For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ESM5079 | Service Strategy | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course deals with the fundamental concepts of service strategy and their application to real world problems. Topics include service vision, service strategy development, service strategy deployment, and strategy validity assessment. The strategic issues related to global service organizations are investigated in depth using various case studies. | |||||||||
| ESM5080 | Ergonomics | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| The advanced ergonomics class focuses on the physics and physiology of humans at work; models for lifting, manual materials handling, muscle strength and EMG, and physical work capacity; special emphasis is given to learning about measurement systems and instrumentation. | |||||||||
| ESM5081 | Mechanics of the Musculoskeletal System | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| The Mechanics of the Musculoskeletal System class focuses on the upper limb and its musculoskeletal components, including mechanical properties and models; work-related musculoskeletal injuries, techniques, models, and instruments to measure and quantify the risks for developing such injuries. | |||||||||
| ESM5082 | Work Physiology | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| The course focuses on the muscle, muscle contraction, skeletal system, physical performance, body dimension, fatigue and physical activity. | |||||||||
| ESM5083 | Advanced Universal Design | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course will cover the theory and applications of universal design to understand the basic concept of accessibility and requirements by various types of disability. By examining diverse aspects of universal access, students will gain more understanding in the inclusive and universal design. The course also covers national policy on the disability and aging. Students will also learn international trends and standards related with universal design and disaiblity. | |||||||||
| ESM5084 | High-tech. Business Planning | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This class aims at planning a business with promising technologies. Students are required to understand the concept and working principles of technology, calculate the possibility of commercialization and product launching, build a proper business model, make a plan of resourcing in a systematic manner. A variety of preliminary knowledge needs, and will be made substantial by using a practical framework given in this class. | |||||||||
| ESM5085 | Quantitative Market Analysis | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This class provides students with 3-sided market view based on quantitative measures and analytic techniques. The 1st side focused on identifying the pattern of market movement with piled data. Then, the current market can be analyzed through the 2nd-side lens. Finally, the 3rd side aims at forecasting the future of market. A variety of quantitative methods from questionnaire design to conjoint analysis will be dealt with. | |||||||||
| ESM5087 | Advanced Topics in Value Engineering | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| The systematic approach of value engineering, is a plan which provides a positive course of action to remove the unwanted and unnecessary. The main topic of discussion the principles of value engineering, meaning and analysis of functions, role of management in value engineering, value engineering techniques, organizing and staffing for value engineering etc. | |||||||||
| ESM5091 | Advanced Topics in Risk Management Engineering | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course will provide the students with thorough knowledge of how to perform Risk Management and the rationale behind Risk Management, Risk Assessment and Risk Analysis and procedures of how to perform Risk Management. At the end of this course the students will be able to apply the fundamentals of risk management to both predict threats and prevent accidents. | |||||||||
| ESM5095 | Multivariate Statistical Analysis and Applications | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| We aim to learn how to drive decision making based on scientific evidences induced by multivariate analysis. This course will cover the techniques such as linear statistical model, factor analysis, principle component analysis, multidimensional scaling, canonical correlation, conjoint analysis, etc. Business-related data will be analyzed using a statistical software such as SAS, SPSS or R. | |||||||||
| ESM5096 | Applied Artificial Intelligence | 3 | 6 | Major | Master/Doctor | 1-4 | Korean | Yes | |
| This course is designed to provide students the capability of optimization for complex problems (NP problems) using Artificial Intelligence algorithms, which can not be solved traditional optimization approaches. Various Computational Intelligence (CI) algorithms are theoretically covered and applied for various cases to obtain the optimal solutions or heuristic solutions. Fuzzy, Neural Network, Swarm Intelligence, Genetic Algorithm, Genetic Programming, and other new algorithms are included and evaluated their performances by computational time, solution quality, etc. | |||||||||
| ESM5098 | Advanced Topics in Supply Chain Management | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Supply Chain Management is a holistic and systematic management of an interconnected palyers involved in the provision of products and services required by the end customers in a supply chain. This course covers some recent developments research topics in the area of supply chain management, specifically those related to global supply chain management, supply chain risk management, supply chain disruption and mitigation management, sustainable suplly chain management and green supply chain management. We also consider recent research methodologies to develop mathematical optimization models on supply chain design and operations, then to learn about various algorithms to find a. optimal solution. Advanced course topics on supply chain management are carried out through a combination of lecture, discussion and student project for research proposal. | |||||||||
| ESM5099 | Robust Optimization and Dynamic Games | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Uncertainty and competition are most challenging external factors for individuals and organizations when they make decisions. This course covers various operations research techniques dealing with decision making under uncertainty and competition. First, to develop mathematical models considering unpredictable uncertainty, we consider robust optimization and several extended models such as adjustable robust optimization, globalized robust optimization and chance constraint programming. Next, we consider differential variatioinal inequality, mathematical programming with equilibrium constraints to develop mathematical models under competition. Finally, these methodologies are applied to the problems in various industries including transportation, telecommunications and electricity market through a term project. | |||||||||
| ESM5100 | Advanced Topics in Data Mining | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course will provide an in-depth understanding of statistical machine learning algorithms in data mining including neural networks, support vector machines, dimension reduction, ensemble methods, EM algorithm, model assessment, etc. Students will be expected to implement some of those algorithms using R or MATLAB so that they can perform a term project involving improvement of existing methodologies or development of new algorithms. | |||||||||
| ESM5101 | Research Seminar on Data Mining | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| In this course, we will select several state-of-the-art topics in the area of data mining and learn the process of inducing new research topics via reading research articles and having a discussion on those. Several key articles on each of the topics will be chosen, summarized, and criticized by the students. This learning process will enable the students to suggest potential research proposals for their own research. Students will also be expected to perform a term project involving improvement of existing methodologies or development of new algorithms. | |||||||||