• 산업공학
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• 제17권2호
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• pp.208-217
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• 2004

This paper deals with a process planning problem in the flexible assembly system (FAS). The problem is to assign assembly tasks to stations with limited working space and to determine assembly routing with the objective of minimizing transfer time of the products among stations, while satisfying precedence relations among the tasks and upper-bound workload constraints for each station. In the process planning of FAS, the optimality of assembly routing depends on tasks loading. The integration of tasks loading and assembly routing is therefore important for an efficient utilization of FAS. To solve the integrated problem at the same time, in this paper we propose a new method using an artificial intelligent search technique, named 2-leveled symbiotic evolutionary algorithm. Through computational experiments, the performance of the proposed algorithm is compared with those of a traditional evolutionary algorithm and a symbiotic evolutionary algorithm. The experimental results show that the proposed algorithm outperforms the algorithms compared.

• 한국멀티미디어학회논문지
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• 제21권1호
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• pp.18-25
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• 2018

In High Efficiency Video Coding (HEVC), integer motion estimation (IME) requires a large amount of computational complexity because HEVC adopts the high flexible and hierarchical coding structures. In order to reduce the computational complexity of IME, this paper proposes the search range reduction algorithm, which takes advantage of motion vectors similarity between different layers. It needs only a few modification for HEVC reference software. Based on the experimental results, the proposed algorithm reduces the processing time of IME by 28.1% on average, whereas its the $Bj{\emptyset}ntegaard$ delta bitrate (BD-BR) increase is 0.15% which is negligible.

• 대한산업공학회지
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• 제31권3호
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• pp.180-193
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• 2005

This paper deals with a multiobjective process planning problem of flexible assembly systems(FASs). The FAS planning problem addressed in this paper is an integrated one of the assignment of assembly tasks to stations and the determination of assembly routing, while satisfying precedence relations among the tasks and flexibility capacity for each station. In this research, we consider two objectives: minimizing transfer time of the products among stations and absolute deviation of workstation workload(ADWW). We place emphasis on finding a set of diverse near Pareto or true Pareto optimal solutions. To achieve this, we present a new multiobjective coevolutionary algorithm for the integrated problem here, named a multiobjective symbiotic evolutionary algorithm(MOSEA). The structure of the algorithm and the strategies of evolution are devised in this paper to enhance the search ability. Extensive computational experiments are carried out to demonstrate the performance of the proposed algorithm. The experimental results show that the proposed algorithm is a promising method for the integrated and multiobjective problem.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.501-504
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• 2005

블록 정합 기법(block matching algorithm, BMA) 중에서 가장 널리 알려진 3 단계 탐색(three-step search, 3SS) 알고리즘은 큰 움직임 추정에 적합하지만 고정된 탐색 점으로 인하여 작은 움직임 추정에는 계산 면에서 낭비가 심하고 탐색이 잘못될 경우가 대부분이다. 한편, 효율적인 3 단계 탐색(efficient three-step search, E3SS)은 중앙-편중된 움직임 추정을 작은 다이아몬드 탐색(small diamond search, SDS) 알고리즘으로 보완하여 예측성과 탐색 속도를 향상시킨 알고리즘이다. 본 논문에서는 탐색 초기 단계에서 탐색 점을 최적 배치하고 E3SS 의 SDS 알고리즘을 변형시킨 탐색 알고리즘을 제안한다. 실험 결과는 제안된 탐색 알고리즘이 E3SS 와 비교하여 평균 22% 정도 계산량을 감소시키면서도 MSE(Mean Square Error)의 성능 저하를 거의 보이지 않는 것으로 나타난다.

• Industrial Engineering and Management Systems
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• 제8권4호
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• pp.239-246
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• 2009

This paper deals with the makespan minimization problem of job shops. The problem is known as one of hard problems to optimize, and therefore, many heuristic methods have been proposed by many researchers. The aim of this study is also to propose a heuristic scheduling method for the problem. However, the difference between the proposed method and many other heuristics is that the proposed method is based on depth-first branch and bound, and thus it is possible to find an optimal solution at least in principle. To accelerate the search, when a node is judged hopeless in the search tree, the proposed flexible branch and bound method can indicate a higher backtracking node. The unexplored nodes are stored and may be explored later to realize the strict optimization. Two methods are proposed to generate the backtracking point based on the critical path of the current best feasible schedule, and the minimum lower bound for the makespan in the unexplored sub-problems. Schedules are generated based on Giffler and Thompson's active schedule generation algorithm. Acceleration of the search by the flexible branch and bound is confirmed by numerical experiment.

