• Journal of the Korea Society for Simulation
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• v.20 no.3
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• pp.49-58
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• 2011

The workloads of facilities and laborers are important for the capacity planning in a factory. They are always referenced whenever a factory develops a new product, increases the production quantity and makes a plan of new investment. There are many measuring methods for estimating the workload effectiveness of facilities and laborers. In this paper, various measuring methods including survey, work sampling, micro-motion study, data gathering from ERP system and simulation, are analyzed for comparing the accuracy of workload. This case study is conducted in a Korean company that produces semiconductor parts like leadframe and packaging substrate.

• Journal of the Korea Safety Management & Science
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• v.6 no.1
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• pp.269-281
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• 2004

This study aims to examine the effects of team member characteristics(change reception, work understand) and team process(potency, social support, workload sharing, communication and cooperation within the team) to team effectiveness(job commitment, job satisfaction and productivity), As the result, both team member characteristics and team process have positive relations with team effectiveness.

• Journal of Korean Society for Quality Management
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• v.20 no.2
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• pp.129-136
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• 1992

This paper aims to develope an algorithm to minimize the total production time, sum of group formation times and processing times, under the balanced workload among the machines by grouping parts with machine loading in FMS. The algorithm of this study is composed of four step procedures ; (1) Parts grouping by Group Technology(GT) (2) Minimizing total processing time in loading problem (3) Machine workload balancing, including above(2) (4) Group formation time, including above(3) For parts grouping, Rank Order Clustering(ROC) algorithm developed by King(1980) is used and this algorithm is programmed by using the MACRO functions of QUATTRO Pro, one of the spreadsheet packages. The structure for loading model is solved by using the Hyper-LINDO. As a case study, numerical examples are demonstrated to show the effectiveness of the proposed machine loading procedure.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.64
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• pp.1-20
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• 2001

In distributed databases, file replication and workload allocation are important design issues. This paper solves these two issues simultaneously, The primary objective is to minimize the system response time that consists of local processing and communication overhead on a local area network. Workload (query transactions) is assigned among any sites in proportion to the remaining file request service rate of the each server The problem is presented in the form of a nonlinear integer programming model. The problem is proved to be NP-complete and thus an efficient heuristic is developed by employing its special structure. To illustrate its effectiveness, it is shown that the proposed heuristic is based on the heuristic of a non-redundant allocation that was provided to be effective. The model and heuristics are likely to provide more effective distributed database designs.

• Industrial Engineering and Management Systems
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• v.11 no.1
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• pp.103-113
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• 2012

A yard in a container terminal is a temporary storage space before containers are loaded onto the target vessel or delivered to consignees. For improving the utilization of the space in the yard and the efficiency of loading and discharging operations, it is important that operation plans must be carefully constructed in advance. A heuristic method is suggested to solve operation space planning problems considering workloads on handling equipment as well as space availabilities. The operation plans in this paper includes quay crane (QC) schedules and space plans for multiple vessels considering the workload in the container yard of container terminals. This paper evaluates the effectiveness of a space planning method and the performance of a new QC scheduling method using a simulation model.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.8
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• pp.905-917
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• 1999

멀티미디어 시스템에서 수행되는 부하 중 상당 부분은 정해진 시간에 주기적으로 수행되어 연속 매체(continuous media)를 처리하는 주기 태스크들이다. 연속 매체들을 처리하는 두 주기 태스크의 수행 요청 시간(release time)간에 인공적인 위상(phase)을 줄 경우, 전체 부하의 처리 타이밍에 영향을 주게 되며, 특히, 적절한 위상을 부여할 경우 부하가 고르게 분산된다. 부하의 고른 분산은 태스크 간섭을 줄여 지터(jitter), 종료시한 초과(deadline miss), 그리고 긴 응답 시간(response time) 등의 문제를 해소시킨다. 본 논문에서는 새로운 위상 스케줄링(phased scheduling) 알고리즘 및 알고리즘을 개발하고 평가할 수 있는 실험 환경을 제안한다. 본 논문에 제시된 알고리즘은 저자들의 선행 논문 1 에서 제시한 알고리즘의 대안이다. 새로운 알고리즘은 정확하게 최적의 위상을 찾지는 않으나 기존 알고리즘보다 빠르게 수행되며 적용 범위가 넓다.Abstract A multimedia system consists of substantial amount of continuous media workload scheduled periodically at deterministic time points. Artificial phase between the invocation times of any two continuous media tasks affects the timing of the entire workload. A proper phase configuration distributes workload uniformly over time and reduces task interference that may otherwise result in jitter, deadline miss, and long response time. The objective of this paper is to work out a phased scheduling algorithm and to evaluate its effectiveness. The algorithm in this paper is an alternative approach to our previous work 1 . It is almost as accurate as the predecessor but two of three times faster in identifying the appropriate phase vector.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.45-49
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• 2022

