• 한국항행학회논문지
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• 제19권1호
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• pp.53-59
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• 2015

노드들 간의 설치비용과 트래픽 요구량이 주어졌을 때, 이 조건을 만족하는 메쉬망을 설계하는 주요 고려사항으로는 설계시간, 비용, 지연, 신뢰성등 여러 가지가 있으며, 일반적으로 설계시간을 줄이고, 비용은 작게, 지연은 적게, 신뢰성이 높은 메쉬망을 설계하여야 한다. 설계시간에 대한 문제는 Aaron Kershenbaum이 제안한 MENTOR (mesh network topology optimization and routing) 알고리즘에 의해 최소화를 이루는데 성공하였지만 비용, 지연, 신뢰성에는 여전히 문제가 남아있다. 본 논문에서는 MENTOR의 설계시간의 장점을 유지하면서 다른 성능인자들을 만족시킬 수 있는 새로운 망설계 알고리즘을 제안하고자 한다. 제안된 알고리즘의 설계결과는 MENTOR 알고리즘의 성능인자들보다 개선되었음을 보여주었다.

• Journal of Biosystems Engineering
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• 제7권1호
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• pp.42-52
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• 1982

This study was intended to develop a cost function for the natural air in-bin drying: system which could lead to an optimization of the drying system cost. Based on the cost function developed, a series of simulated drying tests were conducted with 10-year weather data (1970~1979) for 7 different regions by applying an appropriate levels of system factors. System performance factors treated in this study were initial moisture content, airflow rate, bin diameter and grain depth. An optimization procedure to find the least cost system was developed as follows: First, the worst year of the past decade was determined in consideration of the dryiang time and maximum dry matter loss. Second, the minimum airflow rate for a fixed bin diameter and grain depth was determined. Third, the optimum grain depth was found for the minimum airflow rate with different initial moisture contents and bin diameters. The results obtained in this study are summarized as follows: 1. The optimization procedure developed in this study was able to reduce the time and efforts significantly. 2. Optimum values of drying parameters including airflow rate, grain depth, and fan size were determined for different initial moisture contents and bin diameters in each region. The results are shown in Tables 3 to 9. 3. Optimum grain depths decreased as the initial moisture content and airflow rate increased. 4. Drying time for the least cost system should be reduced with higher initial moisture content and lower drying potential to prevent grain spoilage. 5. The fixed cost was 65 to 75 percent of the total system cost and the variable cost was 25 to 35 percent. To reduce the fixed cost it is desirable to use a drying bin 2 or 3 times a year.

• 대한안전경영과학회지
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• 제8권3호
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• pp.67-75
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• 2006

Scheduling of dismantling old research reactor need to consider time, cost and safety for the worker. The biggest issue when dismantling facility for research reactor is safety for the worker and cost. Large portion of a budget is spending for the labor cost. To save labor cost for the worker, reducing a lead time is inevitable. Several algorithms applied to reduce read time, and safety considered as the most important factor for this project. This research presents three different dismantling scheduling scenarios. Best scenario shows the specific scheduling for worker and machine, so that it could save time and cost.

• 한국컴퓨터정보학회논문지
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• 제20권12호
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• pp.75-82
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• 2015

Generally, the optimal solution of assignment problem can be obtained by Hungarian algorithm of two-sided optimization with time complexity $O(n^4)$. This paper suggests one-sided optimal assignment and swap optimization algorithm with time complexity $O(n^2)$ can be achieve the goal of two-sided optimization. This algorithm selects the minimum cost for each row, and reassigns over-assigned to under-assigned cell. Next, that verifies the existence of swap optimization candidates, and swap optimizes with ${\kappa}-opt({\kappa}=2,3)$. For 27 experimental data, the swap-optimization performs only 22% of data, and 78% of data can be get the two-sided optimal result through one-sided optimal result. Also, that can be improves on the solution of best known solution for partial problems.

• International Journal of Aeronautical and Space Sciences
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• 제17권2호
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• pp.268-283
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• 2016

Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet new market demands while keeping the development time and cost to a minimum. Many researchers have studied the derivative design process, but these research efforts consider baseline and derivative designs together, while using the whole set of design variables. Therefore, an efficient process that can reduce cost and time for aircraft derivative design is needed. In this research, a more efficient design process is proposed which obtains global changes from local changes in aircraft design in order to develop aircraft derivatives efficiently. Sensitivity analysis was introduced to remove unnecessary design variables that have a low impact on the objective function. This prevented wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, uncertainty from the fidelity of analysis tools was considered in design optimization to increase the probability of optimization results. The Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were proposed to handle the uncertainty in aircraft conceptual design optimization. In this paper, Collaborative Optimization (CO) based framework with RBDO and PBDO was implemented to consider uncertainty. The proposed method was applied for civil jet aircraft derivative design that increases cruise range and the number of passengers. The proposed process provided deterministic design optimization, RBDO, and PBDO results for given requirements.

