Guan Wei Huang

Bio: Guan Wei Huang is an academic researcher from National Cheng Kung University. The author has contributed to research in topics: Grey relational analysis & Decision problem. The author has an hindex of 3, co-authored 3 publications receiving 737 citations.

The use of a grey-based Taguchi method for optimizing multi-response simulation problems

Abstract: Simulation modelling is a widely accepted tool in system design and analysis, particularly when the system or environment has stochastic and nonlinear behaviour. However, it does not provide a method for optimization. In general, problems contain more than one response, which are often in conflict with each other. This article proposes a grey-based Taguchi method to solve the multi-response simulation problem. The grey-based Taguchi method is based on the optimizing procedure of the Taguchi method, and adopts grey relational analysis (GRA) to transfer multi-response problems into single-response problems. A practical case study from an integrated-circuit packaging company illustrates that differences in performance of the proposed grey-based Taguchi method and other methods found in the literature were not significant. The grey-based Taguchi method thus provides a new option when solving a multi-response simulation-optimization problem.

Hybrid Particle Swarm Algorithm Based on Differential Evolution for the Research on Space-Time Conflict Optimization of Prefabricated Buildings

Abstract: : Due to the particularity of prefabricated building construction, compared with traditional building construction, it is more prone to the space-time conflict between construction work. Based on the study of space-time conflicts in construction projects and the requirements of schedule management, with the overall goal of meeting construction progress and safety management requirements, and with the objective function of minimizing the degree of space-time conflicts, a conflict optimization model is established

Evaluation of classification algorithms using mcdm and rank correlation

Abstract: Classification algorithm selection is an important issue in many disciplines. Since it normally involves more than one criterion, the task of algorithm selection can be modeled as multiple criteria decision making (MCDM) problems. Different MCDM methods evaluate classifiers from different aspects and thus they may produce divergent rankings of classifiers. The goal of this paper is to propose an approach to resolve disagreements among MCDM methods based on Spearman's rank correlation coefficient. Five MCDM methods are examined using 17 classification algorithms and 10 performance criteria over 11 public-domain binary classification datasets in the experimental study. The rankings of classifiers are quite different at first. After applying the proposed approach, the differences among MCDM rankings are largely reduced. The experimental results prove that the proposed approach can resolve conflicting MCDM rankings and reach an agreement among different MCDM methods.

Risk assessment of construction projects

Abstract: The paper presents risk assessment of construction projects The assessment is based on the multi‐attribute decision‐making methods The risk evaluation attributes are selected taking into consideration the interests and goals of the stakeholders as well as factors that have influence on the construction process efficiency and real estate value Ranking of objects and determination of their optimality are determined by applying TOPSIS grey and COPRAS‐G methods with attributes values determined at intervals A background and a description of the proposed model are provided and key findings of the analysis are presented

GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting

Abstract: The aim of this paper is to investigate the multiple attribute decision-making problems with intuitionistic fuzzy information, in which the information about attribute weights is incompletely known, and the attribute values take the form of intuitionistic fuzzy numbers. In order to get the weight vector of the attribute, we establish an optimization model based on the basic ideal of traditional grey relational analysis (GRA) method, by which the attribute weights can be determined. Then, based on the traditional GRA method, calculation steps for solving intuitionistic fuzzy multiple attribute decision-making problems with incompletely known weight information are given. The degree of grey relation between every alternative and positive-ideal solution and negative-ideal solution are calculated. Then, a relative relational degree is defined to determine the ranking order of all alternatives by calculating the degree of grey relation to both the positive-ideal solution (PIS) and negative-ideal solution (NIS) simultaneously. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.