In-Sup Um

Design of Experiment and Analysis Method for the Integrated Logistics System Using Orthogonal Array

This paper presents the simulation design and analysis of Integrated Logistics System(ILS) which is operated by using the AGV(Automated Guided Vehicle). To maximize the operation performances of ILS with AGV, many parameters should be considered such as the number, velocity, and dispatching rule of AGV, part types, scheduling, and buffer sizes. We established the design of experiment in a way of Orthogonal Array in order to consider (1)maximizing the throughput; (2)maximizing the vehicle utilization; (3)minimizing the congestion; and (4)maximizing the Automated Storage and Retrieval System(AS/RS) utilization among various critical factors. Furthermore, we performed the optimization by using the simulation-based analysis and Evolution Strategy(ES). As a result, Orthogonal Array which is conducted far fewer than ES significantly saved not only the time but the same outcome when compared after validation test on the result from the two methods. Therefore, this approach ensures the confidence and provides better process for quick analysis by specifying exact experiment outcome even though it provides small number of experiment.

A simulation-based optimal design and analysis method for designing a train overhaul maintenance facility

This paper presents a simulation-based optimal design and analysis method for designing a train overhaul maintenance facility. Because the train is composed of one or more cars, the authors design the simulation model after analysing the operation of the train according to the trains following parts: train, car, car body, and wheel. The authors consider the critical (dependent) factors and design (independent) parameters for the simulation analysis. Therefore, multi-criteria decision making (MCDM) is proposed for selecting alternatives, and simulation optimization is used for finding the optimal design parameters of the selected alternative. A case study for the above approach is applied to the electric locomotive overhaul maintenance facility. This paper provides a comprehensive framework for a train overhaul maintenance facility design using MCDM and the simulation optimization.

Wavelet-Based collaborative filtering for adapting changes in user behavior

Recommendation systems help users find the information, products and services they most want to find. Collaborative filtering is the method of making automatic predictions about the interest of a user by collecting interest information from many users, which has been very successful recommendation technique for recommendation systems in both research and practice. However, the traditional collaborative filtering is slow to detect the interest of a user changing with time as a case of user behavior and to adapt the changes, because the traditional collaborative filtering uses Pearsons correlation coefficient between users with the numerous values of property. In this paper, we apply the wavelet analysis to collaborative filtering in order to reveal the trends hidden in the interest of a user and propose the wavelet-based collaborative filtering for adapting changes in user behavior. The results of the performance evaluation show that the proposed wavelet-based collaborative filtering makes the improvement in the personalized recommendations.