• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2470-2476
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• 1997

We propose a new real-time search algorithm and provide experimental evaluation and comparison of the new algorithm with mini-min lookahead algorithm. Many other real-time heuristic-search approached often divide the problem space to several sub-problems. In this paper, the proposed algorithm guarantees not only the sub-problem deadline but also total deadline. Several heuristic real-time search algorithms such as $RTA^{\ast}$, SARTS and DYNORA have been proposed. The performance of such algorithms depend on the quality of their heuristic functions, because such algorithms estimate the search time based on the heuristic function. In real-world problem, however, we often fail to get an effective heuristic function beforehand. Therefore, we propose a new real-time algorithm that determines the sub-problem deadline based on the status of search space during sub-problem search process. That uses the cut-off method that is a dynamic stopping-criterion-strategy to search the sub-problem.

• KIISE Transactions on Computing Practices
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• v.23 no.8
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• pp.510-515
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• 2017

It is important to speed up the loading time of web applications. Parsing is a loading process that contributes to an increased loading time. To address this issue, the optimization called Concurrent Parsing has been proposed which handles the parsing process in parallel by using additional threads. However, Concurrent Parsing has a limitation that it does not consider the priority order of parsing. In this paper, we propose heuristics that exploit priorities of parsing to improve the Concurrent Parsing. For parsing priority, we empirically investigate the sequence of function calls, classify functions into 3 categories, and extract function call probabilities. If a function has high call probability, we give a high priority and if a function has low probability, we give a low priority. We evaluate this priority heuristics on real web applications and get the 2.6% decrease of loading time on average.

• The KIPS Transactions:PartB
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• v.8B no.2
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• pp.183-188
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• 2001

이 논문은 눈의 형태에 대한 정보를 이용하여 눈동자를 효과적으로 찾는 방법과 얼굴 특성을 반영하는 평가함수를 이용하여 눈동자, 입의 위치와 같은 얼굴 특징들을 인식하는 방법을 제안하였다. 색 정보를 이용하여 입술과 얼굴 영역을 추출하고 눈동자와 흰자위간의 명도 차를 이용하는 함수를 사용하여 눈동자를 인식하였다. 마지막으로 얼굴 특성을 반영하느 평가함수를 정의하고 이를 이용하여 최종적인 얼굴과 눈, 입을 인식하였다. 제안한 방법을 사용하여 여러 영상들에 대해 실험하여 좋은 결과를 얻었다.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.403-410
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• 2009

The two-dimensional bin packing problem(2D-BPP) has been known to be NP-hard, and it is difficult to solve the problem exactly. Many approximation methods, such as genetic algorithm, simulated annealing and tabu search etc, have been also proposed to gain better solutions. However, the existing approximation algorithms, such as branch-and-bound and tabu search, have shown the low efficiency and the long execution time due to a large of iterations. To solve these problems, we first define the fitness function to simplify and increase the utility of algorithm. The function decides whether an item is packed into a given area, and as an important information for a packing strategy, the number of subarea that can accommodate a given item is obtained from the variant of the fitness function. Then we present a heuristic algorithm BF for 2D bin packing, constructed by the fitness function and subarea. Finally, the effectiveness of the proposed algorithm will be expressed by the comparison experiments with the heuristic and the metaheuristic of the literatures. As comparing with existing heuristic algorithms and metaheuristic algorithms, it has been found that the packing rate of algorithm BP is the same as 97% as existing heuristic algorithms, FFF and FBS, or better than them. Also, it has been shown the same as 86% as tabu search algorithm or better.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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• pp.423-426
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• 2013

본 연구에서는 임분 단위에서 산림의 이산화탄소 흡수 및 저장 기능을 최적화 할 수 있는 최적의 산림시업체계를 도출하고자하였고, 이를 위해 임분 생장모델과 Simulated Annealing 휴리스틱 기법을 적용하여 임분탄소 최적화 프로그램을 개발하였다. 휴리스틱 알고리즘에서 최적해를 찾기 위해 반복 실행 되는 과정에서 더 이상 최적해을 찾지 못하고 목표 값이 어떤 일정한 값(Local Optimum)에 계속 머무는 현상을 해결하기 위해 임계치를 적용하며, SA 휴리스틱 기법에서는 열균형테스트를 이용하고 있다. 개발된 프로그램을 이용하여 3가지 산림 시업 시나리오에 대한 비교 분석을 실시하기 위해 프로그램을 실행한 결과, 목재수확량의 경우 목재수확량을 최대를 목표로 한 대안이 3개 시나리오 가운데 목재수확량이 가장 높은 것으로 나타났으며, 또한 탄소저장량에서도 탄소저장량을 최적화한 대안이가 탄소저장량이 가장 높은 것으로 나타나 프로그램이 목적에 맞게 개발된 것으로 판단됐다. 또한 열균형 테스트의 온도저감율을 조정하여 프로그램을 반복실행하여 온도저감율이 프로그램 실행 시에 미치는 영향을 분석한 결과 온도저감율에 따라 출력되는 목적함수의 최적값과 프로그램 반복횟수가 영향을 받는 것으로 나타나 프로그램 실행을 최적으로 하기위해 온도 저감율의 파라미터 값을 0.1로 설정하였다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.147-150
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• 2000

