps are then provided. The results of applying the model to the assessment of college students' capacity for innovation and entrepreneurship revealed that, in comparison to the traditional grey clustering model and the two-stage grey cloud clustering evaluation model, the proposed model's clustering results have higher identification and stability, which partially resolves the decision paradox of the maximum criterion. Compared with current models, the proposed model in this study can dynamically depict the clustering process through multi-stage clustering, ensuring the stability and integrity of the clustering results and advancing grey system theory.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-10-06
  DOI: 10.1108/GS-05-2023-0043
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Grey clustering and grey ranking of bank branches based on grey
efficiency
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  Authors: Tooraj Karimi, Mohamad Ahmadian
  Abstract: Competition in the banking sector is more complex than in the past, and survival has become more difficult than before. The purpose of this paper is to propose a grey methodology for evaluating, clustering and ranking the performance of bank branches with imprecise and uncertain data in order to determine the relative status of each branch. In this study, the two-stage data envelopment analysis model with grey data is applied to assess the efficiency of bank branches in terms of operations. The result of grey two-stage data envelopment analysis model is a grey number as efficiency value of each branch. In the following, the branches are classified into three grey categories of performance by grey clustering method, and the complete grey ranking of branches are performed using “minimax regret-based approach” and “whitening value rating”. The results show that after grey clustering of 22 branches based on grey efficiency value obtained from the grey two-stage DEA model, 6 branches are assigned to “excellent” class, 4 branches to “good” class and 12 branches to “poor” class. Moreover, the results of MRA and whitening value rating models are integrated, and a complete ranking of 22 branches are presented. Grey clustering of branches based on grey efficiency value can facilitate planning and policy-making for branches so that there is no need to plan separately for each branch. The grey ranking helps the branches find their current position compared to other branches, and the results can be a dashboard to find the best practices for benchmarking. Compared with traditional DEA methods which use deterministic data and consider decision-making units as black boxes, in this research, a grey two-stage DEA model is proposed to evaluate the efficiency of bank branches. Furthermore, grey clustering and grey ranking of efficiency values are used as a novel solution for improving the accuracy of grey two-stage DEA results.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-09-15
  DOI: 10.1108/GS-04-2023-0034
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Forecast combination using grey prediction with fuzzy integral and
time-varying weighting in tourism
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  Authors: Yi-Chung Hu
  Abstract: Tourism demand forecasting is vital for the airline industry and tourism sector. Combination forecasting has the advantage of fusing several forecasts to reduce the risk of inappropriate model selection for analyzing decisions. This paper investigated the effects of a time-varying weighting strategy on the performance of linear and nonlinear forecast combinations in the context of tourism. This study used grey prediction models, which did not require that the available data satisfy statistical assumptions, to generate forecasts. A quality-control technique was applied to determine when to change the combination weights to generate combined forecasts by using linear and nonlinear methods. The empirical results showed that except for when the Choquet fuzzy integral was used, forecast combination with time-varying weights did not significantly outperform that with fixed weights. The Choquet integral with time-varying weights significantly outperformed that with fixed weights for all model combinations, and had a superior forecasting accuracy to those of other combination methods. The tourism sector can benefit from the use of the Choquet integral with time-varying weights, by using it to formulate suitable strategies for tourist destinations. Combining forecasts with time-varying weights may improve the accuracy of the predictions. This study investigated incorporating a time-varying weighting strategy into combination forecasting by using CUSUM. The results verified the effectiveness of the time-varying Choquet integral for tourism forecast combination.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-08-15
  DOI: 10.1108/GS-04-2023-0037
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Applying claim reduction criteria in selecting efficient contractors with
the two-step grey data envelopment analysis approach
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  Authors: Hamid Asnaashari, Abbas Sheikh Aboumasoudi, Mohammad Reza Mozaffari, Mohammad Reza Feylizadeh
