• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.121-135
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• 2007

Constraint directed scheduling techniques, representing problem constraints explicitly and constructing schedules by constrained heuristic search, have been successfully applied to real world scheduling problems that require satisfying a wide variety of constraints. However, there has been little basic research on the representation and optimization of the objective value of a schedule in the constraint directed scheduling literature. In particular, the cost objective is very crucial for enterprise decision making to analyze the effects of alternative business plans not only from operational shop floor scheduling but also through strategic resource planning. This paper aims to explicitly represent and optimize the total cost of a schedule including the tardiness and inventory costs while satisfying non-relaxable constraints such as resource capacity and temporal constraints. Within the cost based scheduling framework, a cost propagation algorithm is presented to update cost information throughout temporal constraints within the same job.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.342-345
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• 2014

Network intrusion detection system based on machine learning methods is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features from generally used features to detect network intrusion requires extensive computing resources. For instance, the number of possible feature combinations from given n features is $2^n-1$. In this paper, to tackle this problem we propose a optimal feature selection algorithm. Proposed algorithm is based on the local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In addition, the accuracy of clusters which obtained using selected feature components and k-means clustering algorithm is adopted to evaluate a feature assembly. In order to estimate the performance of our proposed algorithm, comparing with a method where all features are used on NSL-KDD data set and multi-layer perceptron.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5723-5743
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• 2018

The evolving internet-based services demand high-speed data transmission in conjunction with scalability. The next generation optical network has to exploit artificial intelligence and cognitive techniques to cope with the emerging requirements. This work proposes a novel way to solve the dynamic provisioning problem in optical network. The provisioning in optical network involves the computation of routes and the reservation of wavelenghs (Routing and Wavelength assignment-RWA). This is an extensively studied multi-objective optimization problem and its complexity is known to be NP-Complete. As the exact algorithms incurs more running time, the heuristic based approaches have been widely preferred to solve this problem. Recently the software-defined networking has impacted the way the optical pipes are configured and monitored. This work proposes the dynamic selection of path computation algorithms in response to the changing service requirements and network scenarios. A software-defined controller mechanism with a novel packet matching feature was proposed to dynamically match the traffic demands with the appropriate algorithm. A software-defined controller with Path Computation Element-PCE was created in the ONOS tool. A simulation study was performed with the case study of dynamic path establishment in ONOS-Open Network Operating System based software defined controller environment. A java based NOX controller was configured with a parent path computation element. The child path computation elements were configured with different path computation algorithms under the control of the parent path computation element. The use case of dynamic bulk path creation was considered. The algorithm selection method is compared with the existing single algorithm based method and the results are analyzed.

• Journal of Information Technology Services
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• v.17 no.4
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• pp.101-117
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• 2018

Companies are responding appropriately to the rapidly changing business environment and striving to lead those changes. As part of that, we are meeting our strategic goals through IT projects, which increase the number of simultaneous projects and the importance of project portfolio management for successful project execution. It also strives for efficient deployment of human resources that have the greatest impact on project portfolio management. In the early stages of project portfolio management, it is very important to establish a reasonable manpower plan and allocate performance personnel. This problem is a problem that can not be solved by linear programming because it is calculated through the standard deviation of the input ratio of professional manpower considering the uniformity of load allocated to the input development manpower and the importance of each project. In this study, genetic algorithm, one of the heuristic methods, was applied to solve this problem. As the objective function, we used the proper input ratio of projects, the input rate of specialist manpower for important projects, and the equal load of workload by manpower. Constraints were not able to input duplicate manpower, Was used as a condition. We also developed a program for efficient application of genetic algorithms and confirmed the execution results. In addition, the parameters of the genetic algorithm were variously changed and repeated test results were selected through the independent sample t test to select optimal parameters, and the improvement effect of about 31.2% was confirmed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1632-1638
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• 2020

In this paper, we investigate a multi-beam satellite communication system where multiple terminals transmit information signals to the gateway via a satellite. The satellite is equipped with phased array antennas to form multiple spot beams of which bandwidths are not identically allocated. We formulate an optimization problem to maximize fairness of beam bandwidth allocation. In order to solve the problem, we propose two heuristic algorithms; iterative beam bandwidth allocation (IBBA) and request ratio-based beam bandwidth allocation (RRBBA) algorithms. The IBBA algorithm iteratively equalizes the ratio of allocated bandwidth of each beam to their resource request while the RRBBA algorithm allocates beam bandwidth calculated from the ratio. Simulation results show that the IBBA algorithm has close fairness performance to the optimum while the RRBBA algorithm has less performance than the IBBA algorithm at the price of reduced computational complexity.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.69-76
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• 2022

