• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.55-63
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• 2019

This paper develops an algorithm to determine the batch size of the batch process in real time for improving production and efficient control of production system with multiple processes and batch processes. It is so important to find the batch size of the batch process, because the variability arising from the batch process in the production system affects the capacity of the production. Specifically, batch size could change system efficiency such as throughput, WIP (Work In Process) in production system, batch formation time and so on. In order to improve the system variability and productivity, real time batch size determined by considering the preparation time and batch formation time according to the number of operation of the batch process. The purpose of the study is to control the WIP by applying CONWIP production system method in the production line and implements an algorithm for a real time batch size decision in a batch process that requires long work preparation time and affects system efficiency. In order to verify the efficiency of the developed algorithm that determine the batch size in a real time, an existed production system with fixed the batch size will be implemented first and determines that batch size in real time considering WIP in queue and average lead time in the current system. To comparing the efficiency of a system with a fixed batch size and a system that determines a batch size in real time, the results are analyzed using three evaluation indexes of lead time, throughput, and average WIP of the queue.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.2
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• pp.17-24
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• 2013

This paper is to analyze the picking lead time for picking batch size in a warehouse system and to get minimum picking batch size that is the warehouse system feasible. The warehouse system consists of aisles and racks, which two racks face each other through aisle. The products are picked from the storage locations by batch size. The probability that items are picked in the each row of the rack in the aisle for order picking activity is derived. The picking lead time for picking batch size is the time passed from the first picking location to arrival at starting location in aisle picking all items included in a batch size. The picking lead time for picking batch size in an aisle is analyzed. The picking lead time for picking batch size in the whole warehouse system is obtained. The warehouse system is feasible if all items that customers order are picked from the storage locations for same period. The picking batch size that is the warehouse system feasible is obtained. The problem is analyzed, a solution procedure is developed, and a numerical example is shown to explain the problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.118-125
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• 2013

This paper deals with the picking batch size which a bi-directional carousel system can be feasible. The items that customers order are retrieved from the bins of carousel with batch size. The mathematical equations representing rotary travel distance and retrieval lead time to pick a given batch size are derived. Rotary travel distance represents the distance which carousel system rotates to retrieve items in a batch. The bi-directional carousel system rotates to minimize the travel distance in retrieving the items in a batch. Rotary travel distance and retrieval lead time are analyzed for the batch size through the simulation approach. From the simulation, the retrieval batch size that carousel system can be feasible is obtained. A numerical example is shown to explain the solution procedure.

• Nuclear Engineering and Technology
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• v.51 no.4
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• pp.954-962
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• 2019

It is well known that the variance of tally is biased in a Monte Carlo calculation based on the power iteration method. Several studies have been conducted to estimate the real variance. Among them, the batch method, which was proposed by Gelbard and Prael, has been utilized actively in many Monte Carlo codes because the method is straightforward, and it is easy to implement the method in the codes. However, there is a problem when utilizing the batch method because the estimated variance varies depending on batch size. Often, the appropriate batch size is not realized before the completion of several Monte Carlo calculations. This study recognizes this shortcoming and addresses it by permitting selection of an appropriate batch size.

• Journal of the Korea Institute of Building Construction
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• v.13 no.4
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• pp.360-371
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• 2013

Fast delivery of construction projects provides more value to project owners. Batch production, which is production not in single pieces, but in batches, is a common approach in repetitive construction projects such as multi-unit residential building construction projects. In batch production, the use of a small batch size allows the early start of subsequent activities, and thus can lead to early completion of projects. In addition to batch size, balance between productivities in construction activities can affect project duration. However, the impact of the balance between productivities with regard to their order on project duration has not been studied. The main goal of this study is to test a hypothesis, which is that the order of construction activities' unbalanced productivities affects the amount of time reduction that can be achieved by using a small batch size. A computer-based simulation model was developed, and five different cases were simulated to test the hypothesis. The conclusion of the simulation result is that the order of productivities does not affect the time reduction achieved by using a small batch size. It is expected that the findings of this study can help general contractors make decisions in terms of batch size.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.246-253
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• 2007

