Department of Industrial & Management Engineering
Logistics Information Technology LAB
We develop algorithms of optimal solutions that can solve problems in industrial engineering(computer science, and mathematics) and have an effort to apply these rhythms both scientifically and practically. For instance, we study in a wide field of imitation and information technology such as environmental technology, medical/life information technology and SCM information technology for the manufacturing industry.
Supply Chain Management (SCM) LAB
The SCM lab puts focus on the planning, organizing, and controlling of any logistics activities throughout the SCM process. System modeling and simulation is widely used to estimate and evaluate SCM systems. The lab members conduct research on comprehensive and state-of-the-art treatment for all the important aspects of a simulation study, including modeling, simulation software, model verification, and validation, input modeling, random-number generators, generating random variates and processes, statistical design and analysis of simulation experiments, and to motivate and highlight major application areas, such as manufacturing.
Management Engineering & Operations Research LAB
The Management Science Lab, sometimes called the Operations Research, has played a major role in developing and improving the industrial systems around us. The members of Management Engineering & Operations Research Lab are ALL professionals researching to develop new theories and apply the existing theories to a variety of industrial systems. The systems that the lab members are with concerned with include but are not limited to, logistics systems, service systems, financial systems, and transportation systems. The lab is focusing on the amalgamation of several research area fields. What is really important to the companies is the concepts of demand, profit, pricing, and market analysis. The final goal that should be sought ultimately is profit maximization.
SEAQ(Statistical Engineering for Advances Quality) LAB
The ACD&FD (Applied Computational Design & Fluid Dynamics) laboratory works to overcome the disadvantages of the experimental system such as space limitations, high cost of manufacturing and time through mechanical engineering simulation. Moreover, The laboratory struggles to achieve the academic and technical development in the field of fluid dynamics via the joint project/ technical transfer wiSEAQ Lab in order to continually develop and improve the theories and methods of statistical process control(SPC) and reliability. We develop user-friendly software and other statistical tools that help business and their employees implement SPC and Six Sigma, as well as tools to help students understand the concepts behind SPC and Six Sigma. In addition, much of our research focuses on combining SPC Knowledge with IT, BT, NT, and ET. Specifically, statistical process control, regression analysis, design of the experiment, reliability engineering, service quality, and quality management are the main disciplines in our lab. th our own know-how.
Ergo Mechanics LAB
The Ergo Mechanics Lab. conducts research on biomechanics, cognitive ergonomics, and affective ergonomics. Specifically, the analysis of kinematics and electromyography, MSD evaluation and prevention, and ergonomic product design are pursued for physical Ergonomics. As for cognitive ergonomics, usability testing methodology, human-computer interface design, and cognitive system design for safety are studied. The recent increasing interest in affective ergonomics is also accommodated by studying affective robotics, Kansei engineering methodology, and affective communication system design.
Probability Application LAB
Probability theory is the only scientific tool that is used to evaluate the systems with uncertainties and predict their performances. Especially, queueing theory is one of the most powerful tools which can analyze the systems which evolve in time probabilistically. Our research area includes the telecommunication system, manufacturing system, supply chain management, and more. Recently, we focused on the performance of stations which deal with Internet data and information, through which the best strategic plan can be derived from.
Information Analysis & Decision System LAB
For the best choice amongst many alternatives, we study the systematic information analysis and focus on two things. The first is a decision system whose four phases include measurement, assessment, management, and prediction. We here apply the Bayesian paradigm for translating uncertainties in nature into the well-formed information system. The second is a graphical presentation used for making decisions. Its logical expression will facilitate the decision making processes.
Complex Systems Interaction LAB
The Comples Xystems Interaction Lab focuses on Data-driven formal modeling and the control of complex systems focusing on human-machine(automated systems) interface.
Production Engineering & Operations Management LAB
The Production Engineering & Operations Management Lab focuses on various topics related to production engineering and operations management. The goal of the lab's research is to provide various optimal inventory policies for different types of production systems. The research topic within this research area include the following: Inventory management: the optimal ordering policies, centralized/-decentralized decision mechanism, deterministic and stochastic models, distribution free approach, and optimization techniques.
Advance manufacturing and process optimization: Decision making model for augmented production system, total productive maintenance for machine life improvement, optimum inspection and defect detection strategies.
Human factors and reverse logistics: Closed-loop supply chain management, Container management and product collection for its reuse through third party logistics (3PL).
Disruption management in the production system: Goal programming and multi-item production system. Humanitarian supply chain management: Inspection errors, Quality of human labors, environmental impact, and transportation.
Fuzzy: Uncertainty/ impreciseness in optimization.
Optimization: Analytical techniques and numerical methods.