• Journal of the Korea Safety Management & Science
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• v.10 no.1
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• pp.145-153
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• 2008

The vehicle routing problem determines each vehicle routes to find the transportation costs, subject to meeting the customer demands of all delivery points in geography. Vehicle routing problem is known to be NP-hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study aims to develop a heuristic method which combines guided local search with a tabu search in order to minimize the transportation costs for the vehicle routing assignment and uses ILOG programming library to solve. The computational tests were performed using the benchmark problems. And computational experiments on these instances show that the proposed heuristic yields better results than the simple tabu search does.

• Smart Structures and Systems
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• v.20 no.5
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• pp.607-618
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• 2017

In this research, a newly developed nature-inspired optimization method, the Lion Pride Optimization algorithm (LPOA), is utilized for optimal design of composite steel box girder bridges. A composite box girder bridge is one of the common types of bridges used for medium spans due to their economic, aesthetic, and structural benefits. The aim of the present optimization procedure is to provide a feasible set of design variables in order to minimize the weight of the steel trapezoidal box girders. The solution space is delimited by different types of design constraints specified by the American Association of State Highway and Transportation Officials. Additionally, the optimal solution obtained by LPOA is compared to the results of other well-established meta-heuristic algorithms, namely Gray Wolf Optimization (GWO), Ant Lion Optimizer (ALO) and the results of former researches. By this comparison the capability of the LPOA in optimal design of composite steel box girder bridges is demonstrated.

• Journal of the Korea Safety Management & Science
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• v.8 no.3
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• pp.143-157
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• 2006

The purpose of this thesis is to develop a heuristic algorithm evaluating the layout to maximize number of boxes when a single-box-type is packed into a container. Actually because three-dimensional problems are involved in NP-complete, much study has been focused on heuristic algorithms. The basic concept of this study is as follows. The problem is solved as palletizing problems with each side of the container respectively. By using the height of boxes, the problem is extended to a three-dimensional problem. Consequently, through this thesis, we can get good solution and the result is displayed by three-dimensional computer graphic.

• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.37-45
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• 2013

In the absence of a single deterministic rule for the channel assignment problems, heuristic algorithms are predominantly employed to partly solve the problems. This paper thus proposes deterministic rules for the channel assignment problems. These deterministic rules have successfully yielded the optimal solution when applied to the Philadelphia's 9 case examples.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.2
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• pp.184-194
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• 2014

Numerous studies have been studied to provide an efficient solution for the Nurse Rostering Problem (NRP), most of which have suffered from its complexity arising from incorporating nurse's work shift and ability. The test-bed data for the NRP is released for the public Competition in 2010. This study suggests a new mixed integer programming for Nurse Rostering Problem and develops a hybrid approach, where a hierarchical decomposition and the corresponding optimization are combined. The computation experiment is performed to show that the suggested algorithms may give a better solution in various instances, compared to the one appeared in the literature.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.123-137
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• 1995

Most of solution methods in scheduling attempt to generate good solutions by either developing algorithms or heuristic rules. However, scheduling problems in the real world require considering more factors such as multiple objectives, different combinations of heuristic rules due to problem characteristics. In this respect, the traditional mathematical a, pp.oach showed limited performance so that new a, pp.oaches need to be developed. Expert system is one of them. When an expert system is developed for scheduling one of the most difficult processes faced could be knowledge acquisition on scheduling heuristics. In this paper we propose a method for the acquisition of knowledge on the selection of scheduling heuristics using Dempster-Shafer Theory(DST). We also show the examples in the multi-objectives environment.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.4
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• pp.135-141
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• 2013

String matching is one of the key algorithms in network security and many areas could be benefit from a faster string matching algorithm. Based on the most efficient string matching algorithm in sual applications, the Boyer-Moore (BM) algorithm, a novel algorithm called RQS is proposed. RQS utilizes an improved bad character heuristic to achieve bigger shift value area and an enhanced good suffix heuristic to dramatically improve the worst case performance. The two heuristics combined with a novel determinant condition to switch between them enable RQS achieve a higher performance than BM both under normal and worst case situation. The experimental results reveal that RQS appears efficient than BM many times in worst case, and the longer the pattern, the bigger the performance improvement. The performance of RQS is 7.57~36.34% higher than BM in English text searching, 16.26~26.18% higher than BM in uniformly random text searching, and 9.77% higher than BM in the real world Snort pattern set searching.

• IE interfaces
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• v.20 no.2
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• pp.121-132
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• 2007

The facility layout problem (FLP) involves the positioning of facilities within a given workplace in order to minimize the material flow distance between facilities. In this paper, we focus on the FLP in which the each facility has a rectangular shape and an input and output points. We develop a heuristic algorithm in which the facilities are arranged according to the layout algorithm using center of gravity and then simulated annealing algorithm is applied to improve the solution. A comparison of the computational results with exiting algorithms shows that the proposed algorithm provides better solutions in reasonable range of computational time.

• Korean Journal of Materials Research
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• v.27 no.6
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• pp.312-317
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• 2017

In the construction of a white LED, the region of the red emission is a very important factor. Red light emitting materials play an important role in improving the color rendering index of commercial lighting. These materials also increase the color gamut of display products. Therefore, the development of novel phosphors with red emission and the study of color tuning are actively underway to improve product quality. In the present study, heuristic algorithms were used to search for phosphors capable of increasing the color rendering index and color gamut. Using a heuristic algorithm, the phosphors that were identified were $SrGe_4O_9:Mn^{4+}$ and $BaGe_4O_9:Mn^{4+}$. Emission spectra study confirmed that these phosphors emit light in the deep red wavelength region, which can fulfill the requirement for the improvement in color rendering index and color gamut for a white LED.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.56-61
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• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.