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Publisher: Emerald   (Total: 335 journals)

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Showing 1 - 200 of 335 Journals sorted alphabetically
A Life in the Day     Hybrid Journal   (Followers: 9)
Academia Revista Latinoamericana de Administración     Open Access   (Followers: 2, SJR: 0.144, h-index: 4)
Accounting Auditing & Accountability J.     Hybrid Journal   (Followers: 30)
Accounting Research J.     Hybrid Journal   (Followers: 25, SJR: 0.26, h-index: 7)
Accounting, Auditing and Accountability J.     Hybrid Journal   (Followers: 19, SJR: 0.88, h-index: 40)
Advances in Accounting Education     Hybrid Journal   (Followers: 14, SJR: 0.514, h-index: 5)
Advances in Appreciative Inquiry     Hybrid Journal   (SJR: 0.124, h-index: 5)
Advances in Autism     Hybrid Journal   (Followers: 6)
Advances in Dual Diagnosis     Hybrid Journal   (Followers: 47, SJR: 0.228, h-index: 2)
Advances in Gender Research     Full-text available via subscription   (Followers: 3, SJR: 0.229, h-index: 7)
Advances in Intl. Marketing     Full-text available via subscription   (Followers: 4, SJR: 0.123, h-index: 11)
Advances in Mental Health and Intellectual Disabilities     Hybrid Journal   (Followers: 60, SJR: 0.29, h-index: 5)
Advances in Mental Health and Learning Disabilities     Hybrid Journal   (Followers: 29)
African J. of Economic and Management Studies     Hybrid Journal   (Followers: 11, SJR: 0.125, h-index: 2)
Agricultural Finance Review     Hybrid Journal  
Aircraft Engineering and Aerospace Technology     Hybrid Journal   (Followers: 175, SJR: 0.391, h-index: 18)
American J. of Business     Hybrid Journal   (Followers: 15)
Annals in Social Responsibility     Full-text available via subscription  
Anti-Corrosion Methods and Materials     Hybrid Journal   (Followers: 10, SJR: 0.215, h-index: 25)
Arts and the Market     Hybrid Journal   (Followers: 9)
Asia Pacific J. of Marketing and Logistics     Hybrid Journal   (Followers: 7, SJR: 0.244, h-index: 15)
Asia-Pacific J. of Business Administration     Hybrid Journal   (Followers: 3, SJR: 0.182, h-index: 7)
Asian Association of Open Universities J.     Open Access  
Asian Education and Development Studies     Hybrid Journal   (Followers: 5)
Asian J. on Quality     Hybrid Journal   (Followers: 1)
Asian Review of Accounting     Hybrid Journal   (Followers: 2, SJR: 0.29, h-index: 7)
Aslib J. of Information Management     Hybrid Journal   (Followers: 22, SJR: 0.65, h-index: 29)
Aslib Proceedings     Hybrid Journal   (Followers: 251)
Assembly Automation     Hybrid Journal   (Followers: 2, SJR: 0.657, h-index: 26)
Baltic J. of Management     Hybrid Journal   (Followers: 3, SJR: 0.354, h-index: 14)
Benchmarking : An Intl. J.     Hybrid Journal   (Followers: 11, SJR: 0.556, h-index: 38)
British Food J.     Hybrid Journal   (Followers: 15, SJR: 0.329, h-index: 35)
Built Environment Project and Asset Management     Hybrid Journal   (Followers: 15, SJR: 0.232, h-index: 4)
Business Process Re-engineering & Management J.     Hybrid Journal   (Followers: 8, SJR: 0.614, h-index: 42)
Business Strategy Series     Hybrid Journal   (Followers: 6, SJR: 0.201, h-index: 6)
Career Development Intl.     Hybrid Journal   (Followers: 16, SJR: 0.686, h-index: 32)
China Agricultural Economic Review     Hybrid Journal   (Followers: 1, SJR: 0.238, h-index: 10)
China Finance Review Intl.     Hybrid Journal   (Followers: 5)
Chinese Management Studies     Hybrid Journal   (Followers: 4, SJR: 0.216, h-index: 12)
Circuit World     Hybrid Journal   (Followers: 15, SJR: 0.346, h-index: 17)
Collection Building     Hybrid Journal   (Followers: 11, SJR: 0.829, h-index: 10)
COMPEL: The Intl. J. for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3, SJR: 0.269, h-index: 22)
Competitiveness Review : An Intl. Business J. incorporating J. of Global Competitiveness     Hybrid Journal   (Followers: 6)
Construction Innovation: Information, Process, Management     Hybrid Journal   (Followers: 14, SJR: 0.508, h-index: 8)
Corporate Communications An Intl. J.     Hybrid Journal   (Followers: 6, SJR: 0.703, h-index: 26)
Corporate Governance Intl. J. of Business in Society     Hybrid Journal   (Followers: 7, SJR: 0.309, h-index: 29)
Critical Perspectives on Intl. Business     Hybrid Journal   (Followers: 1, SJR: 0.32, h-index: 15)
Cross Cultural & Strategic Management     Hybrid Journal   (Followers: 8, SJR: 0.356, h-index: 13)
Development and Learning in Organizations     Hybrid Journal   (Followers: 7, SJR: 0.138, h-index: 8)
Digital Library Perspectives     Hybrid Journal   (Followers: 15)
Direct Marketing An Intl. J.     Hybrid Journal   (Followers: 6)
Disaster Prevention and Management     Hybrid Journal   (Followers: 21, SJR: 0.533, h-index: 32)
Drugs and Alcohol Today     Hybrid Journal   (Followers: 128, SJR: 0.241, h-index: 4)
Education + Training     Hybrid Journal   (Followers: 21, SJR: 0.532, h-index: 30)
Education, Business and Society : Contemporary Middle Eastern Issues     Hybrid Journal   (SJR: 0.141, h-index: 10)
Emerald Emerging Markets Case Studies     Hybrid Journal   (Followers: 1)
Employee Relations     Hybrid Journal   (Followers: 6, SJR: 0.435, h-index: 22)
Engineering Computations     Hybrid Journal   (Followers: 4, SJR: 0.387, h-index: 39)
Engineering, Construction and Architectural Management     Hybrid Journal   (Followers: 14, SJR: 0.541, h-index: 28)
Equal Opportunities Intl.     Hybrid Journal   (Followers: 3)
Equality, Diversity and Inclusion : An Intl. J.     Hybrid Journal   (Followers: 14, SJR: 0.239, h-index: 9)
EuroMed J. of Business     Hybrid Journal   (Followers: 1, SJR: 0.145, h-index: 9)
European Business Review     Hybrid Journal   (Followers: 7, SJR: 0.481, h-index: 21)
European J. of Innovation Management     Hybrid Journal   (Followers: 23, SJR: 0.596, h-index: 30)
European J. of Marketing     Hybrid Journal   (Followers: 20, SJR: 0.933, h-index: 55)
European J. of Training and Development     Hybrid Journal   (Followers: 9, SJR: 0.489, h-index: 23)
Evidence-based HRM     Hybrid Journal   (Followers: 6)
Facilities     Hybrid Journal   (Followers: 2, SJR: 0.371, h-index: 18)
Foresight     Hybrid Journal   (Followers: 7, SJR: 0.486, h-index: 20)
Gender in Management : An Intl. J.     Hybrid Journal   (Followers: 17, SJR: 0.359, h-index: 22)
Grey Systems : Theory and Application     Hybrid Journal   (Followers: 1)
Health Education     Hybrid Journal   (Followers: 3, SJR: 0.383, h-index: 17)
Higher Education, Skills and Work-based Learning     Hybrid Journal   (Followers: 44, SJR: 0.172, h-index: 4)
History of Education Review     Hybrid Journal   (Followers: 13, SJR: 0.141, h-index: 2)
Housing, Care and Support     Hybrid Journal   (Followers: 9, SJR: 0.174, h-index: 4)
Human Resource Management Intl. Digest     Hybrid Journal   (Followers: 17, SJR: 0.121, h-index: 6)
Humanomics     Hybrid Journal   (Followers: 2, SJR: 0.14, h-index: 4)
IMP J.     Hybrid Journal  
Indian Growth and Development Review     Hybrid Journal   (SJR: 0.163, h-index: 4)
Industrial and Commercial Training     Hybrid Journal   (Followers: 5, SJR: 0.217, h-index: 14)
Industrial Lubrication and Tribology     Hybrid Journal   (Followers: 6, SJR: 0.322, h-index: 19)
Industrial Management & Data Systems     Hybrid Journal   (Followers: 6, SJR: 0.63, h-index: 69)
Industrial Robot An Intl. J.     Hybrid Journal   (Followers: 3, SJR: 0.375, h-index: 32)
Info     Hybrid Journal   (Followers: 1, SJR: 0.25, h-index: 21)
Information and Computer Security     Hybrid Journal   (Followers: 22)
Information Technology & People     Hybrid Journal   (Followers: 45, SJR: 0.576, h-index: 28)
Interactive Technology and Smart Education     Hybrid Journal   (Followers: 14, SJR: 0.112, h-index: 1)
Interlending & Document Supply     Hybrid Journal   (Followers: 62, SJR: 0.48, h-index: 13)
Internet Research     Hybrid Journal   (Followers: 43, SJR: 1.746, h-index: 57)
Intl. J. for Lesson and Learning Studies     Hybrid Journal   (Followers: 3)
Intl. J. for Researcher Development     Hybrid Journal   (Followers: 9)
Intl. J. of Accounting and Information Management     Hybrid Journal   (Followers: 8, SJR: 0.304, h-index: 7)
Intl. J. of Bank Marketing     Hybrid Journal   (Followers: 7, SJR: 0.515, h-index: 38)
Intl. J. of Climate Change Strategies and Management     Hybrid Journal   (Followers: 14, SJR: 0.416, h-index: 7)
Intl. J. of Clothing Science and Technology     Hybrid Journal   (Followers: 5, SJR: 0.279, h-index: 25)
Intl. J. of Commerce and Management     Hybrid Journal   (Followers: 1)
Intl. J. of Conflict Management     Hybrid Journal   (Followers: 14, SJR: 0.763, h-index: 38)
Intl. J. of Contemporary Hospitality Management     Hybrid Journal   (Followers: 12, SJR: 1.329, h-index: 35)
Intl. J. of Culture Tourism and Hospitality Research     Hybrid Journal   (Followers: 16, SJR: 0.399, h-index: 5)
Intl. J. of Development Issues     Hybrid Journal   (Followers: 10)
Intl. J. of Disaster Resilience in the Built Environment     Hybrid Journal   (Followers: 7, SJR: 0.225, h-index: 7)