본 논문은 단일설비로 다종제품을 생산하는 생산시스템에서 생산준비비용의 절감효과를 고려한 동적생산계획 모형을 다룬다. 이 모형에서 각 제품에 대한 수요는 유한계획기간에서 동적으로 발생하고 추후조달은 허용되지 않으며 투입자원은 한 종류가 사용된다. 또한, 생산기간마다 생산설비는 다종제품을 동시에 생산하고 이때 각 제품의 생산량은 전체 투입자원량의 일정비율로 생산된다. 이 모형에서 총비용은 생산준비 비용의 절감을 위한 투자비용, 생산준비비용, 각 제품별 재고유지비용으로 구성된다. 본 논문에서는 절감된 생산준비비용 하에서의 최적생산계획과 생산준비비용의 절감을 위한 최적투자액을 동시에 결정할 수 있는 휴리스틱 알고리즘를 제시한다. 또한, 선형 및 지수 감소함수 형태의 생산준비비용 절감함수 하에서 다양한 문제들을 대상으로 한 시뮬레이션 실험을 통해 제시한 휴리스틱 알고리즘의 효율성을 검증한다.

• Journal of Korea Game Society
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• v.13 no.1
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• pp.31-40
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• 2013

A* algorithm is a well known pathfinding algorithm. However, there may be a limit to use A* algorithm in real-time in a map where many interactions occur between objects or many obstacles exist. Therefore, it may be necessary to find a naturally looking path quickly instead of finding a shortest path in games. In this paper, we propose a new heuristic function to exploit path information in a map. We also show that the pathfinding algorithm based on the proposed heuristic function can find a good path much faster than A* algorithm on several grid maps.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.49-62
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• 2024

In this paper, a sustainable closed-loop supply chain (SCLSC) network model is proposed for domestic mobile phone industry. Economic, environmental and social factors are respectively considered for reinforcing the sustainability of the SCLSC network model. These three factors aim at minimizing total cost, minimizing total amount of CO₂ emission, and maximizing total social influence resulting from the establishment and operation of facilities at each stage of the SCLSC network model. Since they are used as each objective function in modeling, the SCLSC network model can be a multi-objective optimization problem. A mathematical formulation is used for representing the SCLSC network model and a hybrid meta-heuristic approach is proposed for efficiently solving it. In numerical experiment, the performance of the proposed hybrid meta-heuristic approach is compared with those of conventional meta-heuristic approaches using some scales of the SCLSC network model. Experimental results shows that the proposed hybrid meta-heuristic approach outperforms conventional meta-heuristic approaches.

• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.19-28
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• 2017

Recently the cargo transportation is increasing according to lager containerships in the container terminal. Thus, the various ways(such as efficient vehicle scheduling and minimizing delay time) are applied to increase productivity to handle the increasing cargo transportation in the container terminal. In this paper, the optimized model(Solvers) is applied to improve the existing heuristic method as a way of increasing productivity. The experimental design is that the result of two objective functions(minimizing travel and delay time of the empty vehicle) is compared to the result of the existing heuristic method by six sample problems. As a result of the two objective function experiments, the optimized model draws 5.3% more improved performance than the heuristic method in four of six problem samples.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.91-98
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• 2010

Conventional path-finding has focused on finding short collision-free paths. However, as computer games become more sophisticated, it is required to take tactical information like ambush points or lines of enemy sight into account. One way to make this information have an effect on path-finding is to represent a heuristic function of a search algorithm as a weighted sum of tactics. In this paper we consider the problem of learning heuristic to optimize path-finding based on given tactical information. What is meant by learning is to produce a good weight vector for a heuristic function. Training examples for learning are given by a game level-designer and will be compared with search results in every search level to update weights. This paper proposes a learning algorithm integrated with search for tactical path-finding. The perceptron-like method for updating weights is described and a simulation tool for implementing these is presented. A level-designer can mark desired paths according to characters' properties in the heuristic learning tool and then it uses them as training examples to learn weights and shows traces of paths changing along with weight learning.