• Journal of Korean Society of Transportation
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• v.17 no.4
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• pp.59-69
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• 1999

A Freeway corridor is a network consisting of a few Primary longitudinal roadways (freeway or major arterial) carrying a major traffic movement with interconnecting roads which offer the motorist alternative paths to his/her destination. Control measures introduced to ameliorate traffic performance in freeway corridors typically include ramp metering at the freeway entrances, and signal control at each intersections. During a severe freeway incident, on-ramp metering usually is not adequate to relieve congestion effectively. Diverting some traffic to the Parallel surface street to make full use of available corridor capacity will be necessary. This is the purpose of the traffic management system. So, an integrated traffic control scheme should include three elements. (a)on-ramp metering, (b)off-ramp diversion and (c)signal timing at surface street intersections. The purpose of this study is to develop an integrated optimal control model in a freeway corridor. By approximating the flow-density relation with a two-segment linear function. the nonlinear optimal control problem can be simplified into a set of Piecewise linear programming models. The formulated optimal-control Problem can be solved in real time using common linear program. In this study, program MPL(ver 4.0) is used to solve the formulated optimal-control problem. Simulation results with TSIS(ver 4.01) for a sample network have demonstrated the merits of the Proposed model and a1gorithm.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.389-394
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• 2008

The purpose of this research was to develop a methodology to determine whether conjunctive surface water and groundwater management could significantly reduce deficits in a river basin with a relatively limited alluvial aquifer. The Geum River basin is one of major river basins in South Korea. The upper region of the Geum River basin is typical of many river basins in Korea where the shape of river basin is narrow with small alluvial aquifer depths from 10m to 20m and where most of the groundwater pumped comes quickly from the steamflow. The basin has two surface reservoirs, Daecheong and Yongdam. The most recent reservoir, Yongdam, provides water to a trans-basin diversion, and therefore reduces the water resources available in the Geum River basin. After the completion of Yongdam reservoir, the reduced water supply in the Geum basin resulted in increasing conflicts between downstream water needs and required instream flows, particularly during the low flow season. Historically, the operation of groundwater pumping has had limited control and is administered separately from surface water diversions. Given the limited size of the alluvial aquifer, it is apparent that groundwater pumping is essentially taking its water from the stream. Therefore, the operation of the surface water withdrawals and groundwater pumping must be considered together. The major component of the conjunction water management in this study is a goal-programmin g based optimization model that simultaneously considers surface water withdrawals, groundwater pumping and instream flow requirements. A 10-day time step is used in the model. The interactions between groundwater pumping and the stream are handled through the use of response and lag coefficients. The impacts of pumping on streamflow are considered for multiple time periods. The model is formulated as a linear goal-programming problem that is solved with the commercial LINGO optimization software package.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.161-166
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• 2022

In this paper, we analyze the problems that occur when performing the big data analysis using R as a data analysis tool, and present the usefulness of the data analysis with SparkR which connects R and Spark to support distributed processing of big data effectively. First, we study the memory allocation problem of R which occurs when loading large amounts of data and performing operations, and the characteristics and programming environment of SparkR. And then, we perform the comparison analysis of the execution performance when linear regression analysis is performed in each environment. As a result of the analysis, it was shown that R can be used for data analysis through SparkR without additional language learning, and the code written in R can be effectively processed distributedly according to the increase in the number of nodes in the cluster.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3474-3497
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• 2016

Network Function Virtualization (NFV) and Software Defined Networking (SDN) are recently considered as very promising drivers of the evolution of existing middlebox services, which play intrinsic and fundamental roles in today's networks. To address the virtual service deployment issues that caused by introducing NFV or SDN to networks, this paper proposes an optimal solution by combining quantum genetic algorithm with cooperative game theory. Specifically, we first state the concrete content of the service deployment problem and describe the system framework based on the architecture of SDN. Second, for the service location placement sub-problem, an integer linear programming model is built, which aims at minimizing the network transport delay by selecting suitable service locations, and then a heuristic solution is designed based on the improved quantum genetic algorithm. Third, for the service amount placement sub-problem, we apply the rigorous cooperative game-theoretic approach to build the mathematical model, and implement a distributed algorithm corresponding to Nash bargaining solution. Finally, experimental results show that our proposed method can calculate automatically the optimized placement locations, which reduces 30% of the average traffic delay compared to that of the random placement scheme. Meanwhile, the service amount placement approach can achieve the performance that the average metric values of satisfaction degree and fairness index reach above 90%. And evaluation results demonstrate that our proposed mechanism has a comprehensive advantage for network application.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2372-2380
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• 2015

