• 산업공학
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• 제21권3호
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• pp.254-261
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• 2008

Connected-(r,s)-out of-(m,n) : F system is an important topic in redundancy design of the complex system reliability and it's maintenance policy. Previous studies applied Monte Carlo simulation and genetic, simulated annealing algorithms to tackle the difficulty of maintenance policy problem. These algorithms suggested most suitable maintenance cycle to optimize maintenance pattern of connected-(r,s)-out of-(m,n) : F system. However, genetic algorithm is required long execution time relatively and simulated annealing has improved computational time but rather poor solutions. In this paper, we propose the ant colony optimization approach for connected-(r,s)-out of-(m,n) : F system that determines maintenance cycle and minimum unit cost. Computational results prove that ant colony optimization algorithm is superior to genetic algorithm, simulated annealing and tabu search in both execution time and quality of solution.

• 대한산업공학회지
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• 제35권4호
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• pp.218-225
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• 2009

This paper considers a parallel-machine scheduling problem with job release times and sequence-dependent setup times. The objective of this problem is to determine the allocation policy of jobs and the scheduling policy of machines so as to minimize the weighted sum of setup times, delay times, and tardy times. A mathematical model for optimal solution is derived and a meta heuristic algorithm based on the improved ant colony system is proposed in this paper. The performance of the meta heuristic algorithm is evaluated through compare with optimal solutions using randomly generated several examples.

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.36-40
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• 2023

Recently, in the problem of multi-order processing in logistics warehouses, multi-pickup systems are changing from the form in which workers walk around the warehouse to the form in which goods come to workers. These changes are shortening the time to process multiple orders and increasing production. This study considered the sequence problem of which warehouse the items to be loaded on each truck come first and which items to be loaded first when loading multiple pallet-unit goods on multiple trucks in an industrial smart logistics automation warehouse. To solve this problem efficiently, we use the mutant algorithm, which combines the GA algorithm and ACO algorithm, and compare with original system.

• 한국정밀공학회지
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• 제29권4호
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• pp.373-379
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• 2012

Disruptions in manufacturing systems caused by system changes and disturbances such as the tool wear, machine breakdown, malfunction of transporter, and so on, reduce the productivity and the increase of downtime and manufacturing cost. In order to cope with these challenges, a new method to build an intelligent manufacturing system with biological principles, namely an ant colony inspired manufacturing system, is presented. In the developed system, the manufacturing system is considered as a swarm of cognitive agents where work-pieces, machines and transporters are controlled by the corresponding cognitive agent. The system reacts to disturbances autonomously based on the algorithm of each autonomous entity or the cooperation with them. To develop the ant colony inspired manufacturing system, the disturbances happened in the machining shop of a transmission case were analyzed to classify them and to find out the corresponding management methods. The system architecture with the autonomous characteristics was generated with the cognitive agent and the ant colony technology. A test bed was implemented to prove the functionality of the developed system.

• 한국지능시스템학회논문지
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• 제8권3호
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• pp.1-8
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• 1998

본 논문에서는 유전자 프로그래밍의 성능을 향상시키기 위하여 강화학습법에 기반한 강화 유전자 프로그래밍을 제안한다. 트리구조와 프로그램을 염색체로 가지는 유전자 프로그래밍(GP)은 다른 진화 알고리즘에 비해 염색체의 크기에 제한이 없기 때문에 표현력에 융통성이 많다는 장점이 있다. 그러나 이러한 특징은 반대고 교차 및 돌연변이 연산에 있어서 수렴성을 떨어뜨리는 단점을 나타낸다. 따라서 유전자 프로그래밍은 다른 진화알고리즘에 비해 개체군의 크기 및 진화 세대수를 크게 잡는 것이 일반적이다. 본 논문에서는 유전자 프로그래밍의 이러한 성질을 개선하기 위해서 프로그램에 강화신호를 주어 이것의 보답/벌칙의 정도에 기반한 교차 및 돌연번이 연산을 실행하는 방법을 제안한다. 제안된 방법은 인공개미(Artificial Ant)문제에 적용하여 그 유효성을 입증한다.

• 산업경영시스템학회지
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• 제42권3호
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• pp.1-7
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• 2019

It is one of the known methods to obtain the optimal solution using the Ant Colony Optimization Algorithm for the Traveling Salesman Problem (TSP), which is a combination optimization problem. In this paper, we solve the TSP problem by proposing an improved new ant colony optimization algorithm that combines genetic algorithm mutations in existing ant colony optimization algorithms to solve TSP problems in many cities. The new ant colony optimization algorithm provides the opportunity to move easily fall on the issue of developing local optimum values of the existing ant colony optimization algorithm to global optimum value through a new path through mutation. The new path will update the pheromone through an ant colony optimization algorithm. The renewed new pheromone serves to derive the global optimal value from what could have fallen to the local optimal value. Experimental results show that the existing algorithms and the new algorithms are superior to those of existing algorithms in the search for optimum values of newly improved algorithms.

