• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.231-234
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• 2002

This thesis is to introduce development process and application for domestic production of Automation System for High Density and Perpendicular Storage used in the industrial field. Most of the traditional Automation Systems for High Density and Perpendicular Storage are imported and using in the industrial field. As a result of this condition, foreign competitive power is decreased cause increasing of production cost. In spite of economical burden to protect a loss with management in the case of the industrial field dealing with various products, imported Automation System for High Density and Perpendicular Storage is used. On this developed Automation System for High Density and Perpendicular Storage solved these problems. Developed system uses managing computer, it is easy to manipulate input and output of production and inventory management. Moreover, Developed system accomplished shortened driving time and mechanical safety.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1990.04a
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• pp.186-194
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• 1990

Just-In-Time production is to keep the kanban system. When production managers implement and operate successfully the system in the multi-Line, multi-stage production setting, it is very important to determine the number of kanbans in deterministic kanban system under consideration with relevant factors as well as with cost. In concrete, we discuss about following factors in kanban system and provide a model formulating the multi-objective goal programming : Demand, stock on hand in process, Inventory cost and Labor cost, Vendor's suppling capacity, Work Load. Finally we analyze several numerical examples in order to test the model and attempt to expand the model in general case.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.290-296
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• 2008

This paper considers a two-stage dynamic lot-sizing problem constrained by a supplier's production capacity. We derive an improved O($T^6$) algorithm over the O($T^7$) algorithm in van Hoesel et al. (2005).

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.7-11
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• 2007

This paper considers a shipment planning of products from manufacturers to a third-party warehouse for demands with production time windows where a demand must be replenished in its time window. The underling lot-sizing model also assumes cargo delivery cost in each inbound replenishment to the warehouse. For this model, an optimal O($nT^4$) is presented where n is the number of demands and T is the length of the planning horizon.

• Proceedings of the Safety Management and Science Conference
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• 2002.11a
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• pp.233-238
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• 2002

The product process is sequence of all the required activities that a company must perform to develop, and manufacture a product. These activities include marketing, research, engineering design, quality assurance, manufacturing, and a whole chain of suppliers and vendors. The process also comprises all strategic planning, capital investments, management decisions, and tasks necessary to create a new product. manufacturing processes must be created so that the product can be produced in the product facility Purchasing new equipment and training workers may be required if new technology is to be used. Tools, fixtures, and the sequence of steps in the manufacturing processes must all be developed to allow rapid, high-quality, cost effective production. Also, it may be needed to be rearrange the production facility to adapt to the new manufacturing processes. Therefore, this study tries to proposed that Scheduling by customer needs group for minimizing the problem and reducing inventory, product development time, cycle time, and order lead time.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.463-468
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• 2002

Recently, a multi-facility, multi-product and multi-period industrial problem has been widely investigated in Supply Chain Management(SCM). One of the key issues in the current SCM research area involves reducing both production and distribution costs. We have developed an optimization model to tackle the above problems under the restricted conditions such as transportation time and a zero inventory. The model can be used to deride an appropriate factory and assign an optimal output the factory yields. This paper deals with the main idea of the proposed methodology in depth.

• Journal of the Korea Safety Management & Science
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• v.15 no.4
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• pp.261-267
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• 2013

In automobile company it is needed to establish the collaborative relationship between the assembly company and the part manufacturing company. In this paper we established the improvement for the procurement logistics and logistics in assembly company and thus we derived the near optimal procurement cycle through the simple EXCEL simulation and the improved inventory management method for H automobile company in CHINA. At this time we adopted the pull manufacturing process instead of push manufacturing process. We resulted that the manufacturing activity of both companies was stabilized and the usage of storage area in assembly company was reduced by 50%, especially it was reduced by 100% in the case of directly delivering the parts to assembly line through the third party logistics company.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.4
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• pp.53-61
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• 2008

Recently, Ertogral et al.[2] suggested two models considering the transportation cost based on single-vendor single-buyer integrated production-inventory problem. Although their problem-solving algorithm guarantees solutions obtained are optimal, a limitation is revealed that its performance can be inefficient due to complete enumeration search in a certain range. In this paper, a more efficient algorithm in finding optimal solutions is suggested for the same problem suggested by Ertogral et at.[2]. Numerical examples are presented for illustrative purpose.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.3
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• pp.79-86
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• 2006

Especially, MTO(Make-To-Order) companies take collaborative approaches with their partner companies to make low-price products and/or technologically low intensive products. The collaborative approach to manufacturing requires collaboration with partner companies for inventory review, production plan, and manufacturing to fulfill customer's orders. However, frequent changes of partnerships binder partner companies from sharing production information in effective ways since their information systems have different data architectures and platforms. Therefore, it is required flexible and standardized system integration approach fir effective information sharing. This research studies current status and problems of collaborative production system, proposes an architecture for collaborative production systems based on Web Services which is a standard information technology, and discusses expected effects and the vision of Web Services.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.139-145
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• 1996

A forecast on the past output data sets of small bar steels is very important information to make a decision on the future production quantities. In many cases, however, it has been mainly determined by experience (or rule of thumb). In this paper, past basic data sets of each small bar steels are statistically analyzed by some graphical and statistical forecasting methods. This work is mainly done by SAS. Among various quantitative forecasting methods in SAS, STEPAR forecasting method was best performed to the above data sets. By the method, the future production quantities of each small bar steels are forecasted. As a result of this statistical analysis, 95% confidence intervals for future forecast quantities are very wide. To improve this problem, a suitable systematic database system, integrated management system of demand-production-inventory and integrated computer system should be required.