• Journal of the Korean Operations Research and Management Science Society
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• v.12 no.1
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• pp.175-175
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• 1987

Business reengineering (BR) is an emerging idea that attempts to restructure inefficient, current business processes through redesigning jobs and exploiting new information technology (IT), and helps achieving significant improvements in white-collar productivity and returns of IT investment. This research analyzed order-taking and purchasing processes of a company (a coil distributor) using BR concepts, models and principles. We also made proposals for reengineering the present business processes. For this, BR literatures were reviewed to derive BR models and principles to be used for analyses. We applied these ideas to six(6) types of order-taking and purchasing processes of Company T. For each type, current processes, BR analyses, and proposals for BR were described. Finally, findings were summarized and discussed.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.402-409
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• 2007

Markov Chains has proven to be effective in predicting human behaviors in the areas of web site assess, multimedia educational system, and driving environment. In order to extend an application area of predicting human behaviors using Markov Chains, this study was conducted to investigate whether Markov Chains could be used to predict human behavior in selecting mobile phone menu item. Compared to the aforementioned application areas, this study has different aspects in using Markov Chains : m-order 1-step Markov Model and the concept of Power Law of Learning. The results showed that human behaviors in predicting mobile phone menu selection were well fitted into with m-order 1-step Markov Model and Power Law of Learning in allocating history path vector weights. In other words, prediction of mobile phone menu selection with Markov Chains was capable of user's actual menu selection.

• Korean Management Science Review
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• v.12 no.1
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• pp.175-195
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• 1995

Business reengineering (BR) is an emerging idea that attempts to restructure inefficient, current business processes through redesigning jobs and exploiting new information technology (IT), and helps achieving significant improvements in white-collar productivity and returns of IT investment. This research analyzed order-taking and purchasing processes of a company (a coil distributor) using BR concepts, models and principles. We also made proposals for reengineering the present business processes. For this, BR literatures were reviewed to derive BR models and principles to be used for analyses. We applied these ideas to six(6) types of order-taking and purchasing processes of Company T. For each type, current processes, BR analyses, and proposals for BR were described. Finally, findings were summarized and discussed.

• Management Science and Financial Engineering
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• v.2 no.1
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• pp.103-122
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• 1996

This paper presents an easily used graphical procedure for simultaneous estimation of the index, skewness, scale, and location parameters of the stable laws. First, the index $\alpha$ and skewness $\beta$ are estimated through the joint use of a tail length statistic $\widetilde{K_t}$ and a skewness statistic $\widetilde{K_s}$, both of which are functions of order statistics. Next, the function of order statistics needed for estimation of scale $\sigma$ and location $\mu$ are determined from a nomogram indexed on the estimates of $\alpha$ and $\beta$. Some applications and examples are provided.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.2
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• pp.125-125
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• 1991

This paper presents a framework for the hierarchical PPC(Production Planning and Control) in make-to-order environment with job shop. The characteristics of the environment are described as : 1) project with non-repetitive and individual production, 2) short delivery date, 3) process layout with large scales manufacturing. 4) job shops. The PPC in a make-to-order typically are organized along hierarchical fashions. A model is proposed for the hierarchical job shop scheduling based on new concepts of production system, work and worker organization. Then, a new integrated hierarchical framework is also developed for the PPC based on concepts of the proposed job shops scheduling model. Finally, the proposed framework has been implemented in the Electric Motor Manufacturing and the results showed good performance.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.531-534
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• 2006

Response surface methodology (RSM) is a useful collection of experimentation techniques for developing, improving, and optimizing products and processes. When we are to estimate second-order regression model and optimize quality characteristic by RSM, central composite designs and Box-Behnken designs are widely in use. However, in developing cutting-edge products, it is very crucial to reduce the time of experimentation as much as possible. In this paper small-sized second-order designs are introduced and their estimation abilities are compared in terms of D-optimality, A-optimality, and variance of regression coefficients, ease of experimentation, number of experiments. Then we present a guideline of using specific designs for specific experimentation circumstances. The result of this study will be beneficial to experimenters who face experiments which are expensive, difficult, or time-consuming.

• Journal of the Korean Operations Research and Management Science Society
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• v.11 no.1
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• pp.44-50
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• 1986

In this paper, we find optimal policy for the (Q, r) inventory model under the lead time uncertainty. The (Q, r) inventory model is such that the fixed order quantity Q is placed whenever the level of on hand stock reaches the reorder point r. We first develop the single level inventory model as the basis for the analysis multi-level distribution systems. The functional problem is to determine when and how much to order in order to minimize the expected total cost per unit time, which includes the set up, inventory holding and inventory shortage cost. The model, then, is extended to the multi-level distribution system consisting of the factory, warehouses and retailers. In this case, we also find an optimal policy which minimizes the total cost of the contralized multi-level distribution system.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.1
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• pp.117-129
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• 2008

Make-to-order manufacturers with multiple plants at multiple sites need to have the ability to quickly determine which plant will produce which customer order to meet the due date and minimize the transportation cost from the plants to the customer. Balancing the work loads and minimizing setups and make-span are also of great concern. Solving such scheduling problems usually takes a long time. We propose a new approach, which we call 'preprocessing', for resolving such complex problems. In preprocessing scheme, a 'good' a priori schedule is prepared and maintained using unconfirmed order information. Upon the confirmation of orders. the preprocessed schedule is quickly modified to obtain the final schedule. We present a preprocessing solution algorithm for multi-site constraint scheduling problem (MSCSP) using genetic algorithm; and conduct computational experiments to evaluate the performance of the algorithm.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.191-196
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• 2008

This paper presents an evolutionary algorithm for goal programming with preemptive priority. To do this, an evolutionary strategy is suggested which search for the solution satisfying the goals in the order of the priority. Two-sided assembly line balancing problems with multiple goals are used to validate the applicability of the algorithm. In the problems, three goals are considered in the following priority order: minimizing the number of mated-stations, achieving the goal level of workload smoothness, and maximizing the work relatedness. The proper evolutionary components such as encoding and decoding method, evaluation scheme, and genetic operators, which are specific to the problem being solved, are designed in order to improve the algorithm's performance. The computational result is reported.

• Journal of the Korean Operations Research and Management Science Society
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• v.42 no.2
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• pp.19-34
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• 2017

In this study, we develop an optimal sourcing strategy considering compensation orders to mitigate the supply chain disruption risks. We considered two-echelon supply chain consisting of a single buyer and multiple suppliers who have fixed transaction cost and probabilistic disruption risks. Under this setting, we provide the joint optimization method to determine the number of suppliers and the order quantities. Through numerical examples, we provide managerial implications on the sourcing strategy by investigating changes in the order quantities and the number of suppliers due to the degree of supply chain disruption risks.