Soft Computing with Industrial Applications

• Main Authors: DAO, THI-KIEN, 陶氏建 (Thi-Kien Dao)
• Other Authors: PAN, TIEN-SZU and PAN, JENG-SHYANG
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/3vvvve

博士 === 國立高雄科技大學 === 電子工程系 === 107 === Industrialization is one of the priority concerns of government departments for socio-economic development. Therefore, industrial applications are paid more considerable attention from the research community. Successful applications have generated enormous economic benefits, e.g., working environment improved, heavily labor liberated, income increased. The government policy for developing economy depends on more increasingly focused on industrial development in the country. However, many real industrial applications face the challenges arising from requirements of the increase of industrial development with creative processes, robustness, and efficiency. Soft Computing (SC) is one of the promising solutions to these challenges. SC is an evolving collection of methodologies, which aims to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, close to the human mind, and low cost. SC is proving robust in delivering optimal global solutions and assisting in resolving the limitations encountered in traditional methods. The soft computing technologies like the evolutionary algorithms (EA), swarm intelligence (SI) fuzzy logic (FL), rough sets (RS), soft sets (SS), and artificial neural networks (ANN) have been applied successfully to industrial applications. This dissertation tries to bridge the gap between the theory of soft computing and industrial applications partially. The primary methodology of our research is to attempt learning how to analyze, redesign and, improve SC, e.g., technologies of EAs and SIs, for solving the particular related industrial problems. Our approach often has two parts included the algorithm and the solution as the application of the algorithm. For the first part, in the algorithm of SC, we consider the techniques like parallel computing (PC), compact computing (CP), hybrid computing (HC), multi-objective (MO), and discrete transform (DF) to enhance or improve the methodologies according to fitting what specification of the problems. For the second part is the solution for related industrial applications by applying the analyzed algorithms. The principal objects in this dissertation to be solved are the aspects of optimization such as scheduling, balancing, and topology control problems. Besides, we will discuss on advantages and disadvantages of SCs over traditional solutions, we also present the early research results of the dissertation. These results include an optimal make-span in Job shop scheduling problems, a solution for topology control scheme in Wireless Sensor Networks (WSN), a solution for the economic load dispatch problem, and a solution for the base stations (BS) optimum formation in WSN. Regarding the orientation of opportunities and challenges of industrial application development, the dissertation would be feasible for practical application in industrial social life.