Grey Rough Set Research Articles

Comprehensive Evaluation of the Development Level of China’s Characteristic Towns under the Perspective of an Urban–Rural Integration Development Strategy

With the advancement of urbanization and the continuous deepening of reforms in urban–rural systems, China’s urbanization process has entered a new era of integrated urban–rural integration. Currently, as a global “new green revolution” gains momentum, numerous countries are deeply integrating the concept of sustainable development into new urban planning. Against this backdrop, urban planners worldwide are committed to building green, livable, and smart cities that can meet the needs of the present generation without compromising the ability of future generations to meet their needs, thus achieving the vision of harmonious coexistence between humanity and nature. Characteristic towns, leveraging their resource advantages, play a significant role in achieving sustainable regional economic development. They serve as valuable references for China’s urban transformation and upgrading, as well as for promoting rural urbanization, and are crucial avenues for advancing China’s urban–rural integration development strategy. The evaluation of the development level of characteristic towns is a necessary step in their progress and a strong guarantee for promoting their construction and development. Therefore, effectively evaluating the social benefits of characteristic towns is paramount. This study constructs an evaluation model based on the grey rough set theory and Technique for Order Preference by Similarity to Ideal Solution of TOPSIS. Firstly, an evaluation index system for the development level of characteristic towns is established. Then, the grey relational analysis method and rough set theory are used to reduce the index attributes, while the conditional information entropy theory is introduced to determine the weights of the reduced indicators. Finally, the TOPSIS model is applied to evaluate the development level of characteristic towns. Through empirical research, eight characteristic towns in Zhejiang Province, China, were assessed and ranked, verifying the effectiveness and feasibility of the proposed model.

Grey variable dual precision rough set model and its application

PurposeThe purpose of this paper is to advance a novel grey variable dual precision rough set model for grey concept.Design/methodology/approachTo obtain the approximation of a grey object, the authors first define the concepts of grey rough membership degree and grey degree of approximation on the basic thinking logic of variable precision rough set. Based on grey rough membership degree and grey degree of approximation, the authors proposed a grey variable dual precision rough set model. It uses a clear knowledge concept to approximate a grey concept, and the output result is also a clear concept.FindingsThe result demonstrates that the proposed model may be closer to the actual decision-making situation, can effectively improve the rationality and scientificity of the approximation and reduce the risk of decision-making. It can effectively achieve the whitenization of grey objects. The model can be degenerated to traditional variable precision rough fuzzy set model, variable precision rough set model and classic Pawlak rough set, when some specific conditions are met.Practical implicationsThe method exposed in the paper can be used to solve multi-criteria decision problems with grey decision objects and provide a decision rule. It can also help us better realize knowledge discovery and attribute reduction. It can effectively achieve the whitenization of grey object.Originality/valueThis method proposed in this paper implements a rough approximation of grey decision object and obtains low-risk probabilistic decision rule. It can effectively achieve a certain degree of whitenization of some grey objects.

A decision system based on intelligent perception and decision for scene ventilation safety

There are many hidden safety hazards in mine ventilation process, which cannot be dealt with in time. It is because the type of coal mine and its mining conditions are complex and changeable, and the safety decision-making level is low when coal mine ventilation is abnormal. To solve these problems, this paper presents a decision system for scene ventilation safety based on intelligent perception and decision. First, grey correlation analysis and rough set theory are used to reduce the decision table horizontally and vertically. Then, the reduced data is input into the mine ventilation safety decision model based on the improved capsule network to make ventilation safety decision. Experimental results show that this system can significantly improve the accuracy of mine ventilation safety decision, has the characteristics of strong information perception ability and accurate decision, and provides an important guarantee for mine ventilation safety.

Product Acceptance; Service preference based on E-service Quality using G-Rough Set Theory

Nowadays, the competitive environment of e-services companies has made them deliver high quality of services. In fact, the perceived quality of e-services by consumers which determines their behaviour towards products is defined as product acceptance. Consumers have a powerful potential to pass their experiences to other consumers. In this regard, this study intends to detect the rules determining the behaviour of consumers in product acceptance based on the perceived quality of e-services. Accordingly, 407 questionnaires were distributed among online retailers' consumers and then grey rough set theory was applied to analyse them. Perceived quality dimensions and net promoter score (NPS) were determined as the condition and decision attribute, respectively. Finally, the six rules determining the behaviour of consumers were extracted through the data analysis process. Furthermore, the results showed that efficiency, system availability, fulfilment, responsiveness, and contact are the most important factors determining product acceptance.