The traveling salesman problem(TSP) is one of the most famous combinatorial optimization problem. So far, many metaheuristic search algorithms have been proposed to solve the problem, and one of them is local search. One of the very important factors in local search is neighbor generation method, and random-based neighbor generation methods such as inversion have been mainly used. This paper proposes 4 new greedy-based neighbor generation methods. Three of them are based on greedy insertion heuristic which insert selected cities one by one into the current best position. The other one is based on greedy rotation. The proposed methods are applied to first-choice hill-climbing search and simulated annealing which are representative local search algorithms. Through the experiment, we confirmed that the proposed greedy-based methods outperform the existing random-based methods. In addition, we confirmed that some greedy-based methods are superior to the existing local search methods.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.59-60
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• 2023

The automation of container terminals is an important factor that determines port competitiveness, and global advanced ports tend to strengthen their competitiveness through container terminal automation. The operational efficiency of the AGV, which is an essential transport equipment of the automated terminal, can improve the productivity of the automated terminal. The operation of AGVs in automated container terminals differs from that of conventional container terminals, as it is based on an automated system in which AGVs travel along designated paths and operate according to assigned tasks, requiring consideration of factors such as workload, congestion, and collisions. To prevent such problems and improve the efficiency of AGV operations, a more sophisticated model is necessary. Thus, this paper proposes an AGV scheduling model that takes into account the AGV travel path and task assignment within the terminal The model prevent the problem of deadlock and. various cases are generated by changing AGV algebra and number of tasks to create AGV driving situations and evaluate the proposed algorithm through algorithm and optimization analysis.

• Journal of The Korean Institute of Defense Technology
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• v.5 no.2
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• pp.17-22
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• 2023

In the midst of the development of science and technology based on the 4th industrial revolution, the ROK Army is moving forward with the ARMY TIGER 4.0 system, a ground combat system that combines future advanced science and technology. The system is developing around an AI-based hyper-connected ground combat system, and has mobility, intelligence, and networking as core concepts. Especially, the dronebot combat system is used as a compound word that refers to unmanned combat systems including drones and ground unmanned systems. In future battlefields, it is expected that the use of unmanned and artificial intelligence-based weapon systems will increase. During the transition to a complete unmanned system, it is a very important issue to ensure connectivity individual unmanned systems themselves or between manned and unmanned systems on the battlefield. This paper introduces the Multiplexer Allocation Problem (MAP) for effective command control and communication of UAV/UGV, and proposes a heuristic algorithm. In addition, the performance of the proposed algorithm is analyzed by comparing the solutions and computing time. Also, we discuss future research area for the MAP.

• The Journal of Bigdata
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• v.9 no.1
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• pp.141-152
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• 2024

In the biopharmaceutical contract manufacturing organization (CMO) business, establishing a production schedule that satisfies the due date for various customer orders is crucial for competitiveness. In a CMO process, each order consists of multiple batches that can be allocated to multiple production lines in small batch units for parallel production. This study proposes a meta-heuristic algorithm to establish a scheduling plan that minimizes the total delivery delay of orders in a CMO process with identical parallel machine. Inspired by biological evolution, the proposed algorithm generates random data structures similar to chromosomes to solve specific problems and effectively explores various solutions through operations such as crossover and mutation. Based on real-world data provided by a domestic CMO company, computer experiments were conducted to verify that the proposed algorithm produces superior scheduling plans compared to expert algorithms used by the company and commercial optimization packages, within a reasonable computation time.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.67-76
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• 2024

In this study, we address the problem of target detection using multiple agents. Specifically, the detection problem involving mobile agents necessitates additional strategies for path planning. The objective is to maximize the total utility derived from the detection process over a specific period. This detection problem incorporates realistic utility values by considering a stochastic process based on the Poisson process, which accounts for the changing probability of target event occurrence over time. The objective function is nonlinear and is classified as an NP-hard problem. To identify an effective solution within an efficient computation time, this study demonstrates that the objective function possesses the characteristic of submodularity. Using this property, we propose a heuristic algorithm designed to obtain a reasonable strategy with relatively low computational time. The proposed algorithm shows solution performance and the ability to generate solutions within an appropriate computation time through theoretical and experimental results.