• 한국인터넷방송통신학회논문지
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• 제21권4호
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• pp.177-182
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• 2021

다양한 원재료를 혼합하여 원하는 성분 함유량을 가진 제품을 최소의 비용으로 생산하는 혼합 최적화 문제에 대해 일반적으로 O(n⁴)의 수행 복잡도의 선형계획법을 적용하고 있다. 본 논문에서는 이 문제에 대해 O(n log n)복잡도로 해를 얻을 수 있는 휴리스틱 알고리즘을 제안한다. 제안된 알고리즘은 합금 강판에서 요구하는 성분들의 함유량 범위를 충족시키면서 최소의 원자재비용을 얻기 위해, 원재료 단가 오름차순으로 성분별 함유량을 충족시키도록 원재료 양을 결정하였다. 3가지 사례에 대해 적용한 결과 제안된 알고리즘은 O(n log n)복잡도로 단순한 결정기법을 적용하였음에도 불구하고, LP의 최적화 기법과 동일하거나 보다 좋은 해를 얻을 수 있었다.

• Earthquakes and Structures
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• 제7권3호
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• pp.271-294
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• 2014

In recent years, along with the advances made in performance-based design optimization, the need for fast calculation of response parameters in dynamic analysis procedures has become an important issue. The main problem in this field is the extremely high computational demand of time-history analyses which may convert the solution algorithm to illogical ones. Two simplifying strategies have shown to be very effective in tackling this problem; first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication, second, wavelet analysis of earthquake records decreasing the number of acceleration points involved in time-history loading. In this paper, we try to develop an efficient framework, using both strategies, to solve the performance-based multi-objective optimal design problem considering the initial cost and the seismic damage cost of steel moment-frame structures. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency (FEMA) recommended design specifications. The results from numerical application of the proposed framework demonstrate the capabilities of the framework in solving the present multi-objective optimization problem.

• Computers and Concrete
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• 제7권3호
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• pp.221-237
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• 2010

In this study 72 different high strength concrete (HSC) mixes were produced according to the Taguchi design of experiment method. The specimens were divided into four groups based on the range of their compressive strengths 40-60, 60-80, 80-100 and 100-125 MPa. Each group included 18 different concrete mixes. The slump and air-content values of each mix were measured at the production time. The compressive strength, splitting tensile strength and water absorption properties were obtained at 28 days. Using this data the Genetic Programming technique was used to construct models to predict mechanical properties of HSC based on its constituients. These models, together with the cost data, were then used with a Genetic Algorithm to obtain an HSC mix that has minimum cost and at the same time meets all the strength and workability requirements. The paper describes details of the experimental results, model development, and optimization results.

• 품질경영학회지
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• 제47권4호
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• pp.701-711
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• 2019

Purpose: The problem of optimizing redundancy allocation in multi-level systems is considered when each item in a multi-level system has alternative items with the same function. The number of redundancy of multi-level system is allocated to maximize the reliability of the system under path set and cost limitation constraints. Methods: Based on cost limitation and path set constraints, a mathematical model is established to maximize system reliability. Particle swarm optimization is employed for redundant allocation and verified by numerical experiments. Results: Comparing the particle swarm optimization method and the memetic algorithm for the 3 and 4 level systems, the particle swarm optimization method showed better performance for solution quality and search time. Particularly, the particle swarm optimization showed much less than the memetic algorithm for variation of results. Conclusion: The proposed particle swarm optimization considerably shortens the time to search for a feasible solution in MRAP with path set constraints. PS optimization is expected to reduce search time and propose the better solution for various problems related to MRAP.

• Industrial Engineering and Management Systems
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• 제6권2호
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• pp.136-145
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• 2007

The selection of suppliers and the determination of order quantities to be placed with those suppliers are important decisions in a supply chain. In this research, a non-linear mixed integer programming model is presented to select suppliers and determine the order quantities. The model considers the purchasing cost which takes into account quantity discount, the cost of transportation, the fixed cost for establishing suppliers, the cost for holding inventory, and the cost of receiving poor quality parts. The capacity constraints for suppliers, quality and lead-time requirements for the parts are also taken into account in the model. Since the purchasing cost, which is a decreasing step function of order quantities, introduces discontinuities to the non-linear objective function, it is not easy to employ traditional optimization methods. Thus, a heuristic algorithm, called particle swarm optimization (PSO), is used to find the (near) optimal solution. However, PSO usually generates initial solutions randomly. To improve the PSO solution quality, a heuristic procedure is proposed to find an initial solution based on the average unit cost including transportation, purchasing, inventory, and poor quality part cost. The results show that PSO with the proposed initial solution heuristic provides better solutions than those with PSO algorithm only.