  Abstract: The application of correct contractor selection strategies leads to the selection of a qualified contractor and, as a result, the on-time delivery of the project with the desired quality and within the predetermined budgetary constraints. For this reason, evaluating and qualifying contractors before reviewing the proposed prices has been considered an important issue. One factor that disrupts the project completion process and the failure to achieve pre-planned goals effectively is the occurrence of contractors' disputes and claims in projects. To this end, the present study explores claim-reduction strategies for selecting effective contractors in an uncertain environment to reduce possible problems. The two-step grey data envelopment analysis (GDEA) approach was used to measure efficiency as a powerful tool in selecting efficient contractors during tenders. This approach can extend the applications of multi-criteria decision-making (MCDM) models. In other words, given some uncertainties, the unavailability of some data, and the problems with the DEA model, the two-step GDEA model was used to rank the contractors. The data confirmed the satisfactory outcomes from the selected model. The preliminary assessment of contractors is a pre-tendering process and a step in categorizing contractors, excluding contractors lacking required qualifications, and selecting efficient contractors. At first, it will help the employer to exclude inexperienced and unqualified contractors, save resources and time, reduce threats, replace opportunities with threats, and reduce material and non-material costs during the completion of the project until the projects are put into operation. Consequently, this approach reduces claims to a minimum level and increases the organization's effective material and non-material profit. Oil and gas plans and projects have a significant, sensitive, and decisive role in the economic, social, political, cultural, infrastructural, and all-round development of Iran; This is while most of the financial resources needed to implement the development and programs across the country come from oil revenues. Studies have indicated that despite the importance of these plans and projects, many of them are not completed successfully, and this causes irreparable losses to the country's economy and development in various fields. The findings of this study can be used by organizations to select more effective contractors to assign projects and plans to them.The preliminary assessment of contractors is a pre-tendering process and a step in categorizing contractors, excluding contractors who lack required qualifications, and finally selecting efficient contractors.At first, it will help the employer to exclude inexperienced and unqualified contractors, save resources and time, reduce threats, replace opportunities with threats, and reduce material and non-material costs during the completion of the project until the projects are put into operation.This approach also gives credit to the employer during the execution period and contributes to assessing unqualified contractors and reducing the temptation to hand over the project to an unqualified contractor but with a lower bid price.Consequently, this approach reduces claims to a minimum level and increases the effective material and non-material profit of the organization.Moreover, it provides an extra-organizational evaluation for contractors, motivating them to upgrade their capabilities and optimally allocate material and non-material resources, especially human resources.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-08-04
  DOI: 10.1108/GS-03-2023-0027
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
A grey ordinal priority approach for healthcare waste disposal location
selection
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  Authors: Santonab Chakraborty, Rakesh D. Raut, T.M. Rofin, Shankar Chakraborty
  Abstract: Increasing public consciousness and demand for sustainable environment make selection of a safe location for effective disposal of healthcare waste (HCW) a challenging issue. This problem becomes more complicated due to involvement of multiple decision makers having varying knowledge and interest, conflicting quantitative and qualitative evaluation criteria, and presence of several alternative locations. To efficiently resolve the problem, the past researchers have already coupled different multi-criteria decision-making tools with uncertainty models and criteria weight measurement techniques, which are time-consuming and highly computationally complex. Based on involvement of a group of experts expressing their opinions with respect to relative importance of criteria and performance of alternative locations against each criterion, this paper proposes application of ordinal priority approach (OPA) integrated with grey numbers to solve an HCW disposal location selection problem. The grey OPA can simultaneously estimate weights of the experts, criteria and locations relieving the decision makers from complicated computational steps. The potentiality of grey OPA in solving an HCW disposal location selection problem is demonstrated here using an illustrative example consisting of three experts, six criteria and four alternative locations. The derived results show that it can be employed to deal with real-time HCW disposal location selection problems in uncertain environment providing acceptable and robust decisions. It relieves the experts from pair-wise comparisons of criteria, normalization of data, identification of ideal and anti-ideal solutions, aggregation of information and so on, while arriving at the most consistent decision with minimum computational effort.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-07-31
  DOI: 10.1108/GS-05-2023-0040
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Evaluation analysis and promotion paths of regional green innovation
vitality in China
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  Authors: Wenhao Zhou, Hailin Li, Liping Zhang, Huimin Tian, Meng Fu
