In the construction of a white LED, the region of the red emission is a very important factor. Red light emitting materials play an important role in improving the color rendering index of commercial lighting. These materials also increase the color gamut of display products. Therefore, the development of novel phosphors with red emission and the study of color tuning are actively underway to improve product quality. In the present study, heuristic algorithms were used to search for phosphors capable of increasing the color rendering index and color gamut. Using a heuristic algorithm, the phosphors that were identified were SrGe4O9:Mn4+ and BaGe4O9:Mn4+. Emission spectra study confirmed that these phosphors emit light in the deep red wavelength region, which can fulfill the requirement for the improvement in color rendering index and color gamut for a white LED.

This paper proposes an improved standard genetic algorithm (GA) of making a near optimal schedule for integrated process planning and scheduling problem (IPPS) considering tool flexibility and tool related constraints. Process planning involves the selection of operations and the allocation of resources. Scheduling, meanwhile, determines the sequence order in which operations are executed on each machine. Due to the high degree of complexity, traditionally, a sequential approach has been preferred, which determines process planning firstly and then performs scheduling independently based on the results. The two sub-problems, however, are complicatedly interrelated to each other, so the IPPS tend to solve the two problems simultaneously. Although many studies for IPPS have been conducted in the past, tool flexibility and capacity constraints are rarely considered. Various meta-heuristics, especially GA, have been applied for IPPS, but the performance is yet satisfactory. To improve solution quality against computation time in GA, we adopted three methods. First, we used a random circular queue during generation of an initial population. It can provide sufficient diversity of individuals at the beginning of GA. Second, we adopted an inferior selection to choose the parents for the crossover and mutation operations. It helps to maintain exploitation capability throughout the evolution process. Third, we employed a modification of the hybrid scheduling algorithm to decode the chromosome of the individual into a schedule, which can generate an active and non-delay schedule. The experimental results show that our proposed algorithm is superior to the current best evolutionary algorithms at most benchmark problems.

This research focused on deciding optimal manufacturing WIP (Work-In-Process) limit for a small production system. Reducing WIP leads to stable capacity, better manufacturing flow and decrease inventory. WIP is the one of the important issue, since it can affect manufacturing area, like productivity and line efficiency and bottlenecks in manufacturing process. Several approaches implemented in this research. First, two strategies applied to decide WIP limit. One is roulette wheel selection and the other one is elite strategy. Second, for each strategy, JIT (Just In Time), CONWIP (Constant WIP), Gated Max WIP System and CWIPL (Critical WIP Loops) system applied to find a best material flow mechanism. Therefore, pull control system is preferred to control production line efficiently. In the production line, the WIP limit has been decided based on mathematical models or expert’s decision. However, due to the complexity of the process or increase of the variables, it is difficult to obtain optimal WIP limit. To obtain an optimal WIP limit, GA applied in each material control system. When evaluating the performance of the result, fitness function is used by reflecting WIP parameter. Elite strategy showed better performance than roulette wheel selection when evaluating fitness value. Elite strategy reach to the optimal WIP limit faster than roulette wheel selection and generation time is short. For this reason, this study proposes a fast and reliable method for determining the WIP level by applying genetic algorithm to pull system based production process. This research showed that this method could be applied to a more complex production system.

본 연구에서는 철근콘크리트 건물에 대한 유전자 알고리즘 기반의 최적구조설계기법을 제시하고자 한다. 목적함수는 구조 물의 비용과 이산화탄소 배출량을 동시에 각각 최소화하는 것이다. 비용 및 인산화탄소 배출량은 구조설계안에서 얻을 수 있는 단면치수, 부재길이, 재료강도, 철근량 등과 같은 설계정보를 통해 계산한다. 즉, 구조물의 물량을 기초로 하여 비용과 이산화탄소 배출량을 평가한다. 재료의 운반, 시공 및 건물 운영 단계에서 발생하는 비용 및 이산화탄소 배출량은 본 연구에 서 제외한다. 제약조건은 철근콘크리트 건물을 구성하는 기둥과 보 부재의 강도조건과 층간변위조건이 고려된다. 제약조건 을 평가하기 위해 OpenSees를 활용한 선형정적해석이 수행된다. 제약조건을 만족시키면서 목적함수에 대해 최소의 값을 제 시하는 설계안을 찾기 위해 유전자 알고리즘이 사용된다. 제시한 알고리즘의 적용성을 검증하기 위해 4층 철근콘크리트 모 멘트 골조 예제에 제시하는 기법을 적용하여 검증한다.

