• 경영과학
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• 제12권2호
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• pp.105-127
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• 1995

This paper solves different machine-part group formation (MPGF) problems using genetic algorithms to demonstrate that it can be a new robust alternative to the conventional heuristic approaches for optimization problems. We first give an overview of genetic algorithms: Its principle, various considerations required for its implementation, and the method for setting up parameter values are explained. Then, we describe the MPGF problem which are critical to the successful operation of cellular manufacturing or flexible manufacturing systems. We concentrate on three models of the MPGF problems whose forms of the objective function and/or constraints are quite different from each other. Finally, numerical examples of each of the models descibed above are solved by using genetic algorithms. The result shows that the solutions derived by genetic algorithms are comparable to those obtained through problem-specific heuristic methods.

• Architectural research
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• 제8권2호
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• pp.43-48
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• 2006

The appropriate fleet estimation of the excavation equipment is a major factor in the determination of the cost and time requirements of a project. But the decision of what kind of equipment selected is often based on heuristic methods or trial and error in Korea. Thus, this study proposes a prototype model that uses genetic algorithms to select fleet estimation of loaders (backhoe) and trucks used in excavation work. To verify the applicability of this model, the case study was performed. And the result of the genetic model was compared with that of the trial & error method. The use of the genetic model suggested this study required 44days, 2 units of backhoes, 7 units of trucks, and a total cost of 171,839,756 won. With the estimated fleet number of equipment, the minimum cost of excavation work can be calculated, taking account of the time-cost trade-off. By utilizing this prototype model, the efficiency of excavation work can be improved.

• 한국경영과학회지
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• 제27권2호
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• pp.97-109
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• 2002

Manufacturing environments in the real world are subject to many sources of change and uncertainty, such as new job releases, job cancellations, a chance in the processing time or start time of some operation. Thus, the realistic scheduling method should Properly reflect these dynamic environment. Based on the release times of jobs, JSSP (Job Shoe Scheduling Problem) can be classified as static and dynamic scheduling problem. In this research, we mainly consider the dynamic JSSP with continually arriving jobs. The goal of this research is to develop an efficient scheduling method based on GA (Genetic Algorithm) to address dynamic JSSP. we designed scheduling method based on SGA (Sing1e Genetic Algorithm) and PGA (Parallel Genetic Algorithm) The scheduling method based on GA is extended to address dynamic JSSP. Then, This algorithms are tested for scheduling and rescheduling in dynamic JSSP. The results is compared with dispatching rule. In comparison to dispatching rule, the GA approach produces better scheduling performance.

• International Journal of Korean Welding Society
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• 제3권2호
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• pp.15-23
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• 2003

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. This method searches for optimal settings of welding parameters through systematic experiments without a model between input and output variables. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. A genetic algorithm was applied to optimization of weld bead geometry. In the optimization problem, the input variables were wire feed rate, welding voltage, and welding speed, root opening and the output variables were bead height, bead width, penetration and back bead width. The number of level for each input variable is 8, 16, 8 and 3, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions, 3,072 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions from less than 48 experiments.

• Industrial Engineering and Management Systems
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• 제8권4호
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• pp.228-238
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• 2009

This paper first presents two supply chain design problems: 1) a factory location setting and factory selection problem, and 2) a factory location setting and factory/supplier selection problem. The first involves a number of location known retailers choosing one factory to supply their demands from a number of factories whose locations are to be determined. The goal is to minimize the transportation and manufacturing cost to satisfy the demands. The problem is then augmented into the second problem, where the procurement cost of the raw materials from a chosen material supplier (from a number of suppliers) is considered for each factory. Economic beneficial is taken into account in the cost evaluation. Therefore, the partner selections will influence the cost of the supply chain significantly. To solve these problems, an agent gaming and genetic algorithm hybrid method (AGGAHM) is proposed. The AGGAHM consecutively and alternatively enable and disable the advancement of agent gaming and the evolution of genetic computation. Computation results on solving a number of examples by the AGGAHM were compared with those from methods of a general genetic algorithm and a mutual frozen genetic algorithm. Results showed that the AGGAHM outperforms the methods solely using genetic algorithms. In addition, various parameter settings are tested and discussed to facilitate the supply chain designs.