  Abstract: The purpose of this work is to construct a grey entropy comprehensive evaluation model to measure the regional green innovation vitality (GIV) of 31 provinces in China. The traditional grey relational proximity and grey relational similarity degree are integrated into the novel comprehensive grey evaluation framework. The evaluation system of regional green innovation vitality is constructed from three dimensions: economic development vitality, innovative transformation power and environmental protection efficacy. The weights of each indicator are obtained by the entropy weight method. The GIV of 31 provinces in China is measured based on provincial panel data from 2016 to 2020. The ward clustering and K-nearest-neighbor (KNN) algorithms are utilized to explore the regional green innovation discrepancies and promotion paths. The novel grey evaluation method exhibits stronger ability to capture intrinsic patterns compared with two separate traditional grey relational models. Green innovation vitality shows obvious regional discrepancies. The Matthew effect of China's regional GIV is obvious, showing a basic trend of strong in the eastern but weak in the western areas. The comprehensive innovation vitality of economically developed provinces exhibits steady increasing trend year by year, while the innovation vitality of less developed regions shows an overall steady state of no fluctuation. The grey entropy comprehensive relational model in this study is applied for the measurement and evaluation of regional GIV, which improves the one-sidedness of traditional grey relational analysis on the proximity or similarity among sequences. In addition, a three-dimensional evaluation system of regional GIV is constructed, which provides the practical guidance for the research of regional development strategic planning as well as promotion paths. A comprehensive grey entropy relational model based on traditional grey incidence analysis (GIA) in terms of proximity and similarity is proposed. The three-dimensional evaluation system of China's regional GIV is constructed, which provides a new research perspective for regional innovation evaluation and expands the application scope of grey system theory.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-07-18
  DOI: 10.1108/GS-02-2023-0008
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
The modified model for hyperspectral estimation of soil organic matter
using positive and inverse grey relational degree
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  Authors: Guozhi Xu, Xican Li, Hong Che
  Abstract: In order to improve the estimation accuracy of soil organic matter, this paper aims to establish a modified model for hyperspectral estimation of soil organic matter content based on the positive and inverse grey relational degrees. Based on 82 soil sample data collected in Daiyue District, Tai'an City, Shandong Province, firstly, the spectral data of soil samples are transformed by the first order differential and logarithmic reciprocal first order differential and so on, the correlation coefficients between the transformed spectral data and soil organic matter content are calculated, and the estimation factors are selected according to the principle of maximum correlation. Secondly, the positive and inverse grey relational degree model is used to identify the samples to be identified, and the initial estimated values of the organic matter content are obtained. Finally, based on the difference information between the samples to be identified and their corresponding known patterns, a modified model for the initial estimation of soil organic matter content is established, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient. The results show that the methods of logarithmic reciprocal first order differential and the first-order differential of the square root for transforming the original spectral data are more effective, which could significantly improve the correlation between soil organic matter content and spectral data. The modified model for hyperspectral estimation of soil organic matter has high estimation accuracy, the average relative error (MRE) of 11 test samples is 4.091%, and the determination coefficient (R2) is 0.936. The estimation precision is higher than that of linear regression model, BP neural network and support vector machine model. The application examples show that the modified model for hyperspectral estimation of soil organic matter content based on positive and inverse grey relational degree proposed in this article is feasible and effective. The model in this paper has clear mathematical and physics meaning, simple calculation and easy programming. The model not only fully excavates and utilizes the internal information of known pattern samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation of soil organic matter. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on. The paper succeeds in realizing both a modified model for hyperspectral estimation of soil organic matter based on the positive and inverse grey relational degrees and effectively dealing with the randomness and grey uncertainty in spectral estimation.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-07-14
  DOI: 10.1108/GS-05-2023-0041
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Research on the properties of separable binary functions
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  Authors: Zhi Cheng Jiang, Yong Wei
