Determination of the Most Suitable Technology Transfer Strategy for Wind Turbines Using an Integrated AHP-TOPSIS Decision Model

Abstract

Abstract:The high-speed development of industrial products and goods in the world has caused "technology" to be considered as a crucial competitive advantage for most large organizations. In recent years, developing countries have considerably tended to promote their technological and innovative capabilities through importing high-tech equipment owned and operated by developed countries. There are currently a variety of solutions to transfer a particular technology from a developed country. The selection of the most profitable technology transfer strategy is a very complex decision-making problem for technology importers as it involves different technical, environmental, social, and economic aspects. In this study, a hybrid multiple-criteria decision making (MCDM) model based on the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to evaluate and prioritise various technology transfer strategies for wind turbine systems. For this purpose, a number of criteria and sub-criteria are defined from the viewpoint of wind energy investors, wind turbine manufacturers, and wind farm operators. The relative importance of criteria and sub-criteria with respect to the ultimate goal are computed using the eigenvalue method and then, the technology transfer alternatives are ranked based on their relative closeness to the ideal solution. The model is finally applied to determine the most suitable wind turbine technology transfer strategy among four options of reverse engineering, technology skills training, turn-key contracts, and technology licensing for the renewable energy sector of Iran, and the results are compared with those obtained by classical decision-making models.Keywords: technology transfer; wind turbine; design and manufacture; multiple-criteria decision making (MCDM); analytic hierarchy process (AHP); technique for order of preference by similarity to ideal solution (TOPSIS)

Efficiency Evaluation and Policy Analysis of Industrial Wastewater Control in China

“…It has great advantages in avoiding subjective factors, simplifying algorithms and reducing errors. Hence we chose it instead of other alternative assessment methods, such as Analytic Hierarchy Process (AHP) [55][56][57][58], Principal Components Analysis (PCA) [59,60], and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) [61][62][63], etc. In recent years, the DEA model has been widely used in the study of industrial wastewater control efficiency.…”

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

“…Among them, the most important theory in the grey decision model is the weighted grey target theory. Recently, the weighted grey target theory and GRA model have been extensively adopted in fault diagnosis of the transformer [59][60][61][62][63][64][65][66][185][186][187]. The two are briefly introduced as follows:…”

“…Procedures of GRA Primary Means needs less data for fault diagnosis high precision without prior information good at dealing with the small samples with some information known and some information unknown, as well as the uncertain systems with poor information first construct a comparative sequence then use GRA to calculate grey correlation between comparative sequence and reference sequence lastly determine the actual fault mode according to the calculated grey correlation weighted grey target theory [59][60][61][62][63][64][65][66][185][186][187]190] GRA model [154,188] combined with hot spot temperature [59] combined with fuzzy SVM [154] grey relation entropy [61] grey prediction-clustering analysis [63] improved grey correlation analysis model [191] combined with feedback cloud entropy model [192] combined with improved SVM, cloud entropy model, BN, ML and data mining techniques…”

“…In other words, it is essential to explore the principles, methods and means from various disciplines that are helpful to the fault diagnosis of transformers, so as to make the fault diagnosis technology interdisciplinary. Aiming at the limitations of traditional methods above, with the rapid development of computer technology and artificial intelligence (AI) theory, multiple intelligence techniques, including artificial neural network (ANN) [37][38][39][40][41][42][43][44][45][46], expert system (EPS) [47][48][49][50][51], fuzzy theory [52][53][54][55][56][57][58], rough sets theory (RST) [36], grey system theory (GST) [59][60][61][62][63][64][65][66], and other intelligent diagnosis tools [5, such as swarm intelligence (SI) algorithm, data mining technology, machine learning (ML), mathematical statistics method, wavelet analysis (WA), optimized neural network, Bayesian network (BN), and evidential reasoning approach, have been introduced to the research field of transformer fault diagnosis based on the DGA approach. These intelligent methods make up for the deficiencies of the mentioned traditional DGA methods, and directly or indirectly improve the accuracy of transformer fault diagnosis, and provide a new train of thought for high-precision transformer fault diagnosis.…”

Choosing the Production Function Model for an Optimal Measurement of the Restructuring Efficiency of the Polish Metallurgical Sector in Years 2000–2015

“…: manufacturing [35], supply chain management [36], technology transfer [37], and others. Nevertheless, we have not found any applications that aimed at qualitative verification of formerly obtained quantitative research results in the metallurgical sector.…”