• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1718-1723
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• 1991

The determination of a production sequence is an important problem in a batch process operation. In this paper a new algorithm for a near optimal production sequence of N product in an M unit serial multiproduct batch process is proposed. The basic principle is the same as that of Johnson's algorithm for two-unit UIS system. Test results on a number of selected examples exhibit the superiority over previously reported results. In addition, a tabulation technique is presented to calculate the makespan of a given sequence of production for all processing units under UIS mode.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.281-283
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• 2006

In this paper, we consider the problem of batch processing of orders, where either a single order or a pair of orders which satisfies specific conditions may be grouped in the same batch. The objective of the problem is to minimize the number of batches formed to accommodate all orders. We prose an approach based on a Known algorithm proven to be optimal for special class of problems with tree structure and show the approach to have the worst case ratio of $2-{\frac{2}{n}}$

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.1
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• pp.37-50
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• 2000

We are concerned with estimating the average performance of a re-entrant line with single-job machines and batch machines. The system has multiclass jobs, which will be processed in predetermined routes. An analytical approach may be intractable since the system would not be modeled by product form queueing networks due to the inclusion of batch machines and the consideraton of multiclass jobs which have different processing times. We propose an approximation method based on the Mean Value Analysis(MVA). Our method obtains the mean walting time in each buffer of a workstation and the mean cycle time using the MVA and heuristics. numerical experiments show that the errors of our method are within 5% compared with simulation.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.475-480
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• 2023

When modeling a sensor system mathematically, we assume that the sensor noise is Gaussian and white to simplify the model. If this assumption fails, the performance of the sensor model-based controller or estimator degrades due to incorrect modeling. In practice, non-Gaussian or non-white noise sources often arise in many digital sensor systems. Additionally, the noise parameters of the sensor model are not known in advance without additional noise statistical information. Moreover, disturbances or high nonlinearities often cause unknown sensor modeling errors. To estimate the uncertain noise and model parameters of a sensor system, this paper proposes an iterative batch calibration method using data-driven machine learning. Our simulation results validate the calibration performance of the proposed approach.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.2
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• pp.133-140
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• 2018

In this paper, we develop an automated software for in-plane structural analysis of composite laminated structures. The developed software supports various failure criteria and reports the analysis results considering user's convenience. It also provides batch job analysis function based on parallel processing technique. To verify the performance of the software, we compared margin of safety(MS) calculated in the software to those obtained from in-house method and the specimen experiment. As a result of comparisons, there was an error of less than 0.01 in the in-house method and it is within about ${\pm}10%$ with the specimen experiment. In addition, we confirmed the improvement of execution speed of batch job analysis based on parallel processing technique.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.1
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• pp.1-13
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• 2002

In Distributed Batch-processing Systems (DBSs), CPU-intensive jobs are automatically transferred and executed using idle computers across a network, there by increasing the resource usage and throughput. To be successful, the systems must guarantee the scalablility, fault-tolerance, and flexibility of dynamic configurations. In practice, however, it is very difficult to provide such capabilities in a non-deterministic environment in which the available set of resources is unpredictable because of network failures, computer failures, or voluntary withdrawal from a pool by a machine owner. In this paper, we present the design and implementation of the J-JDBS (Jini-based Java Distributed Batch-processing System) system which is based on the core Jini services like Discovery service, Lookup service, Lease service and etc. We show that the Jini core services can be very effectively used to build reliable, scalallle, fault-tolerant, and flexible DBS systems with little effort.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.547-549
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• 2021

In Artificial Intelligence analysis, the process of creating a model and verifying it is a task that requires computational processing time because it is Batch Processing performed with already generated data. We need to model, validate, and predict real-time data, such as stocks and defense information, with data generated directly in front of us. As a solution to this, we solve it by applying techniques to segment the data required for artificial intelligence modeling tasks in order of time processing and distribute the data across multiple processes.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.9
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• pp.727-735
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• 2003

The determination of completion time constitutes an important step in developing algorithms for short-term production scheduling. Existing completion time algorithms of multi-product batch processes often assume that intermediate storage tanks are always available when it is needed. In this paper, we consider a serial multi-product batch plants with ZW and NIS policy intermediate storge tank. We limit storage residence time and allocated products in the intermediate storage tank to minimize the quality change of the stored material as well as to reduce the clearing time during product change. A set of recurrence relations is suggested to calculate the completion times for the given batch sequence of products on all processing units. Also analytic solution for the suggested model is solved by a graphical method. The effectiveness of these results is proved by a few illustrative examples.

• Management Science and Financial Engineering
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• v.9 no.2
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• pp.47-68
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• 2003

We consider the customer order scheduling problem with job capacity restriction where the number of jobs in the shop at the same time is fixed. In the customer order scheduling problem, each job is part of some batch (customer order) and the composition of the jobs (product) in the batch is pre-specified. The objective function is associated with the completion time of the batches instead of the completion time of the jobs. We first summarize the known results for the general customer order scheduling problems. Then, we establish some new properties for the problems with job capacity restriction. For the case of unit processing time with the objective of minimizing makespan, we develop a polynomial-time optimal procedure for the two machine case. For the same problem with a variation of no batch alternation, we also develop a polynomial-time optimal procedure. Then, we show that the problems with the objectives of minimizing makespan and minimizing average batch completion time become NP-hard when there exist arbitrary number of machines. Finally, We propose optimal solution procedures for some special cases.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.23-32
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• 2020

Many small and medium-sized manufacturing companies process various product types to respond different customer orders in a single production line. To improve their productivity, they often apply batch processing while considering various product types, constraints on batch sizes and setups, and due date of each order. This study introduces a batch scheduling heuristic for a production line with multiple product types and different due dates of each order. As the process times vary due to the different batch sizes and product types, a recursive equation is developed based on a flow line model to obtain the upper bound on the completion times with less computational complexity than full computation. The batch scheduling algorithm combines and schedules the orders with same product types into a batch to improve productivity, but within the constraints to match the due dates of the orders. The algorithm incorporates simple and intuitive principles for the purpose of being applied to small and medium companies. To test the algorithm, two case studies are introduced; a high pressure coolant (HPC) manufacturing line and a press process at a plate-type heat exchanger manufacturer. From the case studies, the developed algorithm provides significant improvements in setup frequency and thus convenience of workers and productivity, without violating due dates of each order.