• 대한전자공학회논문지SD
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• 제41권3호
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• pp.71-82
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• 2004

생태계의 군집 특성을 네트워크 환경에 적용하여 급변하는 환경에 대한 자가 적응 및 생존 특성을 부여하는 연구가 최근 많은 주목을 받고 있다. 그 중 AntNet은 개미를 모델링한 모바일 에이젼트를 사용하여 최적의 네트워크 경로를 선택하는 적응적 라우팅 알고리즘이다. 본 논문에서는 SoC 시스템에 적용 가능한 AntNet 기반 하드웨어 구조를 제안한다. 제안된 구조는 기존 알고리즘 수준의 AntNet을 하드웨어 레벨로 근사화 하여 설계되었으며, 기존 AntNet과 가상 네트워크 구조에서의 비교를 통하여 그 타당성을 검증하였다. 그리고 RTL 수준의 설계 및 합성 결과를 통하여 제안된 하드웨어 구조가 AntNet 기반 라우팅 구현에 효과적임을 확인할 수 있었다.

• 한국기계가공학회지
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• 제18권9호
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• pp.36-43
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• 2019

Turning, a main machining process, is a widespread process in metal cutting industries. Many researchers have investigated the effects of process parameters on the machining process. In the turning process, input variables including cutting speed, feed, and depth of cut are generally used. Surface roughness and electric current consumption are used as output variables in this study. We construct a simulation model for the turning process using a neural network, which predicts the output values based on input values. In the neural network, obtaining the appropriate set of weights, which is called training, is crucial. In general, back propagation (BP) is widely used for training. In this study, techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) as well as BP were used to obtain the weights in the neural network. Particularly, two combined techniques of ACO_BP and PSO_BP were utilized for training the neural network. Finally, the performances of the two techniques are compared with each other.

• 한국미생물·생명공학회지
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• 제43권3호
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• pp.236-240
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• 2015

초고온성 고세균 Thermococcus onnurineus NA1은 개미산, 일산화탄소, 또는 전분 등을 이용해서 수소를 생산하는 것으로 알려져 있다. 본 연구에서는 T. onnurineus NA1의 고정화 세포를 이용한 수소생산을 고찰하였다. 고정화 실험결과, T. onnurineus NA1은 표면에 아민기가 코팅된 규조토 담체에 정전기적 인력에 의해 효과적으로 고정화되었고, 1 g의 담체에 고정화 될 수 있는 최대 세포의 양은 71.7 mg-dcw로 확인되었다. 고정화 세포를 이용한 세 번의 반복회분식 배양을 통해 개미산으로부터 수소생산 특성을 고찰하였고, 그 결과 배양이 반복됨에 따라 고정화 세포 농도의 증가에 기인하여 초기수소생산속도가 2.3 에서 4.0 mmol l⁻¹ h⁻¹로 상당량 증가됨이 관찰되었다. 따라서, T. onnurineus NA1의 고정화세포 시스템은 수소생산을 위한 좋은 대안이 될 수 있을 것으로 사료된다. 본 연구는 초고온성 고세균의 고정화세포를 수소생산에 적용한 첫 번째 사례이다.

• 산업경영시스템학회지
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• 제40권2호
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• pp.13-21
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• 2017

Airline schedules are highly dependent on various factors of uncertainties such as unfavorable weather conditions, mechanical problems, natural disaster, airport congestion, and strikes. If the schedules are not properly managed to cope with such disturbances, the operational cost and performance are severely affected by the delays, cancelations, and so forth. This is described as a disruption. When the disruption occurs, the airline requires the feasible recovery plan returning to the normal operations in a timely manner so as to minimize the cost and impact of disruptions. In this research, an Ant Colony Optimization (ACO) algorithm with re-timing strategy is developed to solve the recovery problem for both aircraft and passenger. The problem consists of creating new aircraft routes and passenger itineraries to produce a feasible schedule during a recovery period. The suggested algorithm is based on an existing ACO algorithm that aims to reflect all the downstream effects by considering the passenger recovery cost as a part of the objective function value. This algorithm is complemented by re-timing strategy to effectively manage the disrupted passengers by allowing delays even on some of undisrupted flights. The delays no more than 15 minutes are accepted, which does not influence on the on-time performance of the airlines. The suggested method is tested on the real data sets from 2009 ROADEF Challenge, and the computational results are compared with the existing ones on the same data sets. The method generates the solution for most of problem set in 10 minutes, and the result generated by re-timing strategy is discussed for its impact.