All of the machines in a production line can be classified into bottleneck and non-bottleneck machines. A bottleneck is a resource whose capacity limits the throughput of the whole production facility. This paper addresses a batch sizing problem at the bottleneck machine. Traditionally, most batch sizing decisions have been made based on the EOQ (economic order quantity) model where setup and inventory costs are considered while throughput rate is assumed to be given. However, since batch size affects the capacity of the bottleneck machine, the throughput rate may not be constant. As the batch size increases, the frequency of the setup decreases. The saved setup time can be transferred to processing time, which results in higher throughput. But, the larger batch size may also result in longer lead time and larger WIP inventory level. This paper presents an alternative method to determine batch size at the bottleneck machine in a manufacturing line. A linear search algorithm is introduced to find optimal throughput rate and batch size at the same time. Numerical examples are provided to see how the proposed method works and to investigate the effects of some parameters.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.547-554
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• 2021

In order to quickly and accurately diagnose pneumonia on a chest X-ray image, different batch sizes of 4, 8, 16, and 32 were applied to the same Xception deep learning model, and modeling was performed 3 times, respectively. As a result of the performance evaluation of deep learning modeling, in the case of modeling to which batch size 32 was applied, the results of accuracy, loss function value, mean square error, and learning time per epoch showed the best results. And in the accuracy evaluation of the Test Metric, the modeling applied with batch size 8 showed the best results, and the precision evaluation showed excellent results in all batch sizes. In the recall evaluation, modeling applied with batch size 16 showed the best results, and for F1-score, modeling applied with batch size 16 showed the best results. And the AUC score evaluation was the same for all batch sizes. Based on these results, deep learning modeling with batch size 32 showed high accuracy, stable artificial neural network learning, and excellent speed. It is thought that accurate and rapid lesion detection will be possible if a batch size of 32 is applied in an automatic diagnosis study for feature extraction and classification of pneumonia in chest X-ray images using deep learning in the future.

• Journal of the Korea Society for Simulation
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• v.5 no.2
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• pp.59-72
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• 1996

The purpose of this study is to make an improvement to the batch-means method, which is a procedure to construct a confidence interval(c.i.) for the steady-state process mean of a stationary simulation output process. In the batch-means method, the data in the output process are grouped into batches. The sequence of means of the data included in individual batches is called a batch-menas process and can be treated as an independently and identically distributed set of variables if each batch includes sufficiently large number of observations. The traditional batch-means method, therefore, uses a batch size as large as possible in order to. destroy the autocovariance remaining in the batch-means process. The c.i. prodedure developed and empirically tested in this study uses a small batch size which can be well fitted by a simple ARMA model, and then utilizes the dependence structure in the fitted model to correct for bias in the variance estimator of the sample mean.

• Journal of the Korean Ceramic Society
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• v.15 no.4
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• pp.185-192
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• 1978

The effects of slag prticle size, $Na_2SO_4$ addition and sulphide in slag to the foam formation was investigated. This investigation showed that the slag particle size and the amount of $Na_2O$ had produced effects on the size of the foam, foam distribution and firing temperatures. In addition to that the amount of sulphide loss during the firing was controlling factor for the foam formation. The smaller slag particles and higher firing temperatures increased the loss of sulphide in the slag. The addition of $Na_2SO_4$ in the slag batch was likely to inhibit the foam formation. The larger slag particles resutled in the larger foam size and tended to be increasing the batch firing temperature. The main constituents of slag formed glass consisted of wallstonite and glassy phase. It is believed that the controlling the slag particle size and the amount of $Na_2SO_4$ in the slag batch will probably be main factors in foam formation.

• Applied Chemistry for Engineering
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• v.10 no.8
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• pp.1180-1185
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• 1999

Modispersed silica fine particles were produced from the hydrolysis of $Si(OC_2H_5)_4$ by using batch-semibatch mixed systems. Four types of mixed process, i.e., batch-batch, batch-semibatch, semibatch-batch, semibatch-semibatch, were used in order to measure mean particle size, particle size distribution, yield, and packing density. As a result of the test, silica particles prepared by semibatch-semibatch process were larger than those prodeced from any other systems in particle size and yield. On the other hand, silica particles prepared by batch-semibatch process were better than those produced from any other systems in particle size distribution and packing density. Especially, it was found that particle size of $SiO_2$ prepared by semibatch-batch process decreased with increasing the reaction time. Therefore, batch-semibatch process was a successful method for controlling the size, i.e., a narrow distribution of a particle size which ranges to several microns.