Intl. J. of Educational Management     Hybrid Journal   (Followers: 5, SJR: 0.424, h-index: 32)
Intl. J. of Emergency Services     Hybrid Journal   (Followers: 4, SJR: 0.179, h-index: 1)
Intl. J. of Emerging Markets     Hybrid Journal   (Followers: 4, SJR: 0.199, h-index: 5)
Intl. J. of Energy Sector Management     Hybrid Journal   (Followers: 3, SJR: 0.25, h-index: 12)
Intl. J. of Entrepreneurial Behaviour & Research     Hybrid Journal   (Followers: 5, SJR: 0.694, h-index: 28)
Intl. J. of Event and Festival Management     Hybrid Journal   (Followers: 6, SJR: 0.32, h-index: 8)
Intl. J. of Gender and Entrepreneurship     Hybrid Journal   (Followers: 5, SJR: 0.638, h-index: 6)
Intl. J. of Health Care Quality Assurance     Hybrid Journal   (Followers: 9, SJR: 0.352, h-index: 32)
Intl. J. of Health Governance     Hybrid Journal   (Followers: 26, SJR: 0.277, h-index: 15)
Intl. J. of Housing Markets and Analysis     Hybrid Journal   (Followers: 10, SJR: 0.201, h-index: 5)
Intl. J. of Human Rights in Healthcare     Hybrid Journal   (Followers: 9, SJR: 0.13, h-index: 2)
Intl. J. of Information and Learning Technology     Hybrid Journal   (Followers: 7)
Intl. J. of Innovation Science     Hybrid Journal   (Followers: 9, SJR: 0.173, h-index: 5)
Intl. J. of Intelligent Computing and Cybernetics     Hybrid Journal   (Followers: 3, SJR: 0.258, h-index: 10)
Intl. J. of Intelligent Unmanned Systems     Hybrid Journal   (Followers: 4, SJR: 0.145, h-index: 2)
Intl. J. of Islamic and Middle Eastern Finance and Management     Hybrid Journal   (Followers: 8)
Intl. J. of Law and Management     Hybrid Journal   (Followers: 2, SJR: 0.107, h-index: 2)
Intl. J. of Law in the Built Environment     Hybrid Journal   (Followers: 4, SJR: 0.111, h-index: 2)
Intl. J. of Leadership in Public Services     Hybrid Journal   (Followers: 15)
Intl. J. of Lean Six Sigma     Hybrid Journal   (Followers: 7, SJR: 0.562, h-index: 15)
Intl. J. of Logistics Management     Hybrid Journal   (Followers: 12, SJR: 0.998, h-index: 15)
Intl. J. of Managerial Finance     Hybrid Journal   (Followers: 6, SJR: 0.212, h-index: 11)
Intl. J. of Managing Projects in Business     Hybrid Journal   (Followers: 2)
Intl. J. of Manpower     Hybrid Journal   (Followers: 2, SJR: 0.354, h-index: 37)
Intl. J. of Mentoring and Coaching in Education     Hybrid Journal   (Followers: 24)
Intl. J. of Migration, Health and Social Care     Hybrid Journal   (Followers: 10, SJR: 0.261, h-index: 5)
Intl. J. of Numerical Methods for Heat & Fluid Flow     Hybrid Journal   (Followers: 11, SJR: 0.594, h-index: 32)
Intl. J. of Operations & Production Management     Hybrid Journal   (Followers: 17, SJR: 2.198, h-index: 94)
Intl. J. of Organizational Analysis     Hybrid Journal   (Followers: 3, SJR: 0.222, h-index: 11)
Intl. J. of Pervasive Computing and Communications     Hybrid Journal   (Followers: 3, SJR: 0.165, h-index: 9)
Intl. J. of Pharmaceutical and Healthcare Marketing     Hybrid Journal   (Followers: 4, SJR: 0.304, h-index: 12)
Intl. J. of Physical Distribution & Logistics Management     Hybrid Journal   (Followers: 11, SJR: 1.694, h-index: 66)
Intl. J. of Prisoner Health     Hybrid Journal   (Followers: 10, SJR: 0.254, h-index: 10)
Intl. J. of Productivity and Performance Management     Hybrid Journal   (Followers: 6, SJR: 0.785, h-index: 31)
Intl. J. of Public Sector Management     Hybrid Journal   (Followers: 23, SJR: 0.272, h-index: 37)
Intl. J. of Quality & Reliability Management     Hybrid Journal   (Followers: 8, SJR: 0.544, h-index: 63)
Intl. J. of Quality and Service Sciences     Hybrid Journal   (Followers: 2, SJR: 0.133, h-index: 1)
Intl. J. of Retail & Distribution Management     Hybrid Journal   (Followers: 6, SJR: 0.543, h-index: 36)
Intl. J. of Service Industry Management     Hybrid Journal   (Followers: 2)
Intl. J. of Social Economics     Hybrid Journal   (Followers: 9, SJR: 0.227, h-index: 25)
Intl. J. of Sociology and Social Policy     Hybrid Journal   (Followers: 49, SJR: 0.361, h-index: 5)
Intl. J. of Sports Marketing and Sponsorship     Hybrid Journal   (Followers: 1)
Intl. J. of Structural Integrity     Hybrid Journal   (Followers: 2, SJR: 0.325, h-index: 8)
Intl. J. of Sustainability in Higher Education     Hybrid Journal   (Followers: 12, SJR: 0.616, h-index: 29)
Intl. J. of Tourism Cities     Hybrid Journal   (Followers: 2)
Intl. J. of Web Information Systems     Hybrid Journal   (Followers: 5, SJR: 0.208, h-index: 13)
Intl. J. of Wine Business Research     Hybrid Journal   (Followers: 6, SJR: 0.196, h-index: 12)
Intl. J. of Workplace Health Management     Hybrid Journal   (Followers: 11, SJR: 0.358, h-index: 8)
Intl. Marketing Review     Hybrid Journal   (Followers: 16, SJR: 1.076, h-index: 57)
J. for Multicultural Education     Hybrid Journal   (Followers: 3, SJR: 0.124, h-index: 11)
J. of Accounting & Organizational Change     Hybrid Journal   (Followers: 5, SJR: 0.346, h-index: 7)
J. of Accounting in Emerging Economies     Hybrid Journal   (Followers: 7)
J. of Adult Protection, The     Hybrid Journal   (Followers: 15, SJR: 0.291, h-index: 7)
J. of Advances in Management Research     Hybrid Journal   (Followers: 3)
J. of Aggression, Conflict and Peace Research     Hybrid Journal   (Followers: 48, SJR: 0.177, h-index: 9)
J. of Agribusiness in Developing and Emerging Economies     Hybrid Journal   (Followers: 1)
J. of Applied Accounting Research     Hybrid Journal   (Followers: 16, SJR: 0.22, h-index: 5)
J. of Applied Research in Higher Education     Hybrid Journal   (Followers: 51)
J. of Asia Business Studies     Hybrid Journal   (Followers: 3, SJR: 0.115, h-index: 1)
J. of Assistive Technologies     Hybrid Journal   (Followers: 19, SJR: 0.215, h-index: 6)
J. of Business & Industrial Marketing     Hybrid Journal   (Followers: 8, SJR: 0.664, h-index: 48)
J. of Business Strategy     Hybrid Journal   (Followers: 12, SJR: 0.381, h-index: 17)
J. of Centrum Cathedra     Open Access  
J. of Children's Services     Hybrid Journal   (Followers: 5, SJR: 0.167, h-index: 9)
J. of Chinese Economic and Foreign Trade Studies     Hybrid Journal   (Followers: 1, SJR: 0.188, h-index: 4)
J. of Chinese Entrepreneurship     Hybrid Journal   (Followers: 3)
J. of Chinese Human Resource Management     Hybrid Journal   (Followers: 7, SJR: 0.112, h-index: 3)
J. of Communication Management     Hybrid Journal   (Followers: 7, SJR: 0.735, h-index: 6)
J. of Consumer Marketing     Hybrid Journal   (Followers: 18, SJR: 0.613, h-index: 62)
J. of Corporate Real Estate     Hybrid Journal   (Followers: 3, SJR: 0.633, h-index: 5)
J. of Criminal Psychology     Hybrid Journal   (Followers: 119, SJR: 0.13, h-index: 1)
J. of Criminological Research, Policy and Practice     Hybrid Journal   (Followers: 54)
J. of Cultural Heritage Management and Sustainable Development     Hybrid Journal   (Followers: 11, SJR: 0.109, h-index: 5)
J. of Documentation     Hybrid Journal   (Followers: 183, SJR: 0.936, h-index: 50)
J. of Economic and Administrative Sciences     Hybrid Journal   (Followers: 3)
J. of Economic Studies     Hybrid Journal   (Followers: 10, SJR: 0.498, h-index: 26)
J. of Educational Administration     Hybrid Journal   (Followers: 5, SJR: 0.848, h-index: 36)
J. of Engineering, Design and Technology     Hybrid Journal   (Followers: 17, SJR: 0.173, h-index: 10)
J. of Enterprise Information Management     Hybrid Journal   (Followers: 4, SJR: 0.433, h-index: 38)
J. of Enterprising Communities People and Places in the Global Economy     Hybrid Journal   (Followers: 1, SJR: 0.212, h-index: 8)
J. of Entrepreneurship and Public Policy     Hybrid Journal   (Followers: 9)
J. of European Industrial Training     Hybrid Journal   (Followers: 2)
J. of European Real Estate Research     Hybrid Journal   (Followers: 4, SJR: 0.52, h-index: 7)
J. of Facilities Management     Hybrid Journal   (Followers: 3)
J. of Family Business Management     Hybrid Journal   (Followers: 6)
J. of Fashion Marketing and Management     Hybrid Journal   (Followers: 13, SJR: 0.529, h-index: 30)
J. of Financial Crime     Hybrid Journal   (Followers: 337, SJR: 0.158, h-index: 5)
J. of Financial Economic Policy     Hybrid Journal  
J. of Financial Management of Property and Construction     Hybrid Journal   (Followers: 8, SJR: 0.234, h-index: 1)
J. of Financial Regulation and Compliance     Hybrid Journal   (Followers: 9)
J. of Financial Reporting and Accounting     Hybrid Journal   (Followers: 12)
J. of Forensic Practice     Hybrid Journal   (Followers: 49, SJR: 0.225, h-index: 8)
J. of Global Mobility     Hybrid Journal   (Followers: 1)
J. of Global Responsibility     Hybrid Journal   (Followers: 4)
J. of Health Organisation and Management     Hybrid Journal   (Followers: 23, SJR: 0.67, h-index: 27)
J. of Historical Research in Marketing     Hybrid Journal   (Followers: 3, SJR: 0.376, h-index: 8)
J. of Hospitality and Tourism Technology     Hybrid Journal   (Followers: 4, SJR: 0.672, h-index: 10)
J. of Human Resource Costing & Accounting     Hybrid Journal   (Followers: 5)
J. of Humanitarian Logistics and Supply Chain Management     Hybrid Journal   (Followers: 13)

