Most of real-world decision-making processes are used to optimize problems with many objectives of conflicting. Since the betterment of some objectives requires the sacrifice of other objectives, different objectives may not be optimized simultaneously. Consequently, Pareto solution can be considered as candidates of a solution with respect to a multi-objective optimization (MOP). Such problem involves two main procedures: finding Pareto solutions and choosing one solution among them. So-called multi-objective genetic algorithms have been proved to be effective for finding many Pareto solutions. In this study, we suggest a fitness evaluation method based on the achievement level up to the target value to improve the solution search performance by the multi-objective genetic algorithm. Using numerical examples and benchmark problems, we compare the proposed method, which considers the achievement level, with conventional Pareto ranking methods. Based on the comparison, it is verified that the proposed method can generate a highly convergent and diverse solution set. Most of the existing multi-objective genetic algorithms mainly focus on finding solutions, however the ultimate aim of MOP is not to find the entire set of Pareto solutions, but to choose one solution among many obtained solutions. We further propose an interactive decision-making process based on a visualized trade-off analysis that incorporates the satisfaction of the decision maker. The findings of the study will serve as a reference to build a multi-objective decision-making support system.

Companies struggle to make their best products with high quality and service at a competitive price in global markets. However, customer needs and requirements keep changing with a variety of situations. Companies that face the changes can not stay the same and make an effort to adapt themselves to new circumstances. They would probably review the overall management system that is currently implementing to improve management efficiency. Among other things, quality might be considered to be a crucial element if they are manufacturing industries to be sustained in global markets. KSA (Korean Standards Association) is a government- affiliated organization under the Ministry of Trade, Infrastructure, and Energy. It is a Korean standards provider for quality and service industry. KSA confers national commendations for organizations, quality circles, artisans, QCEC (Quality Competitive Excellent Company), and the most honorable KNQA (Korean National Quality Award) every year. KSA established KNQA on the basis of Malcom Baldrige National Quality Award, Deming Prize, and European Quality Award. Research on quality awards shows that there are many similarities in the framework. Although KSA summarizes two factors for 13 evaluation indicators in the quality competitive excellent model of QCEC, the categorization is ambiguous to explain them according to earlier studies. We performed a deep analysis of foreign quality awards and background for KNQA and QCEC. We conducted a content analysis of KNQA and QCEC and matched evaluation items that were closely related. We proposed a quality competitiveness model with three factors, Technology, System, and Tools, summarizing 13 evaluation indicators in QCEC. Based on audit data for six years from 2012 to 2017 we carried out a confirmatory factor analysis for the proposed model by examining the model validity and fitness.

Quality management has become an pervasive philosophy in most sectors of business. Specific movements such as statistical quality control, quality circle, total quality management, and quality management system have become embedded in business organizations. Only the companies with competitive edge can survive in the competition in global market. KSA(Korean Standards Association) established in 1962 has launched all kinds of quality education, quality standard certification service for business, and KNQA(Korean National Quality Award) system. This article considers quality competitiveness excellent company award among KNQA. We performed a statistical analysis of audit data for quality competitiveness excellent company for three years, from 2015 to 2017. By using ANOVA and two sample t-tests, the average scores of 13 evaluation fields were significantly different depending on company size and type. We proposed ways to improve the current hall of fame system. We discovered that the average scores of 13 evaluation fields in the audit data according to years and hall of fame status were not significantly different. We also showed linear relationships among 13 evaluation fields by correlation analysis and obtained an estimated linear regression equation : Business Performance, which is a comprehensive index, as a dependent variable was significantly related to Customer Focus and Product Liability as regressor variables among 13 evaluation fields by regression analysis.