• 산업공학
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• 제22권1호
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• pp.10-16
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• 2009

In this paper, we propose an efficient search algorithm for finding an optimal schedule to minimize makespan, while avoiding deadlock situation in Flexible Manufacturing Systems (FMS) with finite capacity, in which each job needs to be processed in several job stages for completion. The proposed algorithm uses a modeling and control method based on Petri-net. Especially, we improve the efficiency of the search algorithm by using a priority rule and an efficient bounding function during the search procedure. The performance of the proposed algorithm is evaluated through a numerical experiment, showing that it holds considerable promise for providing an optimal solution efficiently comparing to past work.

• 통합자연과학논문집
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• 제2권2호
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• pp.55-58
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• 2009

A ligand-receptor docking program is an indispensible tool in modern pharmaceutical design. An accurate prediction of small molecular docking pose to a receptor is essential in drug design as well as molecular recognition. An effective docking program requires the ability to locate a correct binding pose in a surprisingly complex conformational space. However, there is an inherent difficulty to predict correct binding pose. The odds are more demanding than finding a needle in a haystack. This mainly comes from the flexibility of both ligand and receptor. Because the searching space to consider is so vast, receptor rigidity has been often applied in docking programs. Even nowadays the receptor may not be considered to be fully flexible although there have been some progress in search algorithm. Improving the efficiency of searching algorithm is still in great demand to explore other applications areas with inherently flexible ligand and/or receptor. In addition to classical search algorithms such as molecular dynamics, Monte Carlo, genetic algorithm and simulated annealing, rather recent algorithms such as tabu search, stochastic tunneling, particle swarm optimizations were also found to be effective. A good search algorithm would require a good balance between exploration and exploitation. It would be a good strategy to combine algorithms already developed. This composite algorithms can be more effective than an individual search algorithms.

• 산업경영시스템학회지
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• 제21권47호
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• pp.35-45
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• 1998

Scheduling problem in Flexible Manufacturing Systems(FMS) is complex because of various situation of Manufacturing Systems. Especially, in case of short-term scheduling demanding high efficiency, low cost at short-period, efficient scheduling is a serious problem. To solve this problem, many dispatching rules are developed. But, it leave much to be desired, because real situation in shop floor is complex and real-time scheduling is needed in real manufacturing shop floor. In this paper, search algorithm that allocate different dispatching rules to each machine is presented to complement lack of dispatching rule and develop practical real-time scheduling system. The search algorithm is described in detail. First, algorithm detect machine breakdown, evaluate each dispatching rule. dispatching rules for each machine meeting performance criteria are ranked. The algorithm selects new dispatching nile for bottleneck machine. The effectivenes and efficiency of the mixed dispatching rule and search algorithm is demonstrated.

• Journal of Electrical Engineering and Technology
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• 제7권1호
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• pp.109-114
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• 2012

In this paper, we utilize training strategy of hidden Markov model (HMM) to use in versatile issues such as classification of time-series sequential data such as electric transient disturbance problem in power system. For this, an automatic means of optimizing HMMs would be highly desirable, but it raises important issues: model interpretation and complexity control. With this in mind, we explore the possibility of using genetic algorithm (GA) and harmony search (HS) algorithm for optimizing the HMM. GA is flexible to allow incorporating other methods, such as Baum-Welch, within their cycle. Furthermore, operators that alter the structure of HMMs can be designed to simple structures. HS algorithm with parameter-setting free technique is proper for optimizing the parameters of HMM. HS algorithm is flexible so as to allow the elimination of requiring tedious parameter assigning efforts. In this paper, a sequential data analysis simulation is illustrated, and the optimized-HMMs are evaluated. The optimized HMM was capable of classifying a sequential data set for testing compared with the normal HMM.

• 대한교통학회지
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• 제24권3호
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• pp.177-187
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• 2006

IT 산업이 발달하고, 정보의 양이 넘쳐날수록, 사람들은 획일화되어 제공되는 정보보다는, 스스로 다양한 경로를 통해 정보를 찾아내며, 이를 가공하여 판단하고 반응한다. 그러므로 정보 제공자들은 이러한 개인들의 성향을 만족시키기 위해 서는 획일화된 정보보다는 소비자들이 스스로 판단할 수 있도록 다양한 정보를 제공해 주어야만 할 것이다 이를 위하여 비용의 비교를 통해 경로를 선택하는 기존 알고리즘과 달리 최저비용과의 차이를 통한 알고리즘을 제안한다. 이를 위해 본 연구는 기존의 노드 기반 탐색법에 비해 네트워크 구조의 변화 없이 효율적으로 환승이나 회전제약을 표현할 수 있는 링크 기반 탐색법을 기반으로 운전자들의 다양한 needs를 최대한 반영할 수 있는 즉 유연한 탐색 알고리즘의 개발을 목표로 한다. 이러한 목표를 위해, 기존의 최적 경로와 다경로 탐색 알고리즘을 대상으로 이론적 배경을 고찰하고, 다목적 정보제공을 위한 다경로 탐색기법을 위한 통행원리를 개념화한 후, 이를 알고리즘에 적용하는 방안을 제안하며, 가상의 네트워크에 적용하여 알고리즘 수행과정을 보여주고자 한다