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Journal Cover Grey Systems : Theory and Application
  [1 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2043-9377
   Published by Emerald Homepage  [335 journals]
  • A cuckoo search optimisation-based Grey prediction model for thermal error
           compensation on CNC machine tools
    • Pages: 146 - 155
      Abstract: Grey Systems: Theory and Application, Volume 7, Issue 2, Page 146-155, August 2017.
      Purpose The purpose of this paper is to produce an intelligent technique for modelling machine tool errors caused by the thermal distortion of Computer Numerical Control (CNC) machine tools. A new metaheuristic method, the cuckoo search (CS) algorithm, based on the life of a bird family is proposed to optimize the GMC(1, N) coefficients. It is then used to predict thermal error on a small vertical milling centre based on selected sensors. Design/methodology/approach A Grey model with convolution integral GMC(1, N) is used to design a thermal prediction model. To enhance the accuracy of the proposed model, the generation coefficients of GMC(1, N) are optimized using a new metaheuristic method, called the CS algorithm. Findings The results demonstrate good agreement between the experimental and predicted thermal error. It can therefore be concluded that it is possible to optimize a Grey model using the CS algorithm, which can be used to predict the thermal error of a CNC machine tool. Originality/value An attempt has been made for the first time to apply CS algorithm for calibrating the GMC(1, N) model. The proposed CS-based Grey model has been validated and compared with particle swarm optimization (PSO) based Grey model. Simulations and comparison show that the CS algorithm outperforms PSO and can act as an alternative optmization algorithm for Grey models that can be used for thermal error compensation.
      Citation: Grey Systems: Theory and Application
      PubDate: 2017-07-26T11:12:06Z
      DOI: 10.1108/GS-08-2016-0021
       
