| Category | Program | Course/English | Credits |
|---|---|---|---|
| Major, Required | Master’s degree | Thesis Writing | 3 |
| Major, Required | Major, Elective | Special Study of Quality Management | 3 |
| Major, Required | Major, Elective | Theory of Inspection Control | 3 |
| Major, Required | Major, Elective | Advanced Human Factors Engineering | 3 |
| Major, Required | Major, Elective | Applied Design and Analysis of Experiments | 3 |
| Major, Required | Major, Elective | Stochastic Process and its Application | 3 |
| Major, Required | Major, Elective | Reliability Engineering | 3 |
| Major, Required | Major, Elective | Advanced Decisinon Analysis | 3 |
| Major, Required | Major, Elective | Advanced Statistics | 3 |
| Major, Required | Major, Elective | Queueing Theory | 3 |
| Major, Required | Major, Elective | Forecasting Theory and Application | 3 |
| Major, Required | Major, Elective | Advanced Engineering Economy | 3 |
| Major, Required | Major, Elective | Decision Support Systems | 3 |
| Major, Required | Major, Elective | Advanced Production System Design | 3 |
| Major, Required | Major, Elective | Advanced Simulation Modelling and Application | 3 |
| Major, Required | Major, Elective | Advanced Production Automation | 3 |
| Major, Required | Major, Elective | Human Reliability | 3 |
| Major, Required | Major, Elective | Advanced Logistics Management | 3 |
| Major, Required | Major, Elective | Applied Linear Programming | 3 |
| Major, Required | Major, Elective | Applied Linear Programming | 3 |
| Major, Required | Major, Elective | Special Study of Statistics | 3 |
| Major, Required | Major, Elective | Manufacturing System Modelling | 3 |
| Major, Required | Major, Elective | System Safety Engineering | 3 |
| Major, Required | Major, Elective | Advanced Aesthetic Engineering | 3 |
| Major, Required | Major, Elective | Advanced Electronic Commerce | 3 |
| Major, Required | Major, Elective | Application of Information and Telecommunications | 3 |
| Major, Required | Major, Elective | Application of Management Information System | 3 |
| Major, Required | Major, Elective | ERP Application | 3 |
| Major, Required | Major, Elective | Supply Chain Management | 3 |
| Major, Required | Major, Elective | Advanced Biological Informatics | 3 |
| Major, Required | Major, Elective | Statistical Analysis and Evaluation | 3 |
| Major, Required | Major, Elective | Theory of Management Strategy | 3 |
| Major, Required | Major, Elective | Product Design Process and Methodology | 3 |
| Major, Required | Major, Elective | Advanced Management of Technology | 3 |
| Major, Required | Major, Elective | Multivariate Analysis | 3 |
| Prerequisite | Major, Elective | Probability and Statististics | 3 |
| Prerequisite | Major, Elective | Introduction to Industrial and Management Engineering | 3 |
In this course, students will study a wide range of management models to plan and rationally manage quality in the design of products and services so that they meet the needs of users and suitability for use.
Students will gain an understanding of sampling and inspection methods according to incoming inspection, in-process inspection, etc., design reliable and economical inspection methods, and ensure rational management across inspections.
Students will measure, analyze, evaluate, and apply human factors relationship variables for the design of man-machine systems.
As an application field of statistics, this course is designed to teach students how to plan experiments in a way that would yield the maximum amount of information for cost-effectiveness and how to determine the optimal working conditions by statistically analyzing the data obtained from the experiments.
The goal is to understand different types of stochastic processes and learn techniques to apply them to measuring and evaluating system performance.
This course is a study of mathematical models and analysis methods for reliability problems, and it covers reliability concepts, reliability functions, and system reliability measurement, prediction, and optimization.
This course is a study of rational decision-making under uncertainty, and aims to provide students with a chance to study various methodologies such as influence diagrams, subjective probability methods, and utility functions and apply them to real-world industrial problems.
The goal is to understand the exploratory data analysis method and cultivate the ability to make optimal decisions using new and advanced analytical techniques by acquiring comprehensive theoretical and practical skills through application examples.
By identifying the analysis method of queueing model, students will examine system evaluation and improvement measures.
In this course, students will study the application of various advanced techniques for forecasting medium- and long-term time series and the design of forecasting systems.