Inventory control model based on multi-attribute material classification: An integrated grey-rough set and probabilistic neural network approach

Inventory control model based on multi-attribute material classification: An integrated grey-rough set and probabilistic neural network approach

INTEGRATING SUSTAINABILITY INTO SUPPLIER SELECTION: A GREY-BASED TOPSIS ANALYSIS

Sustainable supplier selection plays an important role in sustainable supply chain management operations and implementation. In this paper a novel formal modeling approach is conceptually developed and presented to address sustainable supplier selection. Grey theory and TOPSIS, a distance based multiple criteria method, are used for the integration and evaluation of sustainable supplier performance for sustainable supplier selection. From a research perspective, TOPSIS is improved to more effectively deal with grey numbers by integrating a degree of likelihood rather than converting grey numbers into crisp numbers functions, and it provides more flexible supplier rankings. This methodology strengthens the sustainable supplier selection process, and can be applied to other multiple criteria decision making problems. Illustrative calculations are made using data on sustainable supplier selection and evaluation published by Bai and Sarkis (2010). The technique is relatively accurate, matching well with results from a published grey rough set approach. The methodology easily implementable with minimal complex calculations required. It can also provide support for sustainable supplier selection, benchmarking, and improvement decisions. This is one of the first papers to integrate a broad set of sustainability factors for grey-based TOPSIS and supplier selection.

Selection of an evaluation index for water ecological civilizations of water-shortage cities based on the grey rough set

According to the characteristics and existing problems of water ecological civilization of water-shortage cities, the evaluation index system of water ecological civilization was established using a grey rough set. From six aspects of water resources, water security, water environment, water ecology, water culture and water management, this study established the prime frame of the evaluation system, including 28 items, and used rough set theory to undertake optimal selection of the index system. Grey correlation theory then was used for weightings in order that the integrated evaluation index system for water ecology civilization of water-shortage cities could be constituted. Xi’an City was taken as an example, for which the results showed that 20 evaluation indexes could be obtained after optimal selection of the preliminary framework of evaluation index. The most influential indices were the water-resource category index and water environment category index. The leakage rate of the public water supply pipe network, as well as the disposal, treatment and usage rate of polluted water, urban water surface area ratio, the water quality of the main rivers, and so on also are important. It was demonstrated that the evaluation index could provide an objectively reflection of regional features and key points for the development of water ecology civilization for cities with scarce water resources. It is considered that the application example has universal applicability.

New and improved: grey multi-granulation rough sets

ABSTRACTRough set has drawn great attention in recent decades, among which multi-granulation rough set (MGRS) is an arresting direction. It constructs a formal theoretical framework to solve complex problems under the circumstance of multiple binary relations. However, the fusion of multi-granulation rough set and grey system for acquiring knowledge is still a gap. Toward this end, we devise a grey multi-granulation rough set (GMGRS) by taking multiple grey relational relations into consideration under the framework of MGRS. In grey information system, the constructed grey relational relation that measures the relationship among objects can be used to further establish multiple binary relations. Based on two different approximate strategies (seeking common reserving difference and seeking common eliminating difference), two types of GMGRS are presented, respectively. After discussing several important properties of GMGRS, we discover that the properties of the proposed GMGRS are synchronous with the classical MGRS. Meanwhile, to obtain the attribute reduction under GMGRS, we reconstruct significance measure and termination criterion based on the θ-precision pessimistic GMGRS. Last but not least, theoretical studies and practical examples demonstrate that our proposed GMGRS largely enrich the MGRS theory and provide a new technique for knowledge discovery, which is practical in real-world scenarios.