  • A novel grey fixed weight cluster model based on interval grey numbers
    • Pages: 156 - 167
      Abstract: Grey Systems: Theory and Application, Volume 7, Issue 2, Page 156-167, August 2017.
      Purpose Nowadays, evaluation objects are becoming more and more complicated. The interval grey numbers can be used to more accurately express the evaluation objects. However, the information distribution of interval grey numbers is not balanced. The purpose of this paper is to introduce the central-point triangular whitenization weight function to solve the clustering process of this kind of numbers. Design/methodology/approach A new expression of the central-point triangular whitenization weight function is presented in this paper, in terms of the grey cluster problem based on interval grey numbers. By establishing the integral mean value function on the set of interval grey numbers, the application range of grey clustering model is extended to the interval grey number category, and, in this way, the grey fixed weight cluster model based on interval grey numbers is obtained. Findings The model is verified by a case which reveals a high distinguishability, validity and practicability. Practical implications This model can be used in many fields, such as agriculture, economy, geology and medical science, and provides a feasible method for evaluation schemes in performance evaluation, scheme selection, risk evaluation and so on. Originality/value The central-point triangular whitenization weight function is introduced. The method reflects the thought “make full use of the information” in grey system theory and further enriches the system of grey clustering theory as well as expands the application scope of the grey clustering method.
      Citation: Grey Systems: Theory and Application
      PubDate: 2017-07-26T11:11:48Z
      DOI: 10.1108/GS-10-2016-0040
       