This course covers advanced-level techniques that can be used to relate the value of systems, products, and services to their costs, so that you can understand the operations and operational feasibility based on a technical background.
This course is a study of computer systems that support rational decision-making by utilizing industrial engineering and operation research techniques for various decisions. Particular attention will be paid to the group decision support system, which is currently in the spotlight.
This course discusses theories and techniques for systems for effectively planning, organizing, conducting, and controlling production activities such as production, inventory management, quality control, and the purchase and transportation of materials.
Statistical analysis of input and output data for simulation and statistical techniques and modeling such as variance reduction method and experimental design method are the basic topics in this course. Students will learn network modeling using simulation languages (SLAM, Simscript, etc.) and examples of simulation of 이사(이산)사건 discrete events.
Students will learn automation concepts and techniques related to production systems.
The goal is to understand and apply concepts and methods for analyzing and evaluating human error and reliability.
Students will learn concepts, issues, and solutions related to logistics across procurement, production, sales, distribution, consumption, disposal, and recycling
The goal is to understand and apply mathematical modeling of advanced linear planning problems to obtain optimal solutions.
Students will analyze data using statistical methods and study theories and techniques that can be used to apply the results of the analysis in the field.
Students will learn techniques related to modeling manufacturing systems as a step in the analysis to optimize the performance of manufacturing systems.
This course focuses on how to conduct qualitative and quantitative evaluations of various risk factors such as FMEA, FTA, HAZOP, PHA, FHA, MORT, Decision Tree, THERP, etc. and take countermeasures to minimize injuries and losses to humans and damage to equipment and facilities under the constraints related to the man-machine system function, time, cost, etc.
In this course, students will study the engineering techniques applied to design products or systems used by humans through the quantification of human aesthetics or emotions.
Students will learn about the planning, design, construction, and operation of e-commerce, which will drive the economy in the e-business era, and develop the ability to apply e-commerce in practice by focusing on actual cases.
Students will learn to apply various techniques of industrial and management engineering to optimize traffic-related performance in various information and communication systems and networks such as computer, Internet, and mobile communications.
Based on the basic concepts of management information systems, students are given the opportunity to develop the ability to apply or improve MIS by learning MIS architecture and operation case examples of companies and other organizations.
Students will learn the concepts and general aspects of enterprise resource planning (ERP) systems that have been actively adopted by domestic and foreign companies in recent years, and learn how to introduce and utilize ERP efficiently according to actual company characteristics by studying successful ERP adoption cases.
The aim of supply chain management is to reduce the risk of uncertainty by effectively and efficiently managing the flow of materials, etc. This is made possible by managing the flow of materials, services, and information from the supplier, through the process of change within the enterprise and through the distribution chain, to the end user using a total systems approach.
This course concerns a study of engineering techniques required to quantitatively measure and evaluate the characteristics of human sensory functions based on an understanding of biological phenomena, structures, and components from the engineering perspective.
This course covers procedures and methods for statistically processing various data, analyzing and evaluating the results, and applying them in practice. Students will thus obtain knowledge of statistical processing and analysis methods required for writing papers and reports.
This course introduces management innovation techniques (reengineering, benchmarking, SIS, DSS, etc.) and cooperation with other companies in response to rapid changes in the business environment, and cultivates practical application skills necessary for the establishment of management strategies in an actual corporate environment.
This course introduces strategic R&D management methods such as technology forecasting and project management techniques, and systematically teaches the basic concepts and methodologies for the management of technology. Actual technology management cases of ventures and tech companies will be examined and analyzed.
This course provides students with an understanding of the basic concepts of product design, the product design process, and the techniques required for product design. Students will also learn procedures, methods, and analytical techniques to reflect consumer needs and ergonomic considerations in the product design process.
Multivariate analysis is a statistical method to analyze two or more variables at the same time. The classification of statistical analysis into univariate and multivariate is based on the number of response variables being considered for the analysis. Techniques that belong to the multivariate domain include factor analysis, discriminant analysis, cluster analysis, canonical correlation analysis, multidimensional scaling, and structural equation modeling.
By introducing the basic concepts of management or production system design and improvement and the way of thinking and related techniques for establishing such systems, this course instills the managerial aptitudes and practical application skills for technology-oriented engineering students who often lack management skills.