The genetic algorithm (GA), one of the artificial intelligence (AI), is developed based on Darwin's theory of evolution, i.e., the mating of randomly selected objects. If more optimal solution is generated, then it is better to repeat the process of setting the optimum value. In this paper, the method of background music using the genetic algorithm is exploited when the computer game is executed each time. As a result, it has created several music that can be used in the actual game, and it could be confirmed that the other music that is created is different music when performed each time.

본 연구에서는 철골모멘트골조의 보-힌지 붕괴모드를 유도하는 최적 내진설계기법을 제안한다. 이는 유전자알고리즘을 사용하며, 기둥의 소성힌지 발생을 억제하는 제약조건을 설정하여 보-힌지 붕괴모드를 유도한다. 제안하는 기법은 구조물량를 최소화하고 에너지소산능력을 최대화하는 목적함수를 사용한다. 제안하는 기법은 9층 철골모멘트골조 예제 적용을 통해 검증한다. 예제 적용을 통해 철골모멘트골조의 보-힌지 붕괴모드를 유도하기 위해 요구되는 기둥-보 강도비를 평가한다. 패널존에 대한 3가지 모델링 기법을 각각 적용하여 모델링 조건에 따른 휨강도비 영향이 추가적으로 검토된다.

This paper considers the allocation and engagement scheduling problem of interceptor missiles, and the problem was formulated by using MIP (mixed integer programming) in the previous research. The objective of the model is the maximization of total intercept altitude instead of the more conventional objective such as the minimization of surviving target value. The concept of the time window was used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. The MIP formulation of the problem is very complex due to the complexity of the real problem itself. Hence, the finding of an efficient optimal solution procedure seems to be difficult. In this paper, an efficient genetic algorithm is developed by improving a general genetic algorithm. The improvement is achieved by carefully analyzing the structure of the formulation. Specifically, the new algorithm includes an enhanced repair process and a crossover operation which utilizes the idea of the PSO (particle swarm optimization). Then, the algorithm is throughly tested on 50 randomly generated engagement scenarios, and its performance is compared with that of a commercial package and a more general genetic algorithm, respectively. The results indicate that the new algorithm consistently performs better than a general genetic algorithm. Also, the new algorithm generates much better results than those by the commercial package on several test cases when the execution time of the commercial package is limited to 8,000 seconds, which is about two hours and 13 minutes. Moreover, it obtains a solution within 0.13 ~33.34 seconds depending on the size of scenarios.

선박 및 플랜트의 배관은 제작부터 설치까지 일련의 과정을 모두 현장에서 하는 것이 아닌, 외부의 공장 또는 숍으로부터 배관의 제일 작은 요소인 스풀 배관을 제작하고, 이를 작업현장 또는 현장 근처의 공장에서 모듈화 또는 가설치 작업 및 현장에서 직접 설치작업을 통해 제작이 된다. 이 과정에서 스풀은 3D CAD를 기반으로 하는 것이 아닌 2D 도면을 기반으로 하기 때문에, 작업공간을 고려하지 못할 수 있다. 이러한 이유로 실제 설치작업 시 작업공간의 방해로 인한 공기의 지연을 발생 시킬 수 있다. 본 논문은 이러한 스풀 배관의 설치 시 또는 운용 및 유지보수 시에 생길 수 있는 외부 구조물과의 스풀 위치에 관하여, 스풀 위치가 외부 구조물로부터 방해를 받지 않도록 하기 위한 방법으로 유전 알고리즘을 적용하여 스풀 위치를 결정하는 방법에 대해 제시하고자 한다.