• 지능정보연구
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• 제13권3호
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• pp.63-82
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• 2007

본 논문에서는 유연제조시스템(FMS)에서 다단계스케줄링 문제를 효율적으로 해결하기 위한 적응형 혼합유전 알고리즘(ahGA) 접근법을 제안한다. 제안된 ahGA는 FMS의 해를 개선시키기 위하여 이웃탐색기법을 사용하며, 유전탐색과정에서의 수행도를 향상시키기 위해 유전알고리즘(GA)의 파라메터들을 조정하기 위한 적응형 구조를 사용한다. 수치실험에서는 제안된 ahGA와 기존의 알고리즘들 간의 수행도를 비교하기 위하여 두가지형태의 다단계스케줄링문제를 제시한다. 실험결과는 제안된 ahGA가 기존의 알고리즘들 보나 더 뛰어난 수행도를 보여주고 있다.

• 산업공학
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• 제17권spc호
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• pp.20-27
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• 2004

A flexible assembly system (FAS) is a production system that assembles various parts with many constraints and manufacturing flexibilities. This paper presents a new method for efficiently solving the integrated process planning and scheduling in FAS. The two problems of FAS process planning and scheduling are tightly related with each other. However, in almost all the existing researches on FAS, the two problems have been considered separately. In this research, an endosymbiotic evolutionary algorithm is adopted as methodology in order to solve the two problems simultaneously. This paper shows how to apply an endosymbiotic evolutionary algorithm to solving the integrated problem. Some evolutionary schemes are used in the algorithm to promote population diversity and search efficiency. The experimental results are reported.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.580-590
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• 2018

Active distribution system (ADS) considering distributed generation (DG) and electric vehicle (EV) is an effective way to cut carbon emission and improve system benefits. ADS is an evolving, complex and uncertain system, thus comprehensive model and effective optimization algorithms are needed. Battery swapping station (BSS) for EV service is an essential type of flexible load (FL). This paper establishes ADS planning model considering BSS firstly for the minimization of total cost including feeder investment, operation and maintenance, net loss and carbon tax. Meanwhile, immune binary firefly algorithm (IBFA) is proposed to optimize ADS planning. Firefly algorithm (FA) is a novel intelligent algorithm with simple structure and good convergence. By involving biological immune system into FA, IBFA adjusts antibody population scale to increase diversity and global search capability. To validate proposed algorithm, IBFA is compared with particle swarm optimization (PSO) algorithm on IEEE 39-bus system. The results prove that IBFA performs better than PSO in global search and convergence in ADS planning.

• 대한기계학회논문집A
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• 제22권1호
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• pp.90-101
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• 1998

This paper presents a scheduling method that uses Petri net modeling and heuristic search to handle the tool setup. In manufacturing systems, a tool is attached to a particular machine to process a particular operation. The activity to attach a tool to a particular machine and detach the tool from the machine requires time. The processing time of operations varies according to the attached tool and the machine used. The method proposed in this paper uses Petri net to model these characteristics and applies a search algorithm to the reachability graph of the Petri net model to generate an optimal or near-optimal schedule. New heuristic functions are developed for efficient search. The experimental results that show the effectiveness of the proposed method are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.3789-3809
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• 2015

In this paper, we deal with the problem of M2M gateways' network selection for different types of M2M traffic in heterogeneous wireless networks. Based on the difference in traffic's quality of service (QoS) requirements, the M2M traffic produced by various applications is mainly classified as two categories: flexible traffic and rigid traffic. Then, game theory is adopted to solve the problem of network-channel selection with the coexistence of flexible and rigid traffic, named as flexible network access (FNA). We prove the formulated discrete game is a potential game. The existence and feasibility of the Nash equilibrium (NE) of the proposed game are also analyzed. Then, an iterative algorithm based on optimal reaction criterion and a distributed algorithm with limited feedback based on learning automata are presented to obtain the NE of the proposed game. In simulations, the proposed iterative algorithm can achieve a near optimal sum utility of whole network with low complexity compared to the exhaustive search. In addition, the simulation results show that our proposed algorithms outperform existing methods in terms of sum utility and load balance.