  • Algorithm rules of interval grey numbers based on different “kernel”
           and the degree of greyness of grey numbers
    • Pages: 168 - 178
      Abstract: Grey Systems: Theory and Application, Volume 7, Issue 2, Page 168-178, August 2017.
      Purpose The purpose of this paper is to establish the algorithm rules of the interval grey numbers and propose a new ranking method of the interval grey numbers. Design/methodology/approach The definitions of “kernels” based on lower measure, upper measure or moderate measure are given according to the properties of the interval grey number problems. By means of the measurement error, the concept of the absolute degree of greyness and the relative degree of greyness corresponding to different “kernel” are given, and different simplified forms of the interval grey numbers are put forward. Findings The definitions of “kernel” and the degree of greyness in this paper not only take the upper limit, lower limit and the coverage of the interval grey numbers into account, but also avoid the inconsistency of the degree of greyness caused by the different universe of discourse. Research limitations/implications Though the method proposed in this paper has some deficiencies, such as the definition of relative degree of greyness is meaningless when the kernel of the interval grey number is 0, it provides a new idea for calculating and sorting the interval grey numbers and is conducive to the further development of the grey system theory. Originality/value The method proposed in this paper can not only distinguish interval grey numbers in different situations, but also avoid the inconsistency of the degree of greyness caused by the different universe of discourse. In this basis, the interval grey number algorithm is established and a new ranking method of interval grey numbers is given.
      Citation: Grey Systems: Theory and Application
      PubDate: 2017-07-26T11:12:00Z
      DOI: 10.1108/GS-10-2015-0073
       