  Abstract: According to the fact that the single function transformation which can both reduce the class ratio dispersion and keep the relative error no enlargement after the inverse transformation does not exist, this paper provides the separable binary function transformation F(x(k),k)=f(x(k))⋅g(k). The authors select the appropriate f(x(k)) and g(k) to get F(x(k),k)=f(x(k))⋅g(k). The sequence {F(x(k),k)}k=1n can not only improve the modeling accuracy but also ensure that the inverse transformation relative error has no enlargement. First of all, to meet that the sequence reduces the class ratio dispersion after binary function transformation, the sufficient and necessary condition of binary function transformation with reduced class ratio dispersion is obtained. Secondly, to meet the condition that the inverse transformation relative error is not enlarged, the necessary condition of separable binary function transformation is obtained respectively for monotonically increasing and monotonically decreasing function f(x). Finally, the feasibility and correctness of this method are illustrated by example analysis and application. The sufficient and necessary condition of binary function transformation with reduced class ratio dispersion and the necessary condition of separable binary function transformation with the inverse transformation relative error no enlargement. According to the properties of separable binary function transformation provided in this paper, the grey prediction function model is established, which can improve the modeling accuracy. This paper provides a binary function transformation, and researches the sufficient and necessary condition of binary function transformation with reduced class ratio dispersion and the necessary condition of separable binary function transformation with the inverse transformation relative error no enlargement. It is easy for scholars to carry out the pretest before selecting the separable binary function transformation. The binary function transformation is the further extension of single function transformation, which broadens and enriches the choice of function transformation.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-07-14
  DOI: 10.1108/GS-11-2022-0109
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Single machine scheduling with interval grey processing time
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  Authors: Naiming Xie, Yuquan Wang
  Abstract: This paper aims to investigate the grey scheduling, which is the combination of grey system theory and scheduling problems with uncertain processing time. Based on the interval grey number and its related definitions, properties, and theorems, the single machine scheduling with uncertain processing time and its general forms are studied as the research object. Then several single machine scheduling models are reconstructed, and an actual production case is developed to illustrate the rationality of the research. In this paper, the authors first summarize the definitions and properties related to interval grey numbers, especially the transitivity of the partial order of interval grey numbers, and give an example to illustrate that the transitivity has a positive effect on the computational time complexity of multiple interval grey number comparisons. Second, the authors redefine the general form of the single machine scheduling problem with uncertain processing time according to the definitions and theorems of interval grey numbers. The authors then reconstruct three single machine scheduling models with uncertain processing time, give the corresponding heuristic algorithms based on the interval grey numbers and prove them. Finally, the authors develop a case study based on the engine test shop of K Company, the results show that the proposed single machine scheduling models and algorithms with uncertain processing time can provide effective guidance for actual production in an uncertain environment. The main findings of this paper are as follows: (1) summarize the definitions and theorems related to interval grey numbers and prove the transitivity of the partial order of interval grey numbers; (2) define the general form of the single machine scheduling problem with interval grey processing time; (3) reconstruct three single machine scheduling models with uncertain processing time and give the corresponding heuristic algorithms; (4) develop a case study to illustrate the rationality of the research. In the further research, the authors will continue to summarize more advanced general forms of grey scheduling, improve the theory of grey scheduling and prove it, and further explore the application of grey scheduling in the real world. In general, grey scheduling needs to be further combined with grey system theory to form a complete theoretical system. It is a fundamental work to define the general form of single machine scheduling with uncertain processing time used the interval grey number. However, it can be seen as an important theoretical basis for the grey scheduling, and it is also beneficial to expand the application of grey system theory in real world.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-07-12
  DOI: 10.1108/GS-03-2023-0030
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Evaluation of rural tourism development level based on entropy-weighted
grey correlation analysis: the case of Jiangxi Province
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  Authors: Xiaoyan Yan, Min Luo, Changbiao Zhong
  Abstract: The purpose of this paper is to establish a more reasonable evaluation system and model for the development level of rural tourism, and provides a method for quantifying the development level of regional rural tourism. This paper provides a method for evaluating the development level of rural tourism, constructs an evaluation index system according to the connotation of rural tourism, then calculates the index weight by entropy method, and makes a comprehensive evaluation by grey relational analysis. Taking the development of rural tourism in 11 cities in Jiangxi Province as the research object, the ranking results of 11 cities were obtained by using grey relational analysis. The overall development level of rural tourism in Jiangxi Province is positive, but the spatial distribution is uneven, showing the characteristics of “low-level aggregation and high-level dispersion”. The barrier model diagnoses that the degree of financial input is the biggest constraint to the development level of rural tourism in Jiangxi Province. This study puts forward an evaluation model based on entropy weight and grey relational analysis, which is an important supplement to the grey relational analysis method system and has a positive role in promoting the quantitative evaluation of regional rural tourism level.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-07-11
