Dynamic Grey Relational Analysis
Dynamic Grey Relational Analysis (DGRA) method is a generalized form of Deng’s GRA model. Unlike Deng’s GRA model, the DGRA model does not assumes the value of the Distinguishing Coefficient to be static (e.g., 0.5). In fact, in the DGRA the Distinguishing Coefficient is dynamic and it evolves as the problem evolves. The Dynamic Distinguishing Coefficient of the DGRA is a true representation of the dynamicness of a grey system, a system with partially known information that evolves as new information becomes available.
It can be used for both Multiple Criteria Decision Analysis (MCDA) and grey correlational analysis between multiple factors. It can be used for nonparametric analysis of the relationship between dependent variable and multiple independent variables. The software to apply the DGRA model will be released soon. Please keep visiting the website.
The model has been positively rated in the contemporary literature (see, e.g., Delcea and Cotfas, 2023). Recently, it has seen application in apparel industry, information and communication technologies (ICT), and e-commerce.
Grey Ordinal Priority Approach
Grey Ordinal Priority Approach (OPA-G) method is a Grey Number-based Ordinal Priority Approach to Multiple Criteria Decision Analysis. The method is suitable for decision-making under uncertainty. The method can estimate the weights of criteria, alternatives and experts simultaneously.
The pioneering work on the OPA-G has been recognized by the De Gruyter Handbook of Responsible Project Management. Recently, the OPA-G model has seen applications in Electric Vehicle, and agriculture industries.
Posterior-Variance Test (for forecasting)
Forecasting of a variable should not end at forecast error reporting. It is also important that whether the forecasting model used was qualified to forecast the given variable reliably or not. For this purpose, the posterior-variance test (also called, posterior-error test) provides a useful technique for the forecasters, especially the ones using grey forecasting models. A very detailed introduction to the PVT has recently been published here.
Recently, it has seen application in digital economy, and methane emissions forecasting.
Optimized Discrete Grey Forecasting Model, DGM (1, 1, θ)
The classical discrete grey forecasting model DGM (1,1) does not produces reliable results under some cases, e.g., when the historic data contains significant amount of non-linearity. To solve this issue DGM (1, 1, θ) was proposed. Generally, DGM (1, 1, θ), which is a generalized case of DGM (1,1), is more accurate than EGM (1, 1, α, θ).
Recently, it has seen application in energy sector and ISO/IEC 27001 Certifications analysis.
Optimized Even Grey Forecasting Model, EGM (1, 1, α, θ)
The classical grey forecasting model EGM (1,1) does not produces reliable results under some cases, e.g., when the historic data contains significant amount of non-linearity. To solve this issue EGM (1, 1, α, θ) was proposed.
Recently, it has seen applications in environmental science, finance, automobile industry, international trade (exports volume prediction; trade deficit prediction), tourism sectors of Malawi and Indonesia, and ISO/IEC 27001 Certifications analysis.
Multivariate Hybrid Grey Forecasting Model Verhulst-GM(1,N)
V-GM(1,N) is a multivariable hybrid of Grey Verhulst and the classical GM (1,N) models. The models enjoys the strengths of both models and is a suitable approach to forecasting using multiple variables.
Grey Linear Programming
Linear programing under uncertainty is no easy task. Grey Linear Programming (GLP) method is a convenient and effective method to solve linear programming problems where uncertainty can be represented through interval grey numbers.
Framework to Predict the State of an Equipment
In manufacturing industry, predicting the state of a system (i.e., a system is performing normally or abnormally) is of critical importance. Two-stage multi-level equipment grey state prediction model is an effective decision support system to deal with such problems and can effectively serve as an early warning tool. The system can also help the managers in reducing the equipment’s major failure risk and maintenance costs.
Grey Assessment (for linguistic input and output)
When the members of a group are ranked using linguistic expressions (e.g., a tecah may grade her students as @excellet’, “good”, “average,” etc.) the overall (mean) performance of the group can’t be assessed by applying traditional method of calculating mean or average of the individuall scores of its members. What is the average of “excellent” and “very bad”? Certainly, it’s not easy to answer. Even if we convert these linguistic/qualitative expressions into numeric scores and then take average what is the qualitative menaing of that mean value? For example, if “excellent” is 7, and “very bad” is 2, and mean is 4.5, then what does this 4.5 signifies? Good or very good or something else? Voskoglou’ Grey Assessment method is a very convenient and effective approach to handle such problems.