I study a solution concept which preserves the nice Nash equilibrium properties of two-person zero-sum games, and define a locally competitive equilibrium which is characterized by a saddle point with respect to the coordinates of strategies. I show that a locally competitive equilibrium shares the properties of uniqueness of equilibrium payoffs, interchangeablity of equilibrium strategies and convexity of the equilibrium set.

The framework of "blue ocean strategy (Kim and Mauborgne, 2005)" applies to not only business firm level but also region or regional industry level. The paper suggests an approach of survey-based blue ocean strategy formulation. The result from survey on customer group and non-customer group gives information on positions of value curves for regions on strategy canvas and hints on how to reposition the focal region's value curve for both increases in value for customers and decreases in cost for local government, i.e., value innovation. In the context of rafting industry in Korea, this study shows the process how blue ocean strategy can be formulated with results from the survey on customers and business people in rafting industry. Relative positions of value curves on strategy canvas and importance scores for strategy elements provides the local government in region A with implications on which elements to raise or to reduce to create "blue ocean" in rafting industry under fierce inter-region competition.

There is a controversy on Internet pricing, flat-rate vs. usage-based. This study gives a comparative analysis between flat-rate and two-part tariff which is realistic alternative of usage-based pricing. In a basic economic model, two-part tariff based on ISP's cost structure satisfies allocative efficiency and relatively expand the number of subscribers. But the characteristics of Internet service like consumers' uncertainty on cost, measurement cost of traffic and network externality induce increase of cost or decrease of marginal utility. The analysis shows that small impact of these can make flat-rate more efficient.

Korean IT industry has been given much weight in national R&D management. A negative side of this fact is that Korean economy is likely to become vulnerable to a condition of the export business in certain items of IT industry which has a serious influence on the national economy. A customized investment strategy through the analysis of technology competitiveness and R&D status in each technology of IT field is required in order to rectify the structural vulnerability and pursue a continuous growth. In this research, a strategic direction to set up an efficient investment strategy is presented. In this process, it draws a portfolio analysis with two axes of technology level and technology life cycle. It also derives a priority order of the national investment considering the degree of technological impact, marketability, and adequacy of public support from AHP (Analytic Hierarchy Process) method by a survey of IT experts. A portfolio analysis in the prior stage helps the respondents in AHP become more familiar with the alternatives' characteristics so that their decision making process more corresponds with national R&D strategies.

AHP is widely used in various fields owing to its simplicity and clarity. However, it has the problems of reliability of panel's judgement and the assumption that each panelist's ability is equal. In this paper, we propose a reliable AHP method using Delphi technique, which aims to incorporate the relative expertise of panel and to increase their consensus. The method is called the Modified Delphi-AHP. We apply the method to analyzing the demand for lectures of Management of Technology.

The undirected Steiner tree problem in graphs is known to be NP-hard. The objective of this problem is to find a shortest tree containing a subset of nodes, called terminal nodes. This paper proposes a method based on a two-step procedure to solve this problem efficiently. In the first step. graph reduction rules eliminate useless nodes and edges which do not contribute to make an optimal solution. In the second step, a max-min ant colony optimization combined with Prim's algorithm is developed to solve the reduced problem. The proposed algorithm is tested in the sets of standard test problems. The results show that the algorithm efficiently presents very correct solutions to the benchmark problems.

It is the most important sucess factor for the electricity generation industry to minimize operations cost of surplus electricity generation through accurate demand forecasts. Temperature forecast is a significant input variable, because power demand is mainly linked to the air temperature. This study estimates the information value of the temperature forecast by analyzing the relationship between electricity load and daily air temperature in Korea. Firstly, several characteristics was analyzed by using a population-weighted temperature index, which was transformed from the daily data of the maximum, minimum and mean temperature for the year of 2005 to 2007. A neural network-based load forecaster was derived on the basis of the temperature index. The neural network then was used to evaluate the performance of load forecasts for various types of temperature forecasts (i.e., persistence forecast and perfect forecast) as well as the actual forecast provided by KMA(Korea Meteorological Administration). Finally, the result of the sensitivity analysis indicates that a $0.1^{\circ}C$ improvement in forecast accuracy is worth about $11 million per year.

In this paper, a model is presented to estimate a cycle time for completing an operation in a new type of AS/RS which can handle very heavy loads by separating the mechnisms for vertical and horizontal movements. Considering loading/unloading time between devices, we generalize the previous work, Hu et al. [9], which neglected the transfer time. Through the numerical experiments for various situations, we find that the difference of the cycle times between two models is fairly large and conclude that the transfer time between devices cannot be neglected at all.

