• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2006.05a
• /
• pp.1133-1138
• /
• 2006

This paper considers a hybrid flow shop scheduling problem for the objective of minimizing the number of tardy jobs. The hybrid flow shop consists of two stages in series, each of which has multiple identical parallel machines, and the problem is to determine the allocation and sequence of jobs at each stage. A branch and bound algorithm that gives the optimal solutions is suggested that incorporates the methods to obtain the lower and upper bounds. Dominance properties are also derived to reduce the search space. To show the performance of the algorithm, computational experiments are done on randomly generated problems, and the results are reported.

• Proceedings of the SAREK Conference
• /
• 2009.06a
• /
• pp.236-241
• /
• 2009

After analyzing the characteristics of the cooling dryer, the mixed cooling dryer was developed by adding the desiccant dryer which supplement the cooling dryer's demerits. Also, the hybrid desiccant cooling dryer was developed to use effectively the exhaust heat energy of the cooling dryer. It could make a more that 20 percent reduction in energy compared with the mixed desiccant cooling dryer. It has become essential to supply this equipment and search the suitable drying product.

• Journal of Korean Institute of Industrial Engineers
• /
• v.33 no.2
• /
• pp.213-220
• /
• 2007

This paper considers a two-stage hybrid flow shop scheduling problem for the objective of minimizing the number of tardy jobs. Each job is processed through the two production stages in stages, each of which has multiple identical parallel machines. The problem is to determine the allocation and sequence of jobs at each stage. A branch and bound algorithm that gives the optimal solutions is suggested that incorporates the methods to obtain the lower and upper bounds. Dominance properties are also suggested to reduce the search space. To show the performance of the algorithm, computational experiments are done on randomly generated problems, and the results are reported.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.05a
• /
• pp.325-329
• /
• 2003

This paper discuss the method of the system's efficient control using a Intelligent hybrid algorithm in nonlinear dynamics systems. Existing neural network and genetic algorithm for the control of non-linear systems work well in static states. but it be not particularly good in changeable states and must re-learn for the control of the system in the changed state. This time spend a lot of time. For the solution of this problem we suggest the intelligent hybrid self-tuning controller. it includes neural network, genetic algorithm and immune system. it is based on neural network, and immune system and genetic algorithm are added against a changed factor. We will call a change factor an antigen. When an antigen broke out, immune system come into action and genetic algorithm search an antibody. So the system is controled more stably and rapidly. Moreover, The Genetic algorithm use the memory address of the immune bank as a genetic factor. So it brings an advantage which the realization of a hardware easy.

• Korean Management Science Review
• /
• v.30 no.2
• /
• pp.31-42
• /
• 2013

The vehicle routing problem (VRP) can be described as a problem to find the optimum traveling routes from one or several depot (s) to number of geographically scattered customers. This study executes a revised Heterogeneous Vehicle Routing Problem (HVRP) to minimize the cost that needs to conduct efficiently the snow removal operations of Air Wing under available resources and limited operations time. For this HVRP, we model the algorithm of an hybrid Ant Colony System (ACS). In the initial step for finding a solution, the modeled algorithm applies various alterations of a parameter that presents an amount of pheromone coming out from ants. This improvement of the initial solution illustrates to affect to derive better result ultimately. The purpose of this study proves that the algorithm using Hybrid heuristic incorporated in tabu and ACS develops the early studies to search best solution.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2003.05a
• /
• pp.520-525
• /
• 2003

The determination of unknown parameters in rotating machinery is a difficult task and optimization techniques represent an alternative technique for parameter identification. The Simulated Annealing(SA) and Genetic Algorithm(GA) are powerful global optimization algorithm. This paper proposes new hybrid algorithm which combined GA with SA and local search algorithm for the purpose of parameter identification. Numerical examples are also presented to verify the efficiency of proposed algorithm. And, this paper presents the general methodology based on hybrid algorithm to identify unknown bearing parameters of flexible rotors using measured unbalance responses. Numerical examples are used to ilustrate the methodology used, which is then validated experimentally.

• The Journal of Information Systems
• /
• v.17 no.3
• /
• pp.39-58
• /
• 2008

Mixed Chinese Postman Problem (MCPP) is a practical generalization of the classical Chinese Postman Problem (CPP) and it could be applied in many real world. Although MCPP is useful in terms of reality, MCPP has been proved to be a NP-complete problem. To find optimal solutions efficiently in MCPP, we can reduce searching space to be small effective searching space containing optimal solutions. We propose GENIIS methodology, which is a kind of hybrid algorithm combines the approximate algorithms and genetic algorithm. To get good solutions in the effective searching space, GENIIS uses approximate algorithm and genetic algorithm. This paper validates the usefulness of the proposed approach in a simulation. The results of our paper could be utilized to increase the efficiencies of network and transportation in business.

• Journal of Korea Multimedia Society
• /
• v.19 no.8
• /
• pp.1597-1608
• /
• 2016

Recently, a number of solutions were proposed to address the challenges and issues of vehicular networks. Vehicular Cloud Computing (VCC) is one of the solutions. The vehicular cloud computing is a new hybrid technology that has a remarkable impact on traffic management and road safety by instantly using vehicular resources. In this paper, we study an important vehicular cloud service, content-based delivery, that allows future vehicular cloud applications to store, share and search data totally within the cloud. We design a VCC-based system architecture for efficient sharing of vehicular contents, and propose a Hierarchical Hybrid Content Delivery scheme using Bloom Filter (H2CDBF) for efficient vehicular content delivery in Vehicular Ad-hoc Networks (VANETs). The performance of the proposed H2CDBF is evaluated through an analytical model, and is compared to the proactive content discovery scheme, Bloom-Filter Routing (BFR).

• Advances in Computational Design
• /
• v.8 no.1
• /
• pp.37-59
• /
• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.

• Steel and Composite Structures
• /
• v.51 no.6
• /
• pp.713-727
• /
• 2024

Damage detection in structures using the change of modal parameters (modal shapes and natural frequencies) has achieved satisfactory results. However, as modal shapes and natural frequencies alone may not provide enough information to accurately detect damages. Therefore, a hybrid singular value decomposition and deep belief network approach is developed to effectively identify damages in aluminum plate structures. Firstly, damage locations are determined using singular value decomposition (SVD) to reveal the singularities of measured displacement modal shapes. Secondly, using experimental modal analysis (EMA) to measure the natural frequencies of damaged aluminum plates as inputs, deep belief network (DBN) is employed to search damage severities from the damage evaluation database, which are calculated using finite element method (FEM). Both simulations and experimental investigations are performed to evaluate the performance of the presented hybrid method. Several damage cases in a simply supported aluminum plate show that the presented method is effective to identify multiple damages in aluminum plates with reasonable precision.