  DOI: 10.1108/GS-03-2023-0019
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Application of a novel hybrid accumulation grey model to forecast total
energy consumption of Southwest Provinces in China
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  Authors: Xuemei Zhao, Xin Ma, Yubin Cai, Hong Yuan, Yanqiao Deng
  Abstract: Considering the small sample size and non-linear characteristics of historical energy consumption data from certain provinces in Southwest China, the authors propose a hybrid accumulation operator and a hybrid accumulation grey univariate model as a more accurate and reliable methodology for forecasting energy consumption. This method can provide valuable decision-making support for policy makers involved in energy management and planning. The hybrid accumulation operator is proposed by linearly combining the fractional-order accumulation operator and the new information priority accumulation. The new operator is then used to build a new grey system model, named the hybrid accumulation grey model (HAGM). An optimization algorithm based on the JAYA optimizer is then designed to solve the non-linear parameters θ, r, and γ of the proposed model. Four different types of curves are used to verify the prediction performance of the model for data series with completely different trends. Finally, the prediction performance of the model is applied to forecast the total energy consumption of Southwest Provinces in China using the real world data sets from 2010 to 2020. The proposed HAGM is a general formulation of existing grey system models, including the fractional-order accumulation and new information priority accumulation. Results from the validation cases and real-world cases on forecasting the total energy consumption of Southwest Provinces in China illustrate that the proposed model outperforms the other seven models based on different modelling methods. The HAGM is used to forecast the total energy consumption of the Southwest Provinces of China from 2010 to 2020. The results indicate that the HAGM with HA has higher prediction accuracy and broader applicability than the seven comparative models, demonstrating its potential for use in the energy field. The HAGM(1,1) is used to predict energy consumption of Southwest Provinces in China with the raw data from 2010 to 2020. The HAGM(1,1) with HA has higher prediction accuracy and wider applicability compared with some existing models, implying its high potential to be used in energy field. Theoretically, this paper presents, for the first time, a hybrid accumulation grey univariate model based on a new hybrid accumulation operator. In terms of application, this work provides a new method for accurate forecasting of the total energy consumption for southwest provinces in China.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-06-06
  DOI: 10.1108/GS-02-2023-0013
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Two-stage grey cloud clustering model under the panel data and its
application
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  Authors: Dang Luo, Nana Zhai
  Abstract: The purpose of this paper is to establish a two-stage grey cloud clustering model under the panel data for the multi-attribute clustering problem with three-parameter interval grey number to evaluation of agricultural drought resistance grade of 18 cities in Henan Province. The clustering process is divided into two stages. In the first stage: Combining variance and time degree, the time weight optimization model is established. Applying the prospect theory, the index weight optimization model is established. Then, with the help of grey possibility function, the first stage of grey cloud clustering evaluation is carried out. In the second stage: the weight vector group of kernel clustering is constructed, and the grey class of the object is determined. A two-stage grey cloud clustering model under the panel data for the multi-attribute clustering problem is proposed. This paper indicates that 18 cities in Henan Province are divided into four categories. The drought capacity in Henan province is high in the east and low in the west, high in the south and low in the north and the central region is relatively stable. The drought is greatly affected by natural factors. And the rationality and validity of this model is illustrated by comparing with other methods. This paper provides a practical method for drought resistance assessment, and provides theoretical support for farmers to grasp the drought information timely and improve the drought resistance ability. The model in this paper solves the situation of ambiguity and randomness to some extent with the help of grey cloud possibility function. Moreover, the time weight of time degree and variance are used to reduce the volatility and the degree of subjective empowerment. Considering the risk attitude of the decision makers, the prospect theory is applied to make the index weight more objective. The rationality and validity of the model are illustrated by taking 18 cities in Henan Province as examples.
  Citation: Grey Systems: Theory and Application
  PubDate: 2023-06-02
  DOI: 10.1108/GS-03-2023-0021
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)