Robust modelling and prediction of thermally induced positional error based on grey rough set theory and neural networks

Thermal errors adversely affect the precision of a machine tool operation. Therefore, reduction of thermal errors is essential for improving of machining accuracy. In this paper, in order to realize real-time compensation, taking a multi-axis machine tool as an example and a method for robust modeling and predicting thermally induced positional error is proposed. First of all, the number of temperature measuring points required in thermal error’s model was reduced based on the rough set theory, which greatly reduced variable searching and modeling time. Then through grey relation theory, systematic analysis of the similarity degree between thermal error and temperature data was carried out to select sensitive temperature measuring points, and the temperature variables in the thermal error’s model were reduced from 24 to 7 after optimization, which eliminated the coupling problems. For reducing the influence of unpredictable noises, radial basis function (RBF) and back propagation (BP) neural network modeling methods were adopted to predict thermally induced positional errors, and in comparison, the prediction accuracy of RBF neural network was found superior to that of traditional BP neural network. Finally, some measured data were selected to verify the validity of the proposed method, and the results showed that prediction accuracy of the proposed thermally induced positional error model was reliable.

Green Marketing: A Grey-based Rough Set Theory Analysis of Activities

As green marketing is a critical function of green supply chain management, it has generated a great deal of discussion in public discourse and academic research. Interestingly, there is a gap in the research on how a company can effectively invest and manage various green marketing activities. Thus this paper aims to introduce a grey multiple criteria decision-making methodology to investigate the relationships between green marketing activities and performance outcomes (environmental and business performance). This paper seeks to make the following contributions to the green supply chain management literature. First, review the literature to identify implementation activities for green marketing from green supply chain management literature. Second, develop a methodology to evaluate relationships of green marketing activities by integrating grey number and rough set method. Third, obtain various decision rules relating the various green marketing activities to the environmental and/or business performance outcomes through an illustrative application within an incomplete information system. Knowing those rules to help in the implementation, management, and evaluation of these green marketing activities is valuable to both researchers and practitioners. Included in the discussion are insights into how these rules for individual and aggregated performance (environmental, business, and joint performance) may be evaluated. Practical and research implications of this work are detailed in the final section of this paper.

Grey rough set evaluation approach for agile concept selection

Agile manufacturing (AM) is a powerful manufacturing paradigm enabling the production of customised products coupled with time compression in a cost effective manner. The selection of best concept design from agility view point involves several criteria and it is a typical multi criteria decision-making (MCDM) problem. In this study, integrated grey system rough set theory was applied. The computation of grey relational grades enables the selection of best agile concept design. The selected concept design (A3) of the automotive product was further subjected to subsequent phases of agile product development.

Grey dominance variable precision rough set model based on grey dominance relationship

Purpose – The purpose of this paper is to construct a novel grey dominance variable precision rough model. Design/methodology/approach – To deal with the problems that the attribute values of the decision-making object are often not exact numbers but interval grey numbers, and the decision-making attributes satisfy a certain preference relationship in the decision-making information because of the complexity and uncertainty of the real world, the authors take advantage of the theoretical thinking of the grey systems, dominance rough set theory and variable precision rough set theory, and construct a novel dominance variable precision rough set model. On the basis of the thinking logic of grey systems, the authors first define the concepts of balance degree, dominance degree and inferior degree, and then the grey dominance relationship based on the comparison of interval grey numbers. Then the authors use the grey dominance relationship to substitute for the indiscernibility relationship of the variable precision rough set so that the grey dominance variable precision rough model is naturally utilized to reduce the system's attributes in order to derive the needed decision rules. At the end, the authors use a decision-making example of the radar target selection to demonstrate the feasibility and effectiveness of the novel model. Findings – The results show that the proposed model possesses certain fault tolerance ability and can well-realize decision rule extraction and knowledge discovery out of a given incomplete information system. Practical implications – The method exposed in the paper can be used to deal with the decision-making problems with the grey information, preference information and noise data. Originality/value – The paper succeeds in realizing both the grey decision-making information with preference information and noise data and the extraction of decision-making rules.

