• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.35-47
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• 2005

This paper is to review a literature concerning Bus Transit Route Network Design(BTRNDP), to describe a future study direction for a systematic application for the BTRNDP. Since a bus transit uses a fixed route, schedule, stop, therefore an approach methodology is different from that of auto network design problem. An approach methodology for BTRNDP is classified by 8 categories: manual & guideline, market analysis, system analytic model. heuristic model. hybrid model. experienced-based model. simulation-based model. mathematical optimization model. In most previous BTRNDP, objective function is to minimize user and operator costs, and constraints on the total operator cost, fleet size and service frequency are common to several previous approach. Transit trip assignment mostly use multi-path trip assignment. Since the search for optimal solution from a large search space of BTRNDP made up by all possible solutions, the mixed combinatorial problem are usually NP-hard. Therefore, previous researches for the BTRNDP use a sequential design process, which is composed of several design steps as follows: the generation of a candidate route set, the route analysis and evaluation process, the selection process of a optimal route set Future study will focus on a development of detailed OD trip table based on bus stop, systematic transit route network evaluation model. updated transit trip assignment technique and advanced solution search algorithm for BTRNDP.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.3
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• pp.183-192
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• 2022

With the growth of the packaging industry, demand on the packaging printing comes in various forms. Customers' orders are diversifying and the standards for quality are increasing. Offset printing is mainly used in the packaging printing since it is easy to print in large quantities. However, productivity of the offset printing decreases when printing various order. This is because it takes time to change colors for each printing unit. Therefore, scheduling that minimizes the color replacement time and shortens the overall makespan is required. By the existing manual method based on workers' experience or intuition, scheduling results may vary for workers and this uncertainty increase the production cost. In this study, we propose an automated scheduling method of parallel offset printing process for packaging printing. We decompose the original problem into assigning and sequencing orders, and ink arrangement for printing problems. Vehicle routing problem and assignment problem are applied to each part. Mixed integer programming is used to model the problem mathematically. But it needs a lot of computational time to solve as the size of the problem grows. So guided local search algorithm is used to solve the problem. Through actual data experiments, we reviewed our method's applicability and role in the field.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.201-207
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• 2018

There is no known polynomial time algorithm for random-type quadratic assignment problem(RQAP) that is a NP-complete problem. Therefore the heuristic or meta-heuristic approach are solve the approximated solution for the RQAP within polynomial time. This paper suggests polynomial time algorithm for random type quadratic assignment problem (QAP) with time complexity of $O(n^2)$. The proposed algorithm applies one-to-one matching strategy between ascending order of sum of distance for each location and descending order of sum of quantity for each facility. Then, swap the facilities for reflect the correlation of distances of locations and quantities of facilities. For the experimental data, this algorithm, in spite of $O(n^2)$ polynomial time algorithm, can be improve the solution than genetic algorithm a kind of metaheuristic method.

• Journal of the Korea Society for Simulation
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• v.28 no.4
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• pp.21-32
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• 2019

Inspired by the social behavior models of a bird flock or fish school, particle swarm optimization (PSO) is a popular metaheuristic optimization algorithm and has been widely used from solving a complex optimization problem to learning a artificial neural network. However, PSO is difficult to apply to many real-life optimization problems involving stochastic noise, since it is originated in a deterministic environment. To resolve this problem, this paper incorporates a resampling method called the uncertainty evaluation (UE) method into PSO. The UE method allows the particles to converge on the accurate optimal solution quickly in a noisy environment by selecting the particles' global best position correctly, one of the significant factors in the performance of PSO. The results of comparative experiments on several benchmark problems demonstrated the improved performance of the propose algorithm compared to the existing studies. In addition, the results of the case study emphasize the necessity of this work. The proposed algorithm is expected to be effectively applied to optimize complex systems through digital twins in the fourth industrial revolution.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.7
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• pp.4999-5008
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• 2015

The application of efficient constraint handling technique is fundamental method to find better solutions in engineering optimization problems with constraints. In this research four of constraint handling techniques are used with a meta-heuristic optimization method, harmony search algorithm, and the efficiency of algorithm is evaluated. The sample problem for evaluation of effectiveness is one of the typical discrete problems, optimal pipe size design problem of water distribution system. The result shows the suggested constraint handling technique derives better solutions than classical constraint handling technique with penalty function. Especially, the case of ${\varepsilon}$-constrained method derives solutions with efficiency and stability. This technique is meaningful method for improvement of harmony search algorithm without the need for development of new algorithm. In addition, the applicability of suggested method for large scale engineering optimization problems is verified with application of constraint handling technique to big size problem has over 400 of decision variables.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.203-210
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• 2014

This study proposes adaptive decision making method having feed-back structure of regression and simulation models to support the quick decision making of production managers by managing and integrating the mutual relationship among historical data. For that, from historical data that have extracted and accumulated from each process, we first selected major constraint resources that are used as independent variables in regression model. The regression model is designed by using the dependent variables (objectives) that defined above by managers and independent variables selected in previous step and simulation model that are composed of constraint resources is designed. In process of simulation run, we obtain the multiple feasible solutions (alternatives) by using meta-heuristic method. Each solution is substituted by regression equation and we found the optimal solution that is minimum of difference between values obtained by regression model and simulation results. The optimal solution is delivered and incorporated to production site and current operation results from production site is used to generate new regression model after that time.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.41-48
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• 2011