Students will be able to understand the concept of probability and important properties of probability distributions, understand and calculate estimation problems, understand and compare the properties of different data, estimate and use basic concepts and properties of various distributions, understand, analyze, and estimate regression and correlation relationships, and understand and analyze the basic theories of hypothesis testing and statistical decision-making methods.
| Category | Program | Course/English | Credits |
|---|---|---|---|
| Major, Required | Master’s degree | Ph.D Thesis | 3 |
| Major, Required | Major, Elective | Advanced System Safety Engineering | 3 |
| Major, Required | Major, Elective | Research methodology | 3 |
| Major, Required | Major, Elective | Quality Improvement Seminar | 3 |
| Major, Required | Major, Elective | Intelligent Manufacturing System | 3 |
| Major, Required | Major, Elective | Application of Probability Theory and Statistics | 3 |
| Major, Required | Major, Elective | Design and Analysis of Information Systems | 3 |
| Major, Required | Major, Elective | Game and Decision Theory | 3 |
| Major, Required | Major, Elective | Advanced Universal Design | 3 |
| Major, Required | Major, Elective | Customer Relationship Management | 3 |
| Major, Required | Major, Elective | Advanced Topics in Data Mining | 3 |
| Major, Required | Major, Elective | Seminar in Project Management | 3 |
| Major, Required | Major, Elective | Advanced Production Management | 3 |
| Major, Required | Major, Elective | Advanced Topics in Computer Aided Design/Computer Aided Manufacturing | 3 |
| Major, Required | Major, Elective | Service Science | 3 |
| Major, Required | Major, Elective | Applications of Queueing theory | 3 |
| Major, Required | Major, Elective | Fuzzy Theory | 3 |
| Major, Required | Major, Elective | Financial Engineering | 3 |
| Major, Required | Major, Elective | Development of Ubiquitous Systems | 3 |
| Major, Required | Major, Elective | Advanced Topics in SCM | 3 |
| Major, Required | Major, Elective | Knowledge-based Expert Systems | 3 |
| Major, Required | Major, Elective | Advanced Topics in Project Management | 3 |
| Major, Required | Major, Elective | Network Optimization | 3 |
| Major, Required | Major, Elective | Advanced Topics in Reliability Engineering | 3 |
| Major, Required | Major, Elective | Computer-Integrated Manufacturing | 3 |
| Major, Required | Major, Elective | Seminar in Industrial and Management Engineering | 3 |
| Major, Required | Major, Elective | Modeling and Simulation | 3 |
| Major, Required | Major, Elective | Mathematical Modeling & Optimization | 3 |
| Major, Required | Major, Elective | Meta-heuristics | 3 |
| Major, Required | Major, Elective | Technical paper writing | 3 |
| Major, Required | Major, Elective | Nonlinear Programming | 3 |
| Major, Required | Major, Elective | Multi Criteria Decision Analysis | 3 |
| Major, Required | Major, Elective | Theory of stochastic proces | 3 |
There are always risks in a company’s production flowchart, and task analysis and risk analysis are necessary to eliminate these risks in advance and ensure safety.
This course deals with the process and method of research and projects. A research project is visualized as a journey where you pass certain landmarks along your way. Every research project needs to start with a clear problem formulation. As you develop your project, you will find critical junctions where you will make choices about how to proceed. This course will help so that you can make wise decision on these junctions such as sampling, measurement, experimental design, analysis and theories of validity.
By analyzing quality improvement cases occurring on industrial sites, students are able to develop the ability to identify problems and improvements in both hardware and software and solve on-site problems in quality, production, cost, delivery, safety, etc.
This course establishes the basic concepts of process, management, and information systems in production, and provides a wide range of theories and techniques for building automated production systems and integrated systems. In particular, it covers methodologies to reduce human intervention and realize small batch production by modeling the human knowledge and experience necessary for manufacturing activities.
Students will learn estimation and testing methods to analyze the characteristics of a population based on the probability theory for decision-making. They will also learn how to represent various data and techniques for statistical processing and analysis (regression analysis, correlation analysis) and apply them when writing research papers and reports.
