• 산업공학
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• 제15권2호
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• pp.189-198
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• 2002

Simulated Annealing(SA) approach has been successfully applied to the combinatorial optimization problems with NP-hard complexity. To apply an SA algorithm to specific problems, generic parameters as well as problem-specific parameters must be determined. To overcome the embedded nature of SA, long computational time, some studies suggested the parameter design methods of determining SA related parameters. In this study, we propose a new parameter design approach based on robust design method. To show the effectiveness of the proposed method, the extensive computation experiments are conducted on the mixed-model sequencing problems.

• 한국항공운항학회지
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• 제22권3호
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• pp.8-14
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• 2014

Arrival management is a tool which provides efficient flow of traffic and reduces ATC workload by determining aircraft's sequence and schedules while they are in cruise phase. As a decision support tool, arrival management should advise on air traffic control service based on the understanding of human factor of its user, air traffic controller. This paper proposed a prediction model for air traffic controller sequencing strategy by analyzing the historical trajectory data. Statistical analysis is used to find how air traffic controller decides the sequence of aircraft based on the speed difference and the airspace entering time difference of aircraft. Logistic regression was applied for the proposed model and its performance was demonstrated through the comparison of the real operational data.

• 디지털콘텐츠학회 논문지
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• 제10권2호
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• pp.259-268
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• 2009

e-러닝 기술 표준안으로서 ADL의 SCORM 2004가 채택한 시퀀싱은 IMS SS(Simple Sequencing)을 기반으로 하며 학습 콘텐츠에서 사용될 수 있는 시퀀싱 행위 중 비교적 간단한 일부만을 정의하고 있으나 실제 시퀀싱 구현 방법은 이름과는 달리 간단하지 않다. 따라서 한국적 이러닝 콘텐츠 개발 환경에서는 LSAL 등에서 제공하는 SCORM 시퀀싱 템플릿을 그대로 사용하거나 필요에 따라 일부 편집하는 형태로 SCORM 시퀀싱을 구현하는 실정이며 일반적으로 기존 SCORM 시퀀싱 템플릿 중의 하나에 개발된 콘텐츠를 삽입하여 콘텐츠 패키지를 구성하는 방법을 사용하고 있는 실정이다. 본 연구에서는 LSAL, ADL, Xerceo 등에서 제공하는 SCORM 시퀀싱을 위한 기본 템플릿과 기존의 국내 SCORM 시퀀싱 모델을 분석하고 이를 바탕으로 한국적 현실에 부합하는 새로운 SCORM 시퀀싱 모델을 제시하였다. 또한 제시한 모델을 국내에서 활용중인 학습 콘텐츠에 적용하여 구체적 시퀀싱 템플릿과 콘텐츠 샘플들을 개발하였다. 본 연구의 결과물은 SCORM 시퀀싱 구현에 어려움을 겪고 있는 콘텐츠 개발자가 국내 환경에 적합하면서도 보다 세련된 형태의 SCORM 시퀀싱을 구현하기 위한 참조 모델로서 활용할 수 있다.

• 한국국방경영분석학회지
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• 제31권2호
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• pp.28-44
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• 2005

The previous studies approach the field artillery fire scheduling problem as deterministic and do not explicitly include information on the potential scenario changes. Unfortunately, the effort used to optimize fire sequences and reduce the total time of engagement is often inefficient as the collected military intelligence changes. Instead of modeling the fire sequencing problem as deterministic model, we consider a stochastic artillery fire scheduling model and devise a solution methodology to integrate possible enemy attack scenarios in the evaluation of artillery fire sequences. The goal is to use that information to find robust solutions that withstand disruptions in a better way, Such an approach is important because we can proactively consider the effects of certain unique scheduling decisions. By identifying more robust schedules, cascading delay effects will be minimized. In this paper we describe our stochastic model for the field artillery fire sequencing problem and offer revised robust stochastic model which considers worst scenario first. The robust stochastic model makes the solution more stable than the general two-stage stochastic model and also reduces the computational cost dramatically. We present computational results demonstrating the effectiveness of our proposed method by EVPI, VSS, and Variances.