In feature selection, the selective evaluation scheme for Ant Colony Optimization(ACO) has recently been proposed, which reduces computational load by excluding unnecessary or less promising candidate solutions from the actual evaluation. Its superiority was supported by experimental results. However the experiment seems to be not statistically sufficient since it used only one dataset. The aim of this paper is to analyze convergence characteristics of the selective evaluation scheme and to make the conclusion more convincing. We chose three datasets related to handwriting, medical, and speech domains from UCI repository whose feature set size ranges from 256 to 617. For each of them, we executed 12 independent runs in order to obtain statistically stable data. Each run was given 72 hours to observe the long-time convergence. Based on analysis of experimental data, we describe a reason for the superiority and where the scheme can be applied.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.233-233
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• 2011

필요한 수자원을 추가확보하기 위한 댐 건설이 갈수록 어려워짐에 따라 이미 건설된 댐을 최대한 활용하는 과학적 저수지운영 방안이 필요하다. 또한 댐운영자가 쉽게 실무에 적용할 수 있는 방법이어야 한다. 본 연구의 목적은 댐관리자가 이해하기 쉽고 사용하기 쉬운 장기 저수지운영 방안을 개발하고자 하는 것이다. 수위구간별 저수지운영을 위한 운영률을 구성하고 이에 따른 순단위 저수지운영 모형을 구축하였다. 다변량 추계학적 모의발생기법을 사용하여 댐 유입량을 모의 발생하였다. 저수지운영의 수위구간을 결정하기 위한 최적화 방법으로 메타휴리스틱 방법으로 차원변화 탐색기법을 선정하였다. 안동댐의 단독운영을 위한 수위구간별 저수지운영률을 도출하여 저수지 모의운영을 수행하고 기존의 운영실적과 모의결과를 저수지운영 평가기준에 따라 비교하여 평가하였다. 안동댐의 단독운영 결과 모의된 저수위는 실적 저수위보다 전반적으로 높게 유지되었고, 모의 발전량이 실적 발전량보다 평균적으로 높음을 볼 수 있었다. 안동댐의 실적 발전량 평균값은 124.81 GWh이며, 모의결과의 발전량은 131.01 GWh이었다. 모의 발전량이 전반적으로 높은 이유는 방류량이 적은 상황에서 저수위를 높게 유지하여 발전효율을 높게 한 것이 주된 이유라고 사료된다. 안동댐의 실적과 모의 결과를 3 가지 저수지운영 평가기준으로 평가한 결과, 실패한 횟수는 실적이 554 회, 모의결과는 426 회이었다. 또한 2 순 연속하여 실패가 발생한 횟수는 각각 71회, 48 회이었고, 최대 연속 실패는 각각 52 순, 51 순이었다. 또한 총운영 기간에 대한 성공 횟수의 비율을 나타내는 신뢰도는 실적은 0.53, 모의된 결과는 0.64로 약 9 %의 차이를 보였다. 취약도는 실적이 $12.69\times10^6\;m^3$, 모의된 결과가 $5.14\times10^6\;m^3$로 $7.55\times10^6\;m^3$의 차이를 보였다. 회복도는 실적이 0.21, 모의 결과가 0.13으로 모의결과가 0.08 낮은 것으로 나타났다. 도출된 장기 저수지운영률을 안동댐의 단독운영에 적용한 결과 실적보다 본 연구에서 개발한 방법론에 의한 모의운영이 공급량, 발전량, 저수지 운영평가 통계량에서 나은 결과를 보였다.

• Journal of KIISE
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• v.43 no.2
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• pp.204-211
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• 2016

In this paper, we propose an automatic melody composition system that can generate a sophisticated melody by adding non-harmony tone in the given chord progression. An overall procedure consists of two steps, which are the rhythm generation and melody generation parts. In the rhythm generation part, we designed new fitness functions for rhythm that can be controlled by a user setting parameters. In the melody generation part, we designed new fitness functions for melody based on harmony theory. We also designed evolutionary operators that are conducted by considering a musical context to improve computational efficiency. In the experiments, we compared four metaheuristics to optimize the rhythm fitness functions: Simple Genetic Algorithm (SGA), Elitism Genetic Algorithm (EGA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Furthermore, we compared proposed genetic algorithm for melody with the four algorithms for verifying performance. In addition, composition results are introduced and analyzed with respect to musical correctness.

• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.645-653
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• 2002

The unit commitment problem in a power system involves determining the start-up and shut-down schedules of many dynamos for a day or a week while satisfying the power demands and diverse constraints of the individual units in the system. It is very difficult to derive an economically optimal schedule due to its huge search space when the number of dynamos involved is large. Tabu search is a popular solution method used for various optimization problems because it is equipped with effective means of searching beyond local optima and also it can naturally incorporate and exploit domain knowledge specific to the target problem. When given a large-scaled problem with a number of complicated constraints, however, tabu search cannot easily find a good solution within a reasonable time. This paper shows that a large- scaled optimization problem such as the unit commitment problem can be solved efficiently by using a parallel tabu search. The parallel tabu search not only reduces the search time significantly but also finds a solution of better quality.