PURPOSES : The Toll Collection System (TCS) operated by the Korea Expressway Corporation provides accurate traffic counts between tollgates within the expressway network under the closed-type toll collection system. However, although origin-destination (OD) matrices for a travel demand model can be constructed using these traffic counts, these matrices cannot be directly applied because it is technically difficult to determine appropriate passenger car equivalent (PCE) values for the vehicle types used in TCS. Therefore, this study was initiated to systematically determine the appropriate PCE values of TCS vehicle types for the travel demand model. METHODS: To search for the appropriate PCE values of TCS vehicle types, a traffic demand model based on TCS-based OD matrices and the expressway network was developed. Using the traffic demand model and a genetic algorithm, the appropriate PCE values were optimized through an approach that minimizes errors between actual link counts and estimated link volumes. RESULTS : As a result of the optimization, the optimal PCE values of TCS vehicle types 1 and 5 were determined to be 1 and 3.7, respectively. Those of TCS vehicle types 2 through 4 are found in the manual for the preliminary feasibility study. CONCLUSIONS: Based on the given vehicle delay functions and network properties (i.e., speeds and capacities), the travel demand model with the optimized PCE values produced a MAPE value of 37.7%, RMSE value of 17124.14, and correlation coefficient of 0.9506. Conclusively, the optimized PCE values were revealed to produce estimates of expressway link volumes sufficiently close to actual link counts.

This paper considers a parallel-machine scheduling problem with dedicated and common processing machines using GA (Genetic Algorithm). Non-identical setup times, processing times and order lot size are assumed for each machine. The GA is proposed to minimize the total-tardiness objective measure. In this paper, heuristic algorithms including EDD (Earliest Due-Date), SPT (Shortest Processing Time) and LPT (Longest Processing Time) are compared with GA. The effectiveness and suitability of the GA are derived and tested through computational experiments.

Recently, a concept of damped outrigger system has been proposed for tall buildings. Structural characteristics and design method of this system were not sufficiently investigated to date. In this study, control performance of damped outrigger system for building structures subjected to seismic excitations has been investigated. And optimal design method of damped outrigger system has been proposed using multi-objective genetic algorithm. To this end, a simplified numerical model of damped outrigger system has been developed. State-space equation formulation proposed in previous research was used to make a numerical model. Multi-objective genetic algorithms has been employed for optimal design of the stiffness and damping parameters of the outrigger damper. Based on numerical analyses, it has been shown that the damped outrigger system control dynamic responses of the tall buildings subjected to earthquake excitations in comparison with a traditional outrigger system.

건물의 경우, 용도 변경에 따른 중력하중 변화, 시공 단계에 따라 중력하중 변화 등이 구조물 시스템에 영향을 미친다. 따라서, 본 연구에서는 시스템 식별 변수 설정에 있어 기존에 강성만을 변수로 설정한 방법에 추가적으로 질량을 변수로 설정하여 시스템을 식별하는 기법을 제안한다. 계측한 동특성과 FE모델에서 추출한 동특성 간의 차이를 최소화하여 변수를 탐색하게 된다. 최소화 기법으로 변형 유전 알고리즘을 적용하였다. 보다 전역적 해탐색을 위해 변형 유전 알고리즘은 더 넓은해 탐색 공간에서 해를 찾는다. 철골 보 구조물의 시뮬레이션을 통해 본 연구가 제시한 기법을 검증하였고 변형 유전 알고리즘과 기존의 단순 유전 알고리즘의 성능을 비교하였다. 또한, 강성 식별만을 수행한 기존 연구의 방법과 본 연구가 제시한 기법간의 차이를 비교하였다.

Supply Chain Management(SCM) is getting important, because size of the company is getting bigger and the kinds of product are various. In the case of manufacturing corporation, for the optimization of SCM, we have to make production and distribution plan by considering the various fluctuation in the aspect of integration. In this paper, first, It proposed the reasonable operational way of the SCM about when the customer’s demanding is various and demanding expectation fluctuates in capacity standardization of producer stage. Second, the paper proposed the management way for demanding by considering confirmed demanding information, related inventory expense and demanding shortage expense when we make production and distribution plan. The paper applied the genetic algorithm proved for current usefulness. it proposed the optimal operational way for SCM by dividing into 2 ways for dealing with the duration of confirmed demanding information and various fluctuation.

The aim of this study is to design an intelligent pattern-based real-time trading system (PRTS) using rough set analysis of technical indicators, dynamic time warping (DTW), and genetic algorithm in stock futures market. Rough set is well known as a data-mining tool for extracting trading rules from huge data sets such as real-time data sets, and a technical indicator is used for the construction of the data sets. To measure similarity of patterns, DTW is used over a given period. Through an empirical study, we identify the ideal performances that were profitable in various market conditions.