• 경영과학
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• 제13권1호
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• pp.13-27
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• 1996

This paper considers the sequencing problem in mixed model assembly lines with hybrid workstation types and sequence-dependent setup times. Computation time is often a critical factor in choosing a method of determining the sequence. We develop a mathematical formulation of the problem to minimize the overall length of a line, and present a tabu search technique which can provide a near optimal solution in real time. The proposed technique is compared with a genetic algorithm and a branch-and-bound method. Experimental results are reported to demonstrate the efficiency of the technique.

• 산업경영시스템학회지
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• 제36권3호
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• pp.95-108
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• 2013

The problem of constrained sequencing of a set of jobs on a conveyor system with the objective of minimizing setup cost is investigated in this paper. A setup cost is associated with extra material, labor, or energy required due to the change of attributes in consecutive jobs at processing stations. A finite set of attributes is considered in this research. Sequencing is constrained by the availability of conveyor junctions. The problem is motivated by the paint purge reduction problem at a major U.S. automotive manufacturer. We first model a diverging junction with a sequence-independent setup cost and predefined attributes as an assignment problem and this model is then extended for a more general situation by relaxing the initial assumptions in various ways.

• 대한산업공학회지
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• 제24권2호
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• pp.251-260
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• 1998

This paper deals with a simulated annealing neural network model for determining sequences of models inputted into a mixed model assembly line. We first present a energy function fitting to our problem, next determine the value of the parameters of the energy function using convergence ratio and the number of searched feasible solution. Finally we compare our model NMS with the modified Thomopoulos model. The result of the comparison shows that NMS and Thomopoulos offer a similar output in the problems having good smoothness.

• 산업경영시스템학회지
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• 제21권48호
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• pp.299-310
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• 1998

Actually mixed assembly line is mixed with open and close type workstation. This workstation is called hybridworkstation. The propose of this paper is to determine the sequencing of model that minimize line length for actual(hybridworkstation) mixed model assembly line. we developed three mathematical formulation of the problem to minimize the overall length of a line with hybrid station. Mathematical formulation classified model by operato schedule. Mixed model assembly line is combination program and NP-hard program. Thus computation time is often a critical factor in choosing a method of determining the sequence. This study suggests a tabu search technique which can provide a near optimal solution in real time and use the hill climbing heuristic method for selecting initial solution. Modified tabu search method is compared with MIP(Mixed Integer Program). Numerical results are reported to demonstrate the efficiency of the method.

• Journal of Korea Artificial Intelligence Association
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• 제2권1호
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• pp.7-14
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• 2024

In this paper, we explore the application of RNA-sequencing data and ensemble machine learning to predict lung cancer and treatment strategies for lung cancer, a leading cause of cancer mortality worldwide. The research utilizes Random Forest, XGBoost, and LightGBM models to analyze gene expression profiles from extensive datasets, aiming to enhance predictive accuracy for lung cancer prognosis. The methodology focuses on preprocessing RNA-seq data to standardize expression levels across samples and applying ensemble algorithms to maximize prediction stability and reduce model overfitting. Key findings indicate that ensemble models, especially XGBoost, substantially outperform traditional predictive models. Significant genetic markers such as ADGRF5 is identified as crucial for predicting lung cancer outcomes. In conclusion, ensemble learning using RNA-seq data proves highly effective in predicting lung cancer, suggesting a potential shift towards more precise and personalized treatment approaches. The results advocate for further integration of molecular and clinical data to refine diagnostic models and improve clinical outcomes, underscoring the critical role of advanced molecular diagnostics in enhancing patient survival rates and quality of life. This study lays the groundwork for future research in the application of RNA-sequencing data and ensemble machine learning techniques in clinical settings.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
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• pp.737-740
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• 1996

This paper suggests a boltzman machine neural network model to determine model input sequences in line balancing process of mixed model assembly line. We first present a proper energy function, next determine the value of parameters using simulation process.