Based on the characteristics of 'Korean electric power industry R&D programs', this study proposes a new performance evaluation framework where electric power industry related R&D projects are scrutinized. The abovementioned R&D programs have their own goals and especially they emphasize the effectiveness as well as the efficiency of each subordinate R&D project. Hence, in this framework, a performance evaluation procedure is established and then a mathematical model is developed according to the procedure. The model calculates performance evaluation indices for a set of R&D projects integrating the effectiveness with the efficiency of each R&D project. In a case study with an empirical dataset, statistical significance is tested on the integrated performance evaluation indices of R&D projects regarding organizational types and program categories considered.

Warfare information system is a core factor of future combat operation under NCW(Network centric Warfare) environment. In this paper, we proposed a new MOE(Measure of Effectiveness) that can assess the combat power synergistic effect of warfare information system in the theater-level joint fire operation. This new MOE uses the rule of Newton's second law($F=(m{\Delta}{\upsilon})/{\Delta}t{\Rightarrow}(M{\upsilon}I)/T$). Four factors considered in combat power evaluation are network power(M), movement velocity(v), information superiority(I), command and control time(T). We applied this new MOE to the JFOS-K(Joint Fire Operating System-Korea) to assess its combat power effect and compared with other's effects obtained from different MOE.

In the early design stage, system reliability must be estimated from life testing data at the component level. Previously, a point estimate of system reliability was obtained from the unbiased estimate of the component reliability after assuming that the number of failed components for a given time followed a binomial distribution. For deriving the confidence interval of system reliability, either the lognormal distribution or the normal approximation of the binomial distribution was assumed for the estimator of system reliability. In this paper, a new estimator is used for the component level reliability, which is biased but has a smaller mean square error than the previous one. We propose to use the beta distribution rather than the lognormal or approximated normal distribution for developing the confidence interval of the system reliability. A numerical example based on Monte Carlo simulation illustrates advantages of the proposed approach over the previous approach.

In this paper we developed a method for an optimal planning to reorganize Dong offices to enhance the administrative efficiency. First we defined a mathematical model for the optimal planning problem of reorganizing Dong office and developed a genetic algorithm to solve the problem. For the purpose of minimizing standard deviation of population, area and distance among reorganized offices, the constraints such as allocation, distance, area, population, etc. are considered and weights are applied to Dong offices in the downtown and shopping area. The developed algorithm was applied for reorganizing Dong offices in Jongro Gu, Seoul. The results showed that the developed algorithm could be applied for the real world problem. This study may be applied to the optimal decision of reorganization of offices in the similar reorganization or company M&A situations by changing constraints and weights.

It is most important for distribution center in retail business to delivery commodities in a timely manner. Accordingly, many companies try to make distribution center effective using the Warehouse Management System(WMS) integrated legacy system. Also, the Customer Relationship Management(CRM) is the most typical paradigm in management lately. Even though the WMS and CRM are independent system of each other, WMS, coupled with CRM makes customer satisfied more effectively. In this paper, we proposed the methodology for inventory location after analyzing and applying customer buying pattern data in the CRM through the MBA(Market Basket Analysis), which is part of data mining. We used an example modeling a real distribution center in retail through a 3D simulation tool and examined correlation between commodities using customer buying pattern. After that, we applied it to the inventory location system through the MBA in an example. Finally, we identified decrease in the time for picking, which is the majority of distribution center. Besides, we proposed a simulation methodology before applying new methodology. Consequently, it removes potential errors in advance and makes a optimized inventory location system.

The driving force of the manufacturing field is members' mind-set and activity. However, there is no appropriate program of change management for workers who work in the manufacturing field. Most of the small group activities were focused on the fixed tools and processes to make improvement only, not the change itself. Therefore, it was difficult to have self generating energy without actually motivating people of change. Study of IFI(Integrated Field Innovation) means change of management. This study will introduces IFI which is a new methodology of change and innovation of workers, and will describe how workers participated in the activities voluntarily. The principle of IFI is that letting employees to find the problems of the sites -anything that makes employees considering as the problem while they are working- and solve those problems through any tools own their own. Workers do not have to waste their time on bounding by unnecessary tools and processes. There are several cases that company "D" has been actually applied the IFI methodology in 2008. Through these cases, IFI proved that it was a optimized methodology for workers who work in the manufacturing field.

It is crucial to prevent low yields in the semiconductor industry. Since many factors affect variation in yield and they are deeply related, preventing low yield is difficult. There have been substantial researches in the field of yield prediction. Many researchers had used the statistical methods. Many studies have shown that artificial neural network (ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance some problems such as over-fitting and poor explanatory power arise. In order to overcome these limitations, a relatively new machine learning technique, support vector machine (SVM), is introduced to classify the yield. SVM is simple enough to be analyzed mathematically, and it leads to high performances in practical applications. This study presents a new efficient classification methodology, Stepwise-SVM (SSVM), for detecting high and low yields. SSVM is step-by-step adjustment of parameters to be precisely the classification for actual high and low yield lot. The objective of this paper is to examine the feasibility of SVM and SSVM in the yield classification. The experimental results show that SVM and SSVM provides a promising alternative to yield classification for the field data.

This research deals with a task assignment problem to worker group which consists of one master and one assistant. Each task must be assigned to only one worker group and it is possible to make a pair of each master and each assistant to organize a worker group. A worker group may have more than one task assigned to it, but the workloads of each worker group must be balanced within the allowable range. This problem can be formulated mathematically using the Mixed Integer Programming(MIP), where the objective function is to minimize the total assignment cost. A two phase heuristic algorithm is suggested in order to find approximate solutions. The first phase is to obtain an initial solution, where the initial assignment is performed to follow the workload adjustment. In the second phase, the solution is improved through the repeated process of the exchange and the assignment adjustment. Numerical experiments have been performed to evaluate the performance of the heuristic algorithm.

The anti-collision algorithms to identify a number of tags in real-time in RFID systems are divided into the anti-collision algorithms based on the Framed slotted ALOHA that randomly select multiple slots to identify the tags, and the anti-collision algorithms based on the Tree-based algorithm that repeat the questions and answer process to identify the tags. In the hybrid algorithm which is combined the advantages of these algorithms, tags are distributed over the frames by selecting one frame among them and then identified by using the Query tree frame by frame. In this hybrid algorithm, however, the time of identifying all tags may increase if many tags are concentrated in a few frames. In this study, to improve the performance of the hybrid algorithm, we suggest an improved algorithm that the tags select a specific group of frames based on the earlier bits of the tag ID so that the tags are distribute equally over the frames. By using the simulation and mathematical analysis, we show that the suggested algorithm outperforms traditional hybrid algorithm from the viewpoint of the number of queries per frame and the time of identifying all tags.

The purpose of this study is to develop a mid and long-term roadmap for "Commercial and Military Collaboration Standardization(CMCS) program." We developed three layer's roadmap through systematical process. The roadmap is composed of four targets, eight projects and forty-six tasks. To evaluate the quality of the roadmap, we applied checklist method and statistical analysis. Consequently, the quality is positive and it shows that the roadmap could apply for CMCS program. In addition, it is expected to contribute to the standardization between military supplies and commercial products.