• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.4
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• pp.143-153
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• 2001

In this study we develop a computer simulation model to evaluate the effects of various production and order processing policies measured in terms of on-time delivery rate and average waiting time of job orders. Policies considered include : eliminating inflated due date, lot splitting, loss time reduction, attaining full flexibility in production lines, and selective order promising scheme. Actual order-production data from a chemical company were used in the simulation model. Based on the simulation results, we make several suggestions that can significantly reduce the production lead time and increase the on-time delivery rate.

• Proceedings of the Korea Society for Simulation Conference
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• 2000.11a
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• pp.165-173
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• 2000

Analytic models have been developed to solve integrated production-distribution problems in supply chain management (SCM). As one of major constraints in analytic models, capacity, which is the total operation time in this paper has mostly been known or disregarded assuming infinite capacity. Also, as major factors, machine processing time to fabricate or assemble a part or product at a certain machine center in production system and vehicle processing time to deliver a product to a customer by a certain vehicle in distribution system have been fixed and regarded as a static factor, But in the real systems significant differences exit between capacity and the required time to achieve the production-distribution plan and between processing time and consumed time to process a part or product. In this paper, capacity and processing times in the analytic model are considered as dynamic factors and adjusted by the results from independently developed simulation model, which includes general production-distribution characteristics. Through experiments, we obtain the more realistic solutions reflecting stochastic natures by performing the iterative analytic-simulation procedure.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.36
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• pp.219-232
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• 1995

Even though Just-In-Time Production System is introduced to industry in order to strengthen competition, applying this production system to field is hard and some problems are occurred. On this study, the algorithm which become smoothed production, one of the prerequisite condition for implementing Just-In-Time, are developed. On the other word. it is possible to become smoothed production based on keeping Lot Production System. The possibility of establishing more stable smoothed production scheduling are proved on this study by comparing with previous researches.

• Transactions of the Korean hydrogen and new energy society
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• v.21 no.1
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• pp.64-71
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• 2010

This paper deals with an economic evaluation of domestic immersing type photoelectrochemical hydrogen production. We also make some sensitivity analysis of hydrogen production prices by changing the values of input factors such as the initial capital cost, the solar to hydrogen conversion efficiency, and the system duration time. The hydrogen production price of the immersing type photoelectrochemical system was estimated as 8,264,324 won/$kgH_2$. It is expected that the production cost by photoelectrochemical hydrogen production can be reduced to 26,961 won/$kgH_2$ if the solar to hydrogen conversion efficiency is increased to 14%, the system duration time is increased to 20,000 hours, and the initial capital cost is decreased to 10% of the current level. The photoelectrochemical hydrogen production is evaluated as uneconomical at this time, and we need to enhance the solar to hydrogen conversion efficiency and the system duration time as well as to reduce prices of the system facilities.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.166-173
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• 2007

In this paper, we propose a model-based approach to estimating production parameters of semiconductor FAB equipment. For FAB scheduling, for example, we need to know equipment's production parameters such as flow time, tact time, setup time, and down time. However, these data are not available, and they have to be estimated from material move data such as loading times and unloading times that are automatically collected in modern automated semiconductor FAB. The proposed estimation method may be regarded as a Bayes estimation method because we use additional information about the production parameters. Namely, it is assumed that the technical ranges of production parameters are known. The proposed estimation method has been applied to a LCD FAB, and found to be valid and useful.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.3
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• pp.8-15
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• 2014

For zero inventory and mixed assembly production, JIT (Just In Time) production system in Toyota and JIS (Just-In-Sequence) production system in Hyundai motor co. have been proposed in automobile production areas. Even though the production systems are popular in the areas, many subcontract companies producing part-modules for final production at a parent company suffers from excessive or shortage amount of inventory due to the time gap of production and delivery to the parent company. In this study, we propose an efficient real-time assembly sequence system applying a well-known Pareto method using Paint-In information in painting process and daily production planning information. Based on this system, a production line can estimate the shortage amount of UPH (Units Per Hour) at production line and recovers the amount before operating assembly production in the line. The proposed system provides efficiency on productivity compared with the previous system.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.283-290
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• 2014

In the flat panel display industry, to meet production target quantities and the deadline of production, the scheduler and dispatching systems are major production management systems which control the order of facility production and the distribution of WIP (Work In Process). Especially the delivery time is a key factor of the dispatching system for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors of the delivery time and to build the delivery time forecasting model. To select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the accelerated failure time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the mean square error (MSE) criteria, the AFT model decreased by 33.8% compared to the statistics prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing the delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

• Journal of the Korea Society for Simulation
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• v.25 no.2
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• pp.75-81
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• 2016

By using simulation, fixed location production method and flow production method have been compared to improve the productivity of deckhouse preceding outfitting process. In this paper, we analyze that the suggested flow production system instead of fixed location production can improve productivity. In current preceding outfit production system which adopts fixed location production, where a block occupies an area and does not move until the work finishes. On the other hand, in improved flow production system, the block moves instead of workers and equipment. Though the output of two systems are almost the same when we did not consider the moving time and waiting time of blocks, the flow production will be better when the variability of task time will be reduced.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.3
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• pp.27-46
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• 1992

This paper considers an (r, Q) policy for operation of a multipurpose facility. It is assumed that whenever the inventory level falls below r, the model starts to produce the fixed amount of Q. The facility can be utilized for extra production during idle periods, that is, when the inventory level is still greater than r right after a main production operation is terminated or an extra production operation is finished. But, whenever the facility is in operation for an extra production, the operation can not be terminated for the main production even though the inventory level falls below r. In the model, the demand for the product is assumed to arrive according to a compound Poisson process and the processing time required to produce a product is assumed to follow an arbitary distribution. Similarly, the orders for the extra production is assumed to accur in a Poisson process are the extra production processing time is assumed to follow an arbitrary distribution. It is further assumed that unsatisfied demands are backordered and the expected comulative amount of demands is less than that of production during each production period. Under a cost structure which includes a setup/ production cost, a linear holding cost, a linear backorder cost, a linear extra production lost sale cost, and a linear extra production profit, an expression for the expected cost per unit time for a given (r, Q) policy is obtained, and using a convex property of the cost function, a procedure to find the optimal (r, Q) policy is presented.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.10a
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• pp.185-190
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• 1993

In this paper, we investigate the effect of production uncertainty - especially demand fluctuation and activity time variation, to production control policies. First, we examine three famous production control policies, namely, MRP, JIT, OPT, from shop floor control perspective and analyze the difference among them. Based on these, simulation studies are performed to draw out the effects of demand fluctuation which are classified into demand lumpiness and demand irregularity, and, the effects of activity time variation which are classified into standard time variation and non-standard time variation. Experimental investigation shows that, in terms of demand fluctuations, MRP is affected by demand lumpiness, but JIT by demand irregularity. And we also see that both MRP and JIT are influenced by standard time variation with respect to activity time variations.