  • Explanations about grey information and framework of grey system modeling
    • Pages: 179 - 193
      Abstract: Grey Systems: Theory and Application, Volume 7, Issue 2, Page 179-193, August 2017.
      Purpose The purpose of this paper is to summarize the different types of grey information, explain the mechanism of grey system modeling and reconstruct the framework of grey system theory (GST). Design/methodology/approach GST has been developed for more than three decades; however, the framework of GST is still in an evolutionary process. This manuscript first explains grey information in detail, and then summarizes a series of grey system models under limited data and poor information. Figures and general steps for different types of grey system models are provided in this paper. Findings The findings in this paper clearly differentiate between grey information and other uncertainty information. The differences between grey system models and other uncertainty models are clearly explained. In addition, general steps for different grey system models are given which demonstrate the orientation of grey system modeling. Practical implications Theoretical framework is very important for developing a new theory. This paper clarified grey information and grey system-based modeling mechanism. It is very useful to understand and explain the systematic framework of GST and it contributes undoubtedly to make GST perfect. Originality/value Grey information is explained in terms of limited data and two types of grey numbers. Accordingly, all of the grey system models were divided into limited data-based grey system models and grey number-based grey system models.
      Citation: Grey Systems: Theory and Application
      PubDate: 2017-07-26T11:12:02Z
      DOI: 10.1108/GS-05-2017-0012
       
  • Forecasting the total energy consumption in China using a new-structure
           grey system model
    • Pages: 194 - 217
      Abstract: Grey Systems: Theory and Application, Volume 7, Issue 2, Page 194-217, August 2017.
      Purpose China is by far the world’s largest energy consumer and importer. Reasonably forecasting the trend of China’s total energy consumption (CTEC) is of great significance. The purpose of this paper is to propose a new-structure grey system model (NSGM (1, 1)) to forecast CTEC. Design/methodology/approach Two matrices for computing the parameters of NSGM (1, 1) were defined and the specific calculation formula was derived. Since the NSGM (1, 1) model increases the number of its background values, which improves the smoothness effect of the background value and weakens the effects of extreme values in the raw sequence on the model’s performance; hence it has better simulation and prediction performances than traditional grey models. Finally, NSGM (1, 1) was used to forecast China’s total energy consumption during 2016-2025. The forecast showed CTEC will grow rapidly in the next ten years. Findings Therefore, in order to meet the target of keeping CTEC under control at 4.8 billion tons of standard coal in 2020, Chinese government needs to take necessary measures such as transforming the economic development pattern and enhancing the energy utilization efficiency. Originality/value A new-structure grey forecasting model, NSGM (1, 1), is proposed in this paper, which improves the smoothness and weakens the effects of extreme values and has a better structure and performance than those of other grey models. The authors successfully employ the new model to simulate and forecast CTEC. The research findings could aid Chinese government in formulating energy policies and help energy exporters make rational energy yield plans.
      Citation: Grey Systems: Theory and Application
      PubDate: 2017-07-26T11:12:05Z
      DOI: 10.1108/GS-05-2017-0011
       
  • Grey relational analysis coupled with principal component analysis for
           optimization of the cyclic parameters of a solar-driven organic Rankine
           cycle
    • Pages: 218 - 235
      Abstract: Grey Systems: Theory and Application, Volume 7, Issue 2, Page 218-235, August 2017.
      Purpose The purpose of this paper is to investigate the effect of four controllable parameters (fuel mixture, evaporation bubble point temperature, expander inlet temperature and condensation dew point temperature) of a solar-driven organic Rankine cycle (ORC) on the first-law efficiency, the exergetic efficiency, the exergy destruction and the volume flow ratio (expander outlet/expander inlet). Design/methodology/approach Nine experiments as per Taguchi’s standard L9 orthogonal array were performed on the solar-driven ORC. Subsequently, multi-response optimization was performed using grey relational and principal component analyses. Findings The results revealed that the grey relational analysis along with the principal component analysis is a simple as well as effective method for solving the multi-response optimization problem and it provides the optimal combination of the solar-driven ORC parameters. Further, the analysis of variance was also employed to identify the most significant parameter based on the percentage of contribution of each cyclic parameter. Confirmation tests were performed to check the validity of the results which revealed good agreement between predicted and experimental values of the response variables at optimum combination of the input parameters. The optimal combination of process parameters is the set with A3 (the best fuel mixture in the context of optimal performance is 0.9 percent butane and 0.1 percent pentane by weight), B2 (evaporation bubble point temperature=358 K), C1 (condensation dew point temperature=300 K) and D3 (expander inlet temperature=370 K). Research limitations/implications In this research, the Taguchi-based grey relational analysis coupled with the principal components analysis has been successfully carried out, whereas for any optimized solution, it is required to have a real-time scenario that may be taken into consideration by the application of different soft computing techniques like genetic algorithm, simulated annealing, etc. The results generated are purely based on theoretical modeling, and, for further research, experimental analyses are required to consolidate the generated results. Originality/value This piece of research work will be helpful to users of solar energy, academicians, researchers and other concerned persons, in understanding the importance, severity and benefits obtained by the application, implementation and optimization of the cyclic parameters of the solar-driven ORC.
      Citation: Grey Systems: Theory and Application
      PubDate: 2017-07-26T11:11:46Z
      DOI: 10.1108/GS-03-2017-0006
       
