• 한국공간구조학회논문집
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• 제3권4호
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• pp.59-67
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• 2003

Genetic algorithm is one of the best ways to solve a discrete variable optimization problem. Genetic algorithm tends to thrive in an environment in which the search space is uneven and has many hills and valleys. In this study, genetic algorithm is used for solving the design problem of gable structure. The design problem of frame structure has some special features(complicate design space, many nonlinear constrants, integer design variables, termination conditions, special information for frame members, etc.), and these features must be considered in the formulation of optimization problem and the application of genetic algorithm. So, 'FRAME operator', a new genetic operator for solving the frame optimization problem effectively, is developed and applied to the design problem of gable structure. This example shows that the new opreator has the possibility to be an effective frame design operator and genetic algorithm is suitable for the frame optimization problem.

• Industrial Engineering and Management Systems
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• 제11권2호
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• pp.170-175
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• 2012

In this paper, a procedure using a genetic algorithm (GA) and a heuristic local search (HLS) is proposed for solving facility rearrangement problem (FRP). FRP is a decision problem for stopping/running of facilities and integration of stopped facilities to running facilities to maximize the production capacity of running facilities under the cost constraint. FRP is formulated as an integer programming model for maximizing the total production capacity under the constraint of the total facility operating cost. In the cases of 90 percent of cost constraint and more than 20 facilities, the previous solving method was not effective. To find effective alternatives, this solving procedure using a GA and a HLS is developed. Stopping/running of facilities are searched by GA. The shifting the production operation of stopped facilities into running facilities is searched by HLS, and this local search is executed for one individual in this GA procedure. The effectiveness of the proposed procedure using a GA and HLS is demonstrated by numerical experiment.

• Journal of applied mathematics & informatics
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• 제16권1_2호
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• pp.337-354
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• 2004

SA, TS, GA and ACS are four of the main algorithms for solving challenging problems of intelligent systems. In this paper we consider Examination Timetabling Problem that is a common problem for all universities and institutions of higher education. There are many methods to solve this problem, In this paper we use Simulated Annealing, Tabu Search, Genetic Algorithm and Ant Colony System in their basic frameworks for solving this problem and compare results of them with each other.

• Industrial Engineering and Management Systems
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• 제10권1호
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• pp.7-14
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• 2011

In solving the Job Shop Scheduling Problem, the best solution rarely is completely random; it follows one or more rules (heuristics). The Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing, and Tabu search, which belong to the Evolutionary Computations Algorithms (ECs), are not efficient enough in solving this problem as they neglect all conventional heuristics and hence they need to be hybridized with different heuristics. In this paper a new algorithm titled "Shaking Optimization Algorithm" is proposed that follows the common methodology of the Evolutionary Computations while utilizing different heuristics during the evolution process of the solution. The results show that the proposed algorithm outperforms the GA, PSO, SA, and TS algorithms, while being a good competitor to some other hybridized techniques in solving a selected number of benchmark Job Shop Scheduling problems.

• 한국정보과학회논문지:소프트웨어및응용
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• 제32권4호
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• pp.268-276
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• 2005

본 논문은 최적 통신 걸침 나무 문제(Optimum Communication Spanning Tree Problem OCST)를 다룬다. 일반적으로, OCST문제는 WP-hard 문제로 알려져 있으며 최근에 Papadimitriou 와 Yannakakis에 의해서 MAX SNP-hard로 밝혀졌다. 그럼에도 불구하고 OCST 문제를 해결하기 위한 기존의 주된 접근법은 polynomial time 알고리즘들 이었다. 본 논문에서는 OCST 문제를 해결하기 위한 진화 알고리즘을 소개한다. 특히, 진화 알고리즘을 어떤 문제에 적용할 때 가장 우선적으로 고려되어야 하는 사항은 해를 어떻게 표현할 것인가 하는 표현법(representation)에 관한 것이다. 따라서 본 논문에서는 기존에 차수 제약 걸침 나무 문제를 해결하기 위해 제안한 표현법의 단점을 개선하는 새로운 표현법을 제안하고 이 표현법을 이용해서 트리(tree)를 만들어 내는 decoding 방법 또한 소개한다. 그리고 제안하는 해 표현법에 맞는 유전 연산자를 찾기 위해 네트워크의 정보 및 부모세대가 지닌 유전 정보를 이용하는 3가지 방법을 실험하였다. 결론적으로, 다양한 실험을 통해서 제안하는 방법이 기존의 방법에 비해 우수한 결과를 보여 준다는 것을 확인할 수 있었다.

