• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.309-313
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• 1998

Delay time of receiving and delivery is one of important factors that should be considered in the evaluation of the customer service level of a container terminal. In this study, dispatching rules are tested with the objective of minimizing the service delay time for arriving outside trucks. A dynamic programming model is suggested for a static dispatching problem in which all the truck arrivals are known in advance. In order to overcome the excessive computational time of the dynamic programming technique, several heuristic rules are suggested that can be applied in practices. A simulation study is carried out to test the performances of the heuristic rules.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5780-5802
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• 2017

Cloud computing system consists of distributed resources in a dynamic and decentralized environment. Therefore, using cloud computing resources efficiently and getting the maximum profits are still challenging problems to the cloud service providers and cloud service users. It is important to provide the efficient scheduling. To schedule cloud resources, numerous heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search (CS) algorithms have been adopted. The paper proposes a Modified Particle Swarm Optimization (MPSO) algorithm to solve the above mentioned issues. We first formulate an optimization problem and propose a Modified PSO optimization technique. The performance of MPSO was evaluated against PSO, and GA. Our experimental results show that the proposed MPSO minimizes the task execution time, and maximizes the resource utilization rate.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.319-325
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• 2001

Recently. AGV systems are used to serve the raw material to each work stations automatically. There exists a trade-off between the adequate service supply and the number of purchased AGVs. Also, to reduce the overall production cost, the amount of inventory hold on the shop floor should be considered. In this paper, we present a heuristic technique for determining the number of AGVs which includes the net present fixed costs of each station, each purchased AGV, delivering cost, stock inventory cost, and safety stock inventory cost. Secondly, by using a genetic algorithm, the optimal number of AGVs and the optimal reorder quantity at each station are decided. Lastly, to verify then heuristic algorithm, we have done a computer simulation with different GA parameters.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.2
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• pp.101-114
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• 2000

Multicasting is the simultaneous transmission of data to multiple destinations. In multicast ATM networks the effect of failures on transmission links or nodes can be catastrophic so that the issue of survivability is of great importance. However little attention has been paid to the problem of multicast restoration. This paper presents an efficient heuristic technique for routing backup virtual paths in ulticast networks with link failure. Genetic algorithm is employed here as a heuristic. In the application of genetic algorithm to the problem, a new genetic encoding and decoding method and genetic operators are proposed in this paper. The other several heuristics are also presented in order to assess the performance of the proposed algorithm. Experimental results demonstrate that our algorithm is a promising approach to solving the problem.

• ETRI Journal
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• v.40 no.5
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• pp.570-581
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• 2018

This work analyzes the performance of implementable detectors for the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) technique under specific and realistic operation system conditions, including antenna correlation and array configuration. A time-domain channel model was used to evaluate the system performance under realistic communication channel and system scenarios, including different channel correlation, modulation order, and antenna array configurations. Several MIMO-OFDM detectors were analyzed for the purpose of achieving high performance combined with high capacity systems and manageable computational complexity. Numerical Monte Carlo simulations demonstrate the channel selectivity effect, while the impact of the number of antennas, adoption of linear against heuristic-based detection schemes, and the spatial correlation effect under linear and planar antenna arrays are analyzed in the MIMO-OFDM context.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.9
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• pp.513-520
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• 2003

Service restoration is an emergency control in distribution constrol centers to restore out-of-service area as soon as possible when a fault occurs in distribution networks. therefore, it requires fast computation time and high quality solutions for load balancing. In this paper. a load balance index and heuristic guided best-first search are proposed for these problem. The proposed algorithm consists of two parts. One is to set up a decision tree to represent the various switching operations available. Another is to identify the most effective the set of switches using proposed search technique and a load balance index. Test results on the KEPCO's 108 bus distribution system show that the performance is efficient and robust.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.1
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• pp.90-99
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• 2003

Object clustering, which makes classification for a set of objects into a number of groups such that objects included in a group have similar characteristic and objects in different groups have dissimilar characteristic each other, has been exploited in diverse area such as information retrieval, data mining, group technology, etc. In this study, an object-clustering problem with similarity coefficients between objects is considered. At first, an evaluation function for the optimization problem is defined. Then, a genetic algorithm and local optimization technique based on heuristic method are proposed and used in order to obtain near optimal solutions. Solutions from the genetic algorithm are improved by local optimization techniques based on object relocation and cluster merging. Throughout extensive experiments, the validity and effectiveness of the proposed algorithms are tested.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.238-245
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• 2023

As technologies have been more quickly developed in this 4th Industry Revolution era, their application to defense industry has been also growing. With these much advanced technologies, we attempt to use Manned-Unmanned Teaming systems in various military operations. In this study, we consider the Location-Routing Problem for reconnaissance surveillance missions of the maritime manned-unmanned surface vehicles. As a solution technique, the two-phase method is presented. In the first location phase, the p-median problem is solved to determine which nodes are used as the seeds for the manned vehicles using Lagrangian relaxation with the subgradient method. In the second routing phase, using the results obtained from the location phase, the Vehicle Routing Problems are solved to determine the search routes of the unmanned vehicles by applying the Location Based Heuristic. For three network data sets, computational experiments are conducted to show the performance of the proposed two-phase method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1615-1622
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• 2009

Generally, the Terminal Based Navigation System(TBNS) used embedded road data searches a path that has less qualitative than The Center Based Navigation System(CBNS). TBNS has not used real time road data but it is recently able to use it with technique such as TPEG. However, it causes to increase a cost of exploring by using real time road data for improvement quality of a path, because of limited performance. In this paper, we propose a Dynamic Heuristic to improve quality of path in the TBNS. Dynamic Heuristic(DH) is not fixed data and is dynamically modified using transferred real time road data from server. In this paper, we propose path-lading algorithm with Maximum Speed Dynamic Heuristic (DH-MAX) and do an experiment. The DH-MAX is to be used the highest speed as DH, in real map divided by same size. And proposed algorithm searches path using the priority searching only of the fixed data, but also the highest speed with real time information. In the performance test, the quality of path is enhanced but the cost of searching is increased than A* algorithm.