  • Multivariate grey gradient incidence model and its application
    • Pages: 236 - 246
      Abstract: Grey Systems: Theory and Application, Volume 7, Issue 2, Page 236-246, August 2017.
      Purpose The purpose of this paper is to overcome the shortcomings of existing multivariate grey incidence models that cannot analyze the similarity of behavior matrixes. Design/methodology/approach First, the feasibility of using gradient to measure the similarity of continuous functions is analyzed theoretically and intuitively. Then, a grey incidence degree is constructed for multivariable continuous functions. The model employs the gradient to measure the local similarity, as incidence coefficient function, of two functions, and combines local similarity into global similarity, as grey incidence degree by double integral. Third, the gradient incidence degree model for behavior matrix is proposed by discretizing the continuous models. Furthermore, the properties and satisfaction of grey incidence atom of the proposed model are research, respectively. Finally, a financial case is studied to examine the validity of the model. Findings The proposed model satisfies properties of invariance under mean value transformation, multiple transformation and linear transformation, which proves it is a model constructed from similarity perspective. Meanwhile, the case study shows that proposed model performs effectively. Practical implications The method proposed in the paper could be used in financial multivariable time series clustering, personalized recommendation in e-commerce, etc., when the behavior matrixes need to be analyzed from trend similarity perspective. Originality/value It will promote the accuracy of multivariate grey incidence model.
      Citation: Grey Systems: Theory and Application
      PubDate: 2017-07-26T11:11:56Z
      DOI: 10.1108/GS-04-2017-0007
       
  • Grey target decision-making model of interval grey number based on cone
           volume
    • Pages: 247 - 258
      Abstract: Grey Systems: Theory and Application, Volume 7, Issue 2, Page 247-258, August 2017.
      Purpose The purpose of this paper is to propose a grey target decision model based on cobweb area in order to overcome the effect and influence from the extreme value of the index on the decision result. However, it does not take into account the impact of the correlation between indicators on the angle of the index, and produce a certain degree decision information distortion as a result of the equal angle between the indicators. In order to solve the above problems, a novel grey decision-making model based on cone volume is proposed. Design/methodology/approach In this paper, the model uses the whitening weight function to whiten the interval grey number, and the Delphi method and the maximal entropy method are exploited to integrate the weight of the index. On the basis of this, the center of the bull’s eye, the weight and the index value are constructed as the center circle, the radius, and the high cone, respectively. The scheme is selected by the volume of the cone, the decision is made according to the order relation, and the example is utilized to prove and analyze the validity of the proposed model. Findings The results show that the proposed model can well improve the traditional grey target decision-making model from the modeling object and modeling method. Practical implications The method exposed in the paper can be used to deal with the grey target decision-making problems which characteristics are multi-indexes, and the attribute values are interval grey numbers. Originality/value The paper succeeds in overcoming the disadvantages of grey target decision making based on the target center distance and the cobweb area.
      Citation: Grey Systems: Theory and Application
      PubDate: 2017-07-26T11:11:43Z
      DOI: 10.1108/GS-04-2017-0010
       
  • Grey-based model for forecasting Mauritius international tourism from
           different regions
    • Pages: 259 - 271
      Abstract: Grey Systems: Theory and Application, Volume 7, Issue 2, Page 259-271, August 2017.
      Purpose The purpose of this paper is to understand the trend and forecast the number of tourists from different regions of the world to Mauritius. Design/methodology/approach The paper adopts two grey system models, the even model GM(1,1) and the non-homogeneous discrete grey model (NDGM), to forecast the total number of international tourism to Mauritius and its structure from different regions tourist arrivals to Mauritius for the next three years. Grey system theory models were used to account for uncertainties and the dynamism of the tourism sector environment. The two models were applied as a comparison to obtain more reliable forecasting figures. Findings The results demonstrate that both of the grey system models can be successfully applied with high accuracy for Mauritian tourism prediction, and also the number of tourist arrivals to Mauritius shows a continued augmentation for the upcoming years. Practical implications Forecasting is meaningful since the Government of Mauritius, private companies or any concerned authority can adopt the forecasting methods exposed in this paper for the development of the tourism sector through managerial and economic decision making. Originality/value Mauritius is a charming travel destination. Through this paper, it can be seen that future tourism travel to Mauritius has been successfully predicted based on previous data. Moreover, it seems that the grey system theory models have not been utilised yet as forecasting tools for the tourism sector of Mauritius as opposed to other countries such as China and Taiwan.
      Citation: Grey Systems: Theory and Application
      PubDate: 2017-07-26T11:11:58Z
      DOI: 10.1108/GS-04-2017-0008
       