By the newly emerging network access technology, we face the new heterogeneous network environment. Focusing on the co-existence of multiple access network technology and the complex service needs of users, the wireless service operators should present the stable service quality for every user. For this, the service operators should build the new operation framework which combine the pre-established network and newly adopted one. Our problem is finding the optimal heterogeneous network operation framework. We suggest market-based marginal cost function for evaluating the relative value of resource of each network and develop the whole new heterogeneous network operation framework. To test the applicability of developed operation framework, we build large-scale JAVA simulator. By this development, we can easily test the new network environment in practical engineering field.

This paper purposes analyzing how task assignments influence on business process efficiency, and improving business process efficiency in Business Process Management (BPM) environments. For this purpose, task assignment is categorized into dynamic assignment and static assignment. Dynamic assignment binds a user to a task at process run-time, whereas static assignments assigns a task to a user at process build-time. So far, the influence of task assignment methods has not been studied. We, in this paper, analyze the business process efficiency using two different assigning rule, in terms of cycle time as a efficiency measure. The comparison result can provide a guideline for a company who wants to employ commercial BPM systems.

An organizational performance measurement practice is a function of performance measurement system and performance management system they are currently employing, and its quality is determined by evaluating the followings in a comprehensive manner: first, if they are measuring right things; second, if they are measuring in a right way; third, if they are actively facilitating a process of data collection, structuring, analysis, interpretation, and dissemination; and fourth, if they are using performance measurement results for the primary purposes of performance measurement. This study proposes a diagnostic framework for evaluating the performance measurement practices of public broadcasting which include the qualities of performance measurement and performance management, and develop a structured questionnaire for that purpose. The framework proposed in this study does not serve only as a useful tool for public broadcasting to revise their respective performance measures and performance measurement systems, but it also make their respective performance measurement practices a strategic management tool as well as an operational management one.

A traffic analysis on the Internet has an advantage for obtaining the characteristics of transferred packets. There were many studies to understand the characteristics of the Internet traffic with mathematical statistical approach. The approach of this study is different from previous studies. We first introduced a virtual network concept to present the Internet as a simplified mathematical model. It also represents each traffic flowing on the Internet as a parallel Gaussian channel on the virtual network. We suggest the optimal capacity of each parallel Gaussian channel using some related studies on the Gaussian channel model.

We consider the inventory replenishment problem and the vehicle routing problem simultaneously in the vending machine operation. This problem is known as the inventory routing problem. We design a memory cell in the clonal selection algorithm. The memory cell store the best solution of previous solved problem and use an initial solution for next problem. In general, the other clonal selection algorithm used memory cell for reserving the best solution in current problem. Experiments are performed for testing efficiency of the memory cell in demand uncertainty. Experiment result shows that the solution quality of our algorithm is similar to general clonal selection algorithm and the calculations time is reduced by 20% when the demand uncertainty is less than 30%.

Customer retention has been a pressing issue for companies to get and maintain the loyal customers in the competing environment. Lots of researchers make effort to seek the characteristics of the churning customers and the loyal customers using the data mining techniques such as decision tree. However, such existing researches don't consider relationships among customers. Social network analysis has been used to search relationships among social entities such as genetics network, traffic network, organization network and so on. In this study, a customer network is proposed to investigate the differences of network characteristics of churning customers and loyal customers. The customer networks are constructed by analyzing the real purchase data collected from a Korean cosmetic provider. We investigated whether the churning customers and the loyal customers have different degree centralities and densities of the customer networks. In addition, we compared products purchased by the churning customers and those by the loyal customers. Our data analysis results indicate that degree centrality and density of the churning customer network are higher than those of the loyal customer network, and the various products are purchased by churning customers rather than by the loyal customers. We expect that the suggested social network analysis is used to as a complementary analysis methodology with existing statistical analysis and data mining analysis.

In this paper, we study stochastic maximal covering location problem considering floating population. Traditional maximal covering location problem assumed that number of populations at demand point is already known and fixed. In this manner, someone who try to solve real world maximal covering location problem must consider administrative population as a population at demand point. But, after observing floating population, appliance of population in steady-state is more reasonable. In this paper, we suggest revised numerical model of maximal covering location problem. We suggest heuristic methodology to solve large scale problem by using genetic algorithm.

In this paper, an analysis problem of repair levels and spare part allocation for MIME(Multi indenture multi echelon) systems is studied using simulation and meta-heuristics. We suggest a method to determine simultaneously repair levels and spare parts allocation to minimize the life cycle cost of MIME system under availability constraint. A simulated annealing method is used to analyze the repair levels and genetic algorithm is used to obtain the optimal allocation of spare parts. We also develop a simulation system to calculate the life cycle cost and system availability. Some numerical examples are also studied.