• 한국인터넷방송통신학회논문지
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• 제10권3호
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• pp.91-95
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• 2010

검색엔진을 사용하는 이용자의 정보 즉 선호도에 따른 지속적인 피드백으로 검색 결과의 랭킹을 향상시켜 유연한 검색이 가능하게 하는 방법에는 학습된 인공 신경망을 이용한다. 인공 신경망 학습은 신경망이 여러 다른 검색어로 학습된 후 다른 사용자들이 과거에 실제 검색했던 결과를 좀 더 반영하기 위한 것이다. 가중치의 지속적인 변경을 위해서는 네트워크에서 역방향으로 움직이면서 가중치를 변경하는 역전파 알고리즘을 이용하여 학습한다. 그러나 이러한 학습은 초기에는 훈련데이터에 적합한 성능을 보이나 학습의 횟수가 증가할수록 점점 과대적합되는 것을 알 수 있다. 따라서 본 논문에서는 최적화해야 할 개체가 많을 때 강한 장점을 가지고 있는 유전자 알고리즘을 적용하여 검색어에 관련성이 높은 페이지들 유연하게 랭킹하기 위해 URL리스트를 개체로 랜덤으로 선택하여 학습하는 기법을 제안한다.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 1995년도 추계학술대회논문집
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• pp.30-36
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• 1995

Conventionally design for multi-stage cold forging depends on the designer's experience and decision-making. Due to such non-deterministic nature of the process sequence design, a flexible inference engine is needed for process design expert system. In this study, A* searching algorithm was introduced to arrive at the vetter process sequence design considering the number of forming stages and levels of effective strain, effective stress, and forming load during the porcess. In order to optimize the process sequence in producing the final part, cost function was defined and minimized using the proposed A* searching algorithm. For verification of the designed forming sequences, forming experiments and finite element analyses were carried out in the present investigation. The developed expert system using A* searching algorithm can produce a flexible design system based on changes in the number of forming stages and weights.

• 지능정보연구
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• 제2권1호
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• pp.93-107
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• 1996

A flexible flow line(FFL) consists of several groups of identical machines. All work-orders flow along the same path through successive machine groups. Thus, we emphasized the balancing of workloads between machine groups in order to maximize total productivity. On the other hand, many different types of work-orders, in varying batch or lot sizes, are produced simultaneously. The mix of work-orders, their lot sizes, and the sequence in which they are produced affect the amount of workload. However, the work-orders and their lot sizes are prefixed and cannot be changed. Because of these reasons, we have developed an optimal route selection model using heuristic search and Min-Max algorithm for balancing the workload between machine groups in the FFL.

• 산업경영시스템학회지
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• 제22권53호
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• pp.37-44
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• 1999

The problem to be studied here is the minimization of the total travel distance and the number of vehicles used for delivering goods to customers. Vehicle routes must also satisfy a variety of constraints such as fixed vehicle capacity, allowed operating time. Genetic algorithm to solve the VRPTW with heterogeneous fleet is presented. The chromosome of the proposed GA in this study has the 3-dimension. We propose GA that has the cubic-chromosome for VRPTW with heterogeneous fleet. The newly suggested ‘Cubic-GA (or 3-D GA)’ in this paper means the 2-D GA with GLS(Genetic Local Search) algorithms and is quite flexible. To evaluate the performance of the algorithm, we apply it to the Solomon's VRPTW instances. It produces a set of good routes and the reasonable number of vehicles.

• 한국정보시스템학회지:정보시스템연구
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• 제25권1호
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• pp.159-182
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• 2016

Purpose This paper aims to develop a novel tabu search algorithm for solving the sequencing problems with precedence constraints. Due to constraints, the traditional meta heuristic methods can generate infeasible solutions during search procedure, which must be carefully dealt with. On the contrary, the candidate order based tabu search (COTS) is based on a novel neighborhood structure that guarantees the feasibility of solutions, and can dealt with a wide range of sequencing problems in flexible manner. Design/methodology/approach Candidate order scheme is a strategy for constructing a feasible sequence by iteratively appending an item at a time, and it has been successfully applied to genetic algorithm. The primary benefit of the candidate order scheme is that it can effectively deal with the additional constraints of sequencing problems and always generates the feasible solutions. In this paper, the candidate order scheme is used to design the neighborhood structure, tabu list and diversification operation of tabu search. Findings The COTS has been applied to the single machine job sequencing problems, and we can see that COTS can find the good solutions whether additional constraints exist or not. Especially, the experiment results reveal that the COTS is a promising approach for solving the sequencing problems with precedence constraints. In addition, the operations of COTS are intuitive and easy to understand, and it is expected that this paper will provide useful insights into the sequencing problems to the practitioners.