• 대한전기학회논문지:전력기술부문A
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• 제50권2호
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• pp.76-82
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• 2001

This paper presents a approach for a service restoration and optimal reconfiguration of distribution network using Genetic algorithm(GA) and Tabu search(TS) method. Restoration and reconfiguration problems in distribution network are difficult to solve in short times, because distribution network supplies power for customers combined with many tie-line switches and sectionalizing switches. Furthermore, the solutions of these problems have to satisfy radial operation conditions and reliability indices. To overcome these time consuming and sub-optimal problem characteristics, this paper applied Genetic-Tabu algorithm. The Genetic-Tabu algorithm is a Tabu search combined with Genetic algorithm to complement the weak points of each algorithm. The case studies with 7 bus distribution network showed that not the loss reduction but also the reliability cost should be considered to achieve the economic service restoration and reconfiguration in the distribution network. The results of suggested Genetic-Tabu algorithm and simple Genetic algorithm are compared in the case study also.

• 한국미생물학회:학술대회논문집
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• 한국미생물학회 1991년도 춘계학술발표대회 논문집
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• pp.157-172
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• 1991

• 한국산학기술학회논문지
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• 제17권8호
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• pp.344-351
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• 2016

열차 운영사의 관점에서, 열차 운행에 관한 주요 관심사는 정해진 운행시간의 준수뿐만 아니라, 그와 동시에 열차 운행을 위해 소비되는 에너지 량을 최소화하는 것이다. 일반적인 수동 운전시, 기관사는 운행 노선의 특성에 따라 미리 규정된 최대 속도 프로파일을 기준으로, 노치를 제어함에 따라 규정 속도를 넘지 않도록 열차의 가감속을 조절한다. 이때 규정 속도를 준수하면서 동시에, 전체 운행 중에 소요되는 에너지 량의 절감을 위해, 기관사가 적절한 노치를 선택할 수 있는 구간별 노치 지정 가이드가 있어야 하며, 이는 운행구간에서의 노치 단계 최적화라는 절차를 통해 가능하다. 본 논문에서는 일반적인 운행 환경뿐만 아니라, 열차가 운행 중에 일시적으로 잔여구간의 트랙 정보 또는 규정속도 정보가 변경되는 이벤트 발생시에도 변화된 정보들을 기반으로 실시간으로 잔역구간의 노치 단계를 최적화 할 수 있는 유전 알고리즘을 활용한 실시간 노치 최적화 방안을 제안하였다. 또한 유전 알고리즘을 통해 얻어진 최적해를 적용할 때 에너지 절감 효과와 최적해 수렴특성에 관하여 분석하였다.

• 상하수도학회지
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• 제32권5호
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• pp.421-434
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• 2018

Water supply/intake pumps operation use 70~80% of power costs in water treatment plants. In the water treatment plant, seasonal and hourly differential electricity rates are applied, so proper pump scheduling can yield power cost savings. Accordingly, the purpose of this study was to develop an optimal water supply pump scheduling scheme. An optimal operation method of water supply pumps by using genetic algorithm was developed. Also, a method to minimize power cost for water supply pump operation based on pump performance derived from the thermodynamic pump efficiency measurement method was proposed. Water level constraints to provide sufficient disinfection performance in a clearwell and reservoirs were calibrated. In addition, continuous operation time constraints were calibrated to prevent frequent pump switching. As a result of optimization, savings ratios during 7 days in winter and summer were 4.5% and 5.1%, respectively. In this study, the method for optimal water pump operation was developed to secure disinfection performance in the clearwell and to save power cost. It is expected that it will be used as a more advanced optimal water pump operation method through further studies such as water demand forecasting and efficiency according to pump combination.

• 대한전기학회논문지:전력기술부문A
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• 제53권9호
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• pp.481-486
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• 2004

In operation of distribution system, $DG_s$ Distributed Generations) are installed as an alternative of extension and establishment of substations and transmission and distribution lines according to increasing power demand. In operation planning of $DG_s$, determining optimal capacity and allocation gets economical pro(it and improves power reliability. This paper proposes determining a optimal number, size and allocation of $DG_s$ needed to minimize operation cost of distribution system. Capacity of $DG_s$ (or economical operation of distribution system estimated by the load growth and line capacity during operation planning duration, DG allocations are determined to minimize total cost with power buying cost. operation cost of DG, loss cost and outage cost using GA(Genetic Algorithm).