• 한국시뮬레이션학회논문지
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• 제12권1호
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• pp.59-71
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• 2003

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Management (SCM). One of the key issues in the current SCM research area involves reducing both production and distribution costs. The purpose of this study is to determine the optimum quantity of production and transportation with minimum cost in the supply chain network. We have presented a mathematical model that deals with real world factors and constraints. Considering the complexity of solving such model, we have applied the genetic algorithm approach for solving this model using a commercial genetic algorithm based optimizer. The results for computational experiments show that the real size problems we encountered can be solved in reasonable time.

• International Journal of Computer Science & Network Security
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• 제21권12호
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• pp.175-182
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• 2021

Deadlock is a situation in which two or more processes competing for resources are waiting for the others to finish, and neither ever does. There are two different forms of systems, multi-unit and single-unit resource systems. The difference is the number of instances (or units) of each type of resource. Deadlock problem can be modeled as a constrained combinatorial problem that seeks to find a possible scheduling for the processes through which the system can avoid entering a deadlock state. To solve deadlock problem, several algorithms and techniques have been introduced, but the use of metaheuristics is one of the powerful methods to solve it. Genetic algorithms have been effective in solving many optimization issues, including deadlock Problem. In this paper, an improved parallel framework of the genetic algorithm is introduced and adapted effectively and efficiently to deadlock problem. The proposed modified method is implemented in java and tested on a specific dataset. The experiment shows that proposed approach can produce optimal solutions in terms of burst time and the number of feasible solutions in each advanced generation. Further, the proposed approach enables all types of crossovers to work with high performance.

• 한국정보과학회논문지:소프트웨어및응용
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• 제32권8호
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• pp.752-758
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• 2005

수송문제는 산업공학 및 OR 그리고 전자계산학 분야에서 중요한 문제 중의 하나로 인식된다. 수송 문제가 시설을 수립하거나 고객들의 요구를 이행하기 위한 추가적인 고정 비용과 연관될 때, fcTP(fixed charge Transportation Problem)라 한다. fcTP는 이전의 고전적인 방법으로 해결하기 어려운 NP-hard 문제들 중의 하나이다. 본 논문에서는 비선형 fcTP를 해결하기 위한 신장트리 기반 유전자알고리즘을 제안한다. 특히, 염색체(chromosome)에 대한 feasibility criteria와 repairing procedure를 포함하는 GA 염색체 표현에 대해 새로운 아이디어를 제안한다. 또한, 본 논문에서 제안하는 방법의 효율성을 입증하기 위한 여러 가지 수치 실험 결과를 기술한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권3호
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• pp.353-360
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• 2004

In this paper we describe Task-Based Problem Solving Adapters (TPSAs) for modeling a humam solution (through activity-centered analysis) to a software solution (in form of computer-based artifact). TPSAs are derived from the problem solving pattern or consistent problem solving structures/strategies employed by practitioners while designing solutions to complex problems. The adapters developed by us lead toward human-centeredness in their design and underpinning that help us to address the pragmatic task constraints through a range of technologies like neural networks, fuzzy logic, and genetic algorithms. We also outline an example of applying the TPSAs to develop a working system for assisting sales engineers of an electrical manufacturing firm in preparing indent and monitoring the status of orders in the company.

• 경영과학
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• 제29권3호
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• pp.107-120
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• 2012

In this paper we propose an effective genetic algorithm for solving the integrated inventory and routing problem of supply chain composed of multi-warehouses and multi-retailers. Unlike extant studies dealing with integrated inventory and routing problem of supply chain, our model incorporates more realistic aspect such as positive inventory at the multi-warehouses under the assumption of inventory policy of power of two-replenishment-cycle. The objective is to determine replenishment intervals for the retailers and warehouses as well as the vehicles routes so that the total cost of delivery and inventory cost is minimized. A notable feature of our algorithm is that the procedure for evaluating the fitness of objective function has the computational complexity closing to linear function. Computational results show effectiveness of our algorithm.