A grey rough set model for evaluation and selection of software cost estimation methods

Purpose – Due to the limitation of acknowledgment, the complexity of software system and the interference of noises, this paper aims to solve the traditional problem: traditional software cost estimation methods face the challenge of poor and uncertain inputs. Design/methodology/approach – Under such circumstances, different cost estimation methods vary greatly on estimation accuracy and effectiveness. Therefore, it is crucial to perform evaluation and selection on estimation methods against a poor information database. This paper presents a grey rough set model by introducing grey system theory into rough set based analysis, aiming for a better choice of software cost estimation method on accuracy and effectiveness. Findings – The results are very encouraging in the sense of comparison among four machine learning techniques and thus indicate it an effective approach to evaluate software cost estimation method where insufficient information is provided. Practical implications – Based on the grey rough set model, the decision targets can be classified approximately. Furthermore, the grey of information and the limitation of cognition can be overcome during the use of the grey rough interval correlation cluster method. Originality/value – This paper proposed the grey rough set model combining grey system theory with rough set for software cost estimation method evaluation and selection.

Post-Evaluation of Electric Power Construction Project Based on Grey Rough Set

The post-evaluation of electric power construction project is an important link, which is necessary in electric grid projects cycle. An advanced method of post-evaluation is proposed by combining Rough Set (RS) theory and Grey System (GS) theory, defined as Grey Rough Set (GRS). According to post-evaluation project experiences, this paper provides the method of index screening by attribute reduction and weight decision.

Apply Grey Relational Grade And Rough Set Theory for The Factor Weighting Analysis in Liver Function

This paper is mainly a continuation of the previous paper, which is based on an assessment of liver function and verifies the results. First of all, we use the grey relational grade in grey system theory and integrate the numerical results of the examiner. And then, the influence factors of liver function toward weighting value are derived by significant in rough set theory. In addition, by using software, toolbox concept and the powerful function of software application, that is based on a relevant measuring model and weighting model to develop a toolbox and to achieve a practical setting and academic research. The paper mainly does an in-depth analysis on liver function diagnosis. In order to achieve the purpose, we first refer to the relevant regulation of Department of Health and decide the analyzed influence factors of diagnosis. In addition, we suggest five influence factors according to the past researches and the current medical analysis factors. In order to explore the nature of each factor, the expected input data is over 300 cases. Secondly, we make use of grey relational grade in grey system theory to convert a subjective judgment into an objective way of quantitative value, and take the value as the output of weighting analysis. Then, the influence factors of liver function toward system’s objective weighting are derived by means of significant model in rough set theory. During the setting period, we also develop a Matlab toolbox and support with lots of calculation and complicate mathematical calculation due to the mutual influence of objects data. In addition to complete a medical assistance platform of the objective intelligent liver function evaluation, we continuously pile up the evaluation software of medical system.

Research on Mining Classification Rules of Spare Parts Based on Grey Rough Set Theory

Mining classification rule of spare parts is very important for inventory management. In traditional classification work of spare parts, the attributes of spare parts were used as a standard to extract classification rules, but it was hard to know the influence of every attribute of spare parts, and which one should be considered, because the attributes of spare parts had many species. So it was necessary for inventory management to mine classification rules of spare parts. Because the values of many attributes of the spare parts are in form of the range of data, the grey rough set theory was borrowed to mine the classification rules in this paper. Firstly, the mining classification rules model of spare parts was built by the grey rough set theory. Then the attributes of spare parts were summarized, and the steps of mining data samples and the mining classification rules of spare parts were introduced respectively. Finally, case study from the classification management of the spare parts of a maintenance factory shows that the proposed mining classification rules model of spare parts based on grey rough set theory can reduce the unnecessary attributes of spare parts without affecting the results of classification.

A grey-based rough approximation model for interval data processing

A new rough set model for interval data named grey-rough set is proposed in this paper. Information system in the real world are quite complicated. Most of information tables record not only categorical data but also numerical data including a range of interval data. The grey lattice operation in grey system theory is one of the operations for interval data that modifies endpoints non-arithmetically, and which is useful for interval data processing. The grey-rough approximation is based on an interval coincidence relation and an interval inclusion relation instead of an equivalence relation and an indiscernibility relation in Pawlak’s model. Numerical examples and four fields of practical examples, decision-making, information retrieval, knowledge discovery and kansei engineering are shown. The advantages of the proposal include: extending a treatable value compared with classical rough set for non-deterministic information systems, providing a maximum solution and minimum solution both in upper and lower approximations, and not only providing mathematical support to SQL but also functions for further extension in the future.