The fourth industrial revolution, internet of things, and the expansion of online web services have increased an exponential growth and deployment in the number of cloud data centers (CDC). The cloud is emerging as new paradigm for delivering the Internet-based computing services. Due to the dynamic and non-linear workload and availability of the resources is a critical problem for efficient workload and resource management. In this paper, we propose the particle swarm optimization (PSO) based gated recurrent unit (GRU) neural network for efficient prediction the future value of the CPU and memory usage in the cloud data centers. We investigate the hyper-parameters of the GRU for better model to effectively predict the cloud resources. We use the Google Cluster traces to evaluate the aforementioned PSO-GRU prediction. The experimental shows the effectiveness of the proposed algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.56-63
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• 2015

Several fields of science have demanded large-scale workflow support, which requires thousands of CPU cores or more. In order to support such large-scale scientific workflows, high capacity parallel systems such as supercomputers are widely used. In order to increase the utilization of these systems, most schedulers use backfilling policy: Small jobs are moved ahead to fill in holes in the schedule when large jobs do not delay. Since an estimate of the runtime is necessary for backfilling, most parallel systems use user's estimated runtime. However, it is found to be extremely inaccurate because users overestimate their jobs. Therefore, in this paper, we propose a novel system for the runtime prediction based on workload-aware clustering with the goal of improving prediction performance. The proposed method for runtime prediction of parallel applications consists of three main phases. First, a feature selection based on factor analysis is performed to identify important input features. Then, it performs a clustering analysis of history data based on self-organizing map which is followed by hierarchical clustering for finding the clustering boundaries from the weight vectors. Finally, prediction models are constructed using support vector regression with the clustered workload data. Multiple prediction models for each clustered data pattern can reduce the error rate compared with a single model for the whole data pattern. In the experiments, we use workload logs on parallel systems (i.e., iPSC, LANL-CM5, SDSC-Par95, SDSC-Par96, and CTC-SP2) to evaluate the effectiveness of our approach. Comparing with other techniques, experimental results show that the proposed method improves the accuracy up to 69.08%.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.3
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• pp.213-221
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• 2003

In this paper, we consider a multi-objective part-machine grouping problem, in which part types have several alternative part routings and each part routing has a machining sequence. This problem is characterized as optimally determining part type sets and its corresponding machine cells such that the sum of inter-cell part movements and the sum of machine workload imbalances are simultaneously minimized. Due to the complexity of the problem, a two-stage heuristic algorithm is proposed, and experiments are shown to verify the effectiveness of the algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.4
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• pp.284-291
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• 2017

There is no doubt that advances in UAV technology have improved military performance. However, these advances require humans to adapt to new and complex operational systems. UAV has been rapidly expanding to a variety of fields such as reconnaissance, transportation, communication and aerial photographing recently. Also, with the development of UAV automation technology, one operator is able to supervisory-control multiple-UAVs. However, as the number of assigned UAV increases, the amount of information increases and this results in the workload of the operator increasing and deterioration in controlling performance. Accordingly, there is a need for a model to determine the level of overload an operator may encounter with regard to multiple-UAV but nationally this kind of research is currently lacking. Therefore, this paper provides an experimental platform for evaluating workload index effectiveness integrating multiple-UAV operational environments, GCS, and eye-tracking system followed by a limited survey of domestic and international studies of multi-UAV overload studies.