• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.142-148
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• 2008

Facility Layout Planning is concerned with how to arrange facilities necessary for production in a given space. Its objective is often to minimize the total sum of all material flows multiplied by the distance among facilities. FLP belongs to NP complete problem; i.e., the number of possible layout solutions increases with the increase of the number of facilities. Thus, meta heuristics such as Genetic Algorithm (GA) and Simulated Annealing have been investigated to solve the FLP problems. However, one of the biggest problems which lie in the existing meta heuristics including GA is hard to find an appropriate combinations of parameters which result in optimal solutions for the specific problem. The Ant System algorithm with elitist and ranking strategies is used to solve the FLP problem as an another good alternative. Experimental results show that the AS algorithm is able to produce the same level of solution quality with less sensitive parameters selection comparing to the ones obtained by applying other existing meta heuristic algorithms.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2327-2329
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• 2000

This paper addresses the use of a nonlinear modeling procedure which construct a wavelet-based fuzzy model using genetic algorithm. A fuzzy inference system has the functional equivalence with a wavelet transform. Therefore, a wavelet-based fuzzy model using GA inherits the advantage of wavelet transform. Hereby, its performance is promoted. By help of the ability of GA to search the optimum globally, parameters of wavelet transform is determined closely to the optimal point. The feasibility of the proposed fuzzy model is proved by modelling a highly nonlinear function and comparing it with previous research.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1767-1771
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• 2004

A 4D-PTV system was constructed. The measurement system consists of three high-speed high-definition cameras, Nd-Yag laser(10mJ, 2000fps) and a host computer. The GA-3D-PTV algorithm was used to extract three-dimensional velocity vectors in the measurement volume. A horizontal impinged jet flow was measured with the constructed system. The Reynolds number is about 40,000. Spatial temporal evolution of the jet flow was examined in detail and physical properties such as spatial distributions of vorticity and turbulent kinetic energy were obtained with the constructed system.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.822-827
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• 2004

In this paper, an intelligent fault diagnosis system is proposed for induction motors through the combination of feature extraction, genetic algorithm (GA) and neural network (ANN) techniques. Features are extracted from motor vibration signals, while reducing data transfers and making on-line application available. GA is used to select most significant features from whole feature database and optimize the ANN structure parameter. Optimized ANN diagnoses the condition of induction motors online after trained by the selected features. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origin on the induction motors. The results of the test indicate that the proposed system is promising for real time application.

• IE interfaces
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• v.14 no.2
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• pp.111-119
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• 2001

A tandem AGV system is based on partitioning all the stations into non-overlapping single vehicle closed loops with additional stations provided as an interface between adjacent loops. For an efficient use of this configuration, it is required to solve the load routing problem(LRP), which is primarily based on the fact that a load may be handled by several vehicles and moved through several loops before it reaches its destination. In this paper, a heuristic based on genetic algorithm(GA) is first developed to solve LRP. The first model obtains the optimal route of each job and the optimal direction of each loop when the vehicle in each loop travels unidirectionally. The second GA model obtaines the optimal polling sequence of the empty vehicle in each loop, when the vehicle can move bidirectionally.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.2
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• pp.140-149
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• 2001

The paper discusses the problem of finding the Origin-Destination(O-D) shortest paths in internodal transportation networks with time-schedule constraints. The shortest path problem on the internodal transportation network is concerned with finding a path with minimum distance, time, or cost from an origin to a destination using all possible transportation modalities. The time-schedule constraint requires that the departure time to travel from a transfer station to another node takes place only at one of pre-specified departure times. The scheduled departure times at the transfer station are the times when the passengers are allowed to leave the station to another node using the relative transportation modality. Therefore, the total time of a path in an internodal transportation network subject to time-schedule constraints includes traveling time and transfer waiting time. In this paper, a genetic algorithm (GA) approach is developed to deal with this problem. The effectiveness of the GA approach is evaluated using several test problems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.241-244
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• 2000

The optimization method based on an enhanced genetic algorithms is proposed for multimodal function optimization in this paper This method is consisted of two main steps. The first step is global search step using the genetic algorithm(GA) and function assurance criterion(FAC). The belonging of an population to initial solution group is decided according to the FAC. The second step is to decide resemblance between individuals and research optimum solutions by single point method in reconstructive research space. Two numerical examples are also presented in this paper to comparing with conventional methods.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.3
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• pp.362-372
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• 2008

Manufacturers, or vendors, and their customers continue to adopt vendor-managed inventory(VMI) program to improve supply chain performance through collaboration achieved by consolidating replenishment responsibility upstream with vendors. In this paper, we construct a mixed integer linear programming model and propose a genetic algorithm for the integrated inventory and routing problems with lost sales maximizing the total profit in the VMI supply chains which comprise of a single manufacturer and multi-retailer. The proposed GA is compared with the mathematical model on the various sized test problems with respect to the solution quality and computation time. As a result, the GA demonstrates the capability of reaching solutions that are very close to those obtained by the mathematical model for small problems and stay within 3.2% from those obtained by the mathematical model for larger problems, with a much shorter computation time. Finally, we investigate the effects of the cost and operation variables on the total profit of the problem as well as the GA performance through the sensitivity analyses.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.43-54
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• 2005

A flexible assembly line (FAL) is a production system that assembles various parts in unidirectional flow line with many constraints and manufacturing flexibilities. In this research we deal with a FAL balancing problem with the objective of minimizing the maximum workload allocated to the stations. However, almost all the existing researches do not appropriately consider various constraints due to the problem complexity. Therefore, this study addresses a balancing problem of FAL with many constraints and manufacturing flexibilities, unlike the previous researches. We use a genetic algorithm (GA) to solve this problem. To apply GA to FAL. we suggest a genetic representation suitable for FAL balancing and devise evaluation method for individual's fitness and genetic operators specific to the problem, including efficient repair method for preserving solution feasibility. After we obtain a solution using the proposed GA. we use a heuristic method for reassigning some tasks of each product to one or more stations. This method can improve workload smoothness and raise work efficiency of each station. The proposed algorithm is compared and analyzed in terms of solution quality through computational experiments.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.537-542
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• 2001

In recent engineering, the designer has become more and more dependent on computer simulation. But defining exact model using computer simulation is too expensive and time consuming in the complicate systems. Thus, designers often use approximation models, which express the relation between design variables and response variables. These models are called metamodel. In this paper, we introduce one of the metamodel, named Kriging. This model employs an interpolation scheme and is developed in the fields of spatial statistics and geostatistics. This class of interpolating model has flexibility to model response data with multiple local extreme. By reason of this multi modality, we can't use any gradient-based optimization algorithm to find global extreme value of this model. Thus we have to introduce global optimization algorithm. To do this, we introduce DE(Differential Evolution). DE algorithm is developed by Ken Price and Rainer Storn, and it has recently proven to be an efficient method for optimizing real-valued multi-modal objective functions. This algorithm is similar to GA(Genetic Algorithm) in populating points, crossing over, and mutating. But it introduces vector concept in populating process. So it is very simple and easy to use. Finally, we show how we determine Kriging metamodel and find global extreme value through two mathematical examples.