  • Export injury early warning of the new energy industries in China
    • Pages: 272 - 285
      Abstract: Grey Systems: Theory and Application, Volume 7, Issue 2, Page 272-285, August 2017.
      Purpose In recent years, continuous expansion of the scale of the new energy export industry in China caused a boycott of American and European countries. Export injury early warning research is an urgent task to develop the new energy industry in China. The purpose of this paper is to build an indicator system of exports injury early warning of the new energy industry in China and corresponding quantitative early warning models. Design/methodology/approach In consideration of the actual condition of the new energy industry in China, this paper establishes an indicator system according to four aspects: export price, export quantity, impact on domestic industry and impact on macro economy. Based on the actual data of new energy industry and its five sub-industries (solar, wind, nuclear power, smart grid and biomass) in China from 2003 to 2013, GM (1,1) model is used to predict early warning index values for 2014-2018. Then, the principal component analysis (PCA) is used to obtain the comprehensive early warning index values for 2003-2018. The 3-sigma principle is used to divide the early warning intervals according to the comprehensive early warning index values for 2003-2018 and their standard deviation. Finally, this paper determines alarm degrees for 2003-2018. Findings Overall export condition of the new energy industry in China is a process from cold to normal in 2003-2013, and the forecast result shows that it will be normal from 2014 to 2018. The export condition of the solar energy industry experienced a warming process, tended to be normal, and the forecast result shows that it will also be normal in 2014-2018. The biomass and other new energy industries and nuclear power industry show a similar development process. Export condition of the wind energy industry is relatively unstable, and it will be partially hot in 2014-2018, according to the forecast result. As for the smart grid industry, the overall export condition of it is normal, but it is also unstable, in few years it will be partially hot or partially cold. The forecast result shows that in 2014-2018, it will maintain the normal state. In general, there is a rapid progress in the export competitiveness of the new energy industry in China in the recent decade. Practical implications Export injury early warning research of the new energy industry can help new energy companies to take appropriate measures to reduce trade losses in advance. It can also help the relevant government departments to adjust industrial policies and optimize the new energy industry structure. Originality/value This paper constructs an index system that can measure the alarm degrees of the new energy industry. By combining the GM (1,1) model and the PCA method, the problem of warning condition detection under small sample data sets is solved.
      Citation: Grey Systems: Theory and Application
      PubDate: 2017-07-26T11:11:50Z
      DOI: 10.1108/GS-02-2017-0003
       
  • Using the GM(1,1) model cluster to forecast global oil consumption
    • Pages: 286 - 296
      Abstract: Grey Systems: Theory and Application, Volume 7, Issue 2, Page 286-296, August 2017.
      Purpose The purpose of this paper is to compare GM(1,1) model, rolling GM(1,1) model and metabolism GM(1,1) model included in the GM(1,1) model cluster and use these models to forecast global oil consumption. Design/methodology/approach Simulated sequences will be generated randomly, and used to test the models included in the GM(1,1) model cluster; and these grey forecasting models are applied to forecast global oil consumption. Findings Effectiveness of these grey forecasting models is proved by random experiments, which explains the model adaptability. Global oil consumption is predicted, and it shows that global oil consumption will increase at a rather big growth rate in the next years. Originality/value The effectiveness of medium-term prediction of these grey forecasting models is analyzed by random experiments. These models are compared, and some basis for model selection is obtained.
      Citation: Grey Systems: Theory and Application
      PubDate: 2017-07-26T11:11:41Z
      DOI: 10.1108/GS-01-2017-0001
       
  • A multiple-attribute decision-making method based on the mean value of
           grey number weight optimisation and its application in supply-chain
           management
    • Pages: 297 - 307
      Abstract: Grey Systems: Theory and Application, Volume 7, Issue 2, Page 297-307, August 2017.
      Purpose With respect to the multiple-attribute decision-making problem with subjective preference for a certain attribute whose weight-value range have been given over other attributes whose weight values are unknown, a method based on the mean value of the grey number is proposed to analyse the decision-making problem. This method is used to choose a supply-chain partner under the condition that the decision makers have a preference for a certain attribute of various alternatives. The paper aims to discuss these issues. Design/methodology/approach First, the middle value of the preferred attribute’s weight-value range is supposed to be its weight value according to the content of the mean value of the grey number. Second, to reflect the decision maker’s subjective preference information, an improved optimisation model that requests the minimum deviation between the actual and expected numerical value of each attribute is constructed to assess the attributes’ weights. Third, the correlated degree and the correlation matrix, which are determined by the weight values of all attributes, are used to rank all the alternatives. Findings This paper provides a method for making a decision when decision makers have a preference for a certain attribute from an array of various alternatives, and the range of the certain attribute’s weight value is given but the weight value of the other attributes is unknown. When applied to supply-chain partner selection, this method proves feasible and effective. Practical implications This method is feasible and effective when applied to supply-chain partner selection, and can be applied to other kinds of decision-making problems. This means it has significant theoretical importance and extensive practical value. Originality/value Based on the mean value of the grey number, an optimisation model is built to determine the importance degree of each attribute, then the correlated degree of each alternative is combined to rank all the alternatives. This method can suit the decision makers’ subjective preference for a certain attribute well.
      Citation: Grey Systems: Theory and Application
      PubDate: 2017-07-26T11:11:34Z
      DOI: 10.1108/GS-09-2016-0026
       
 
 
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