This study develops a dynamic scheduling model for parallel machine scheduling problem based on genetic algorithm (GA). GA combined with discrete event simulation to minimize the makespan and verifies the effectiveness of the developed model. This research consists of two stages. In the first stage, work sequence will be generated using GA, and the second stage developed work schedule applied to a real work area to verify that it could be executed in real work environment and remove the overlapping work, which causes bottleneck and long lead time. If not, go back to the first stage and develop another schedule until satisfied. Small size problem was experimented and suggested a reasonable schedule within limited resources. As a result of this research, work efficiency is increased, cycle time is decreased, and due date is satisfied within existed resources.

Release planning in a software product line (SPL) is to select and assign the features of the multiple software products in the SPL in sequence of releases along a specified planning horizon satisfying the numerous constraints regarding technical prece- dence, conflicting priorities for features, and available resources. A greedy genetic algorithm is designed to solve the problems of release planning in SPL which is formulated as a precedence-constrained multiple 0-1 knapsack problem. To be guaranteed to obtain feasible solutions after the crossover and mutation operation, a greedy-like heuristic is developed as a repair operator and reflected into the genetic algorithm. The performance of the proposed solution methodology in this research is tested using a fractional factorial experimental design as well as compared with the performance of a genetic algorithm developed for the software release planning. The comparison shows that the solution approach proposed in this research yields better result than the genetic algorithm.

본 논문에서는 유전알고리즘을 사용하여 철근콘크리트 구조물의 최적 지진설계를 효율적으로 수행하기 위해 클러스터를 사용하는 경우 확장성을 확인하였다. 클러스터를 구성하는 코어프로세서의 개수를 증가시키면서 유전알고리즘의 각 세대에 소요되는 시간의 감소를 관찰하였다. 단일 퍼스널 컴퓨터의 구성을 분류한 후, wall-clock time과 암달의 법칙으로 예상된 값을 비교하여 예상되었던 병목현상을 확인하였다. 이에 클러스터의 확장성에서 복합적인 요인에 의한 경향을 확인할 수 있었다. 병목현상의 물리적인 요인과 알고리즘 측면에서의 요인을 구분하기 위해 유전알고리즘의 개채수를 나누어 실험을 수행하여 결과를 확인하였다.

Reduction of microvibration is regarded as important in high-technology facilities with high precision equipments. In this paper, smart control technology is used to improve the microvibration control performance. Mr damper is used to make a smart base isolation system amd fuzzy logic control algorithm is employed to appropriately control the MR damper. In order to develop optimal fuzzy control algorithm, a multi-objective genetic algorithm is used in this study. As an excitation, a train-induced ground acceleration is used for time history analysis and three-story example building structure is employed. Microvibration control performance of passive and smart base isolation systems have been investigated in this study. Numerical simulation results show that the multi-objective genetic algorithm can provide optimal fuzzy logic controllers for smart base isolation system and the smart control system can effectively reduce microvibration of a high-technology facility subjected to train-induced excitation.

Release planning for incremental software development is to select and assign features in sequence of releases along a specified planning horizon. It includes the technical precedence inherent in the features, the conflicting priorities as determined by the representative stakeholders, and the balance between required and available resources. The complexity of this consideration is getting more complicated when planning releases in software product lines. The problem is formulated as a precedence-constrained multiple 0-1 knapsack problem. In this research a genetic algorithm is developed for solving the release planning problems in software product lines as well as tests for the proposed solution methodology are conducted using data generated randomly.

플라스틱 사출 제품은 다양한 가전제품과 하이테크 제품에 널리 사용되고 있다. 그러나 현재의 치열한 경쟁적 비즈니스 환경에서 플라스틱 사출 제품 제조업자들은 고객을 만족시키면서 경쟁력을 얻기 위하여 다른 경쟁자들보다 먼저 새로운 제품을 시장에 출시하고 신제품의 개발기간을 줄이기 위한 노력을 할 여유가 부족하다. 따라서 무한경쟁의 시장에서 살아남기 위해서는 제조업자들은 시장 마켓 점유를 빠르게 올리는 것과 동시에 제품의 가격 경쟁력을 가져야 한다. 특징기반