With the advent of the mobile era, information systems for systematically managing and analyzing data generated in tremendous amounts throughout society enhance the competitiveness of the company and provide the basis for proposing a roadmap for the company. With the growing importance of information systems, the process of designing and analyzing information systems is the most crucial step in the operation of information systems in the future. This course covers software development methodologies, documentation techniques, and project management, and introduces way to applies the knowledge and techniques obtained in the course to real-world problems using various tools.
This course is a study of human judgment and behavior, and explores new and advanced theories of games and decision-making, including traditional game models and decision-making models.
Universal design is a creative paradigm for the 21st century that will enable us to promote human dignity and equality. Considering that design is about making people’s lives fuller and more convenient, universal design is a design concept that revitalizes the meaning of “for human beings.” It stems from the recognition that products, buildings, and environments should be designed to meet the needs and activities of a wide range of people, including the elderly, people with disabilities, and children. In recent years, especially in the United States, research in this area has been actively conducted, and universal design has become an increasingly important topic of interest for industrial designers, architects, environmental designers, and others. As such, universal design aims to make products and environments accessible to all, with little or no additional cost.
This is a course on how businesses manage customer relationships, acquire and retain customers, and analyze and store information about customers, sellers, and partners.
This course examines how to systematically and automatically identify statistical rules or patterns in large amounts of stored data or apply them.
In order to effectively manage various projects, students will study the latest techniques and principles for efficiently performing various aspects of project management, including project conceptualization and planning, schedule and cost management, resource management, team formation and operation, risk management and decision making, and utilization of related software and the Internet.
This is a course on how businesses plan, organize, and control production in order to carry out production activities rationally and efficiently.
This course is a study of computer-aided design and manufacturing and systems that use computers to design a product and automatically produce the product by creating a numerical control (NC) tape to operate machine tools based on the design.
It aims to identify the essence of the service industry by combining various disciplines such as science, business administration, social sciences, and computer science, and to revolutionize service levels.
This course covers stochastic models and modeling processes, queueing systems, Markovian queueing systems, generalized arrival queueing systems, queueing networks, and discrete-time queueing systems.
This is a mathematical theory of how the brain makes judgments and decisions in ambiguous and unclear situations, with applications in a wide range of fields, including control engineering and artificial intelligence.
This course is aimed at building the framework for understanding investment decision-making. In this course, students will study key concepts such as efficient markets, asset allocation, and investment analysis, examine some of the recent changes that have occurred in the investment climate and conditions, and examine the main concerns of investors and the tools needed to assess and address these challenges.
Students will examine the strategic importance of the strategy, design, planning, and operations of supply chains and develop analytical skills to mathematically model and solve problems that arise in the supply chain.
While learning the overall contents of building a knowledge expert system (language, method, software utilization, etc.), students will select an application field and research and develop a system that can be applied in practice.
Students will examine a new paradigm of business management techniques for projects with high risks that are unpredictable.
The goal is to develop product reliability requirements, establish appropriate reliability programs, and perform appropriate analysis and work to ensure that the product meets those requirements.
This is a field where computers are used to build an integrated system from technology development, design, production, and sales. When customers demand a new product, a new product that meets the needs is quickly designed, and production drawings and product specifications are immediately transmitted to production staff through a computer to prepare for production. The manufacturing location, delivery date, specifications, and quantity are automatically sent to the production site from the sales department through a network. The information on production status is reviewed not only at the production site but also by the sales department at the same time, so that the entire company has an integrated production and sales system.
Students will examine issues related to each area of industrial and management engineering, especially those related to their respective dissertation topics.
This is a course on testing or analyzing how objects or phenomena in a particular system behave/work by running a model.
This is a course on analyzing the conditions under which a real value, function, or integer is maximized or minimized for a given set of real values, functions, or integers defined over a given set, and is used to address the problem of allocating limited resources in the most efficient manner.
This course introduces metaheuristic methods such as genetic algorithms, tabu search, and simulated annealing to solve complex real-world optimization problems that are difficult to deal with using traditional optimization algorithms, and covers the theoretical background and real-world applications of these methods.
This course concerns a method of solving problems using a mathematical approach and deals with optimization problems, allocation problems, etc.
This is a course on the process of selecting the best option by systematically analyzing and examining the advantages and disadvantages of various options in relation to costs, benefits, gains, and losses to achieve a goal.
In this course, students will learn various stochastic processes such as birth-death process, renewal process, Poisson process, Markov process, etc. and learn how to apply them in the real world.