In this paper, we propose frequency allocation and path selection scheme in underlay cognitive radio (CR) networks using network coding. In the proposed scheme, we choose the path with consideration of network coding and interference temperature in underlay CR networks and propose an optimization problem to maximize the system throughput of secondary users (SUs). Then, we represent the proposed optimization problem as the multi-dimensional multiple-choice knapsack problem and give the theoretical upper bound for the system throughput of SUs by using linear programming. Finally, we compute the system throughput of SUs by using brute-force search (BFS) and link quality first (LQF) scheme in underlay CR networks. Simulation results show that the system throughput of SUs with BFS is higher than that with LQF in underlay CR networks with and without application of network coding, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2892-2913
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• 2016

Network virtualization promises to play a dominant role in shaping the future Internet by overcoming the Internet ossification problem. However, due to the injecting of additional virtualization layers into the network architecture, several new security risks are introduced by the network virtualization. Although traditional protection mechanisms can help in virtualized environment, they are not guaranteed to be successful and may incur high security overheads. By performing the virtual network (VN) embedding in a security-aware way, the risks exposed to both the virtual and substrate networks can be minimized, and the additional techniques adopted to enhance the security of the networks can be reduced. Unfortunately, existing embedding algorithms largely ignore the widespread security risks, making their applicability in a realistic environment rather doubtful. In this paper, we attempt to address the security risks by integrating the security factors into the VN embedding. We first abstract the security requirements and the protection mechanisms as numerical concept of security demands and security levels, and the corresponding security constraints are introduced into the VN embedding. Based on the abstraction, we develop three security-risky modes to model various levels of risky conditions in the virtualized environment, aiming at enabling a more flexible VN embedding. Then, we present a mixed integer linear programming formulation for the VN embedding problem in different security-risky modes. Moreover, we design three heuristic embedding algorithms to solve this problem, which are all based on the same proposed node-ranking approach to quantify the embedding potential of each substrate node and adopt the k-shortest path algorithm to map virtual links. Simulation results demonstrate the effectiveness and efficiency of our algorithms.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.6
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• pp.55-65
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• 2019

With the advent of the Fourth Industrial Revolution and changes in the educational environment, team-based assignments are increasing in university classes. Effective team formation in team-based class is an important issue that affects students' satisfaction and the effectiveness of education. However, previous studies mostly focused on post analysis on the results of team formation, which makes it difficult to use them in actual classes. In this paper, we present a mathematical model of how to create a balanced team that reflects students' abilities and other characteristics. Characteristic values for assignment may be scores, such as students' proficiency, binary values such as gender, and multi-values, such as grade or department. This problem is a type of equitable partitioning problem, which takes the form of 0-1 integer programming, and the objective function is linear or nonlinear, depending on how balance is achieved. The basic model or the extended model presented can be applied to the situation where teams are balanced in consideration of various factors in actual class.

• 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 Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.24-35
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• 2021

Due to increasing awareness on the treatment of end-of-use/life products, disassembly has been a fast-growing research area of interest for many researchers over recent decades. This paper introduces a novel lot-sizing problem that has not been studied in the literature, which is the service-parts lot-sizing with disassembly option. The disassembly option implies that the demands of service parts can be fulfilled by newly manufactured parts, but also by disassembled parts. The disassembled parts are the ones recovered after the disassembly of end-of-use/life products. The objective of the considered problem is to maximize the total profit, i.e., the revenue of selling the service parts minus the total cost of the fixed setup, production, disassembly, inventory holding, and disposal over a planning horizon. This paper proves that the single-period version of the considered problem is NP-hard and suggests a heuristic by combining a simulated annealing algorithm and a linear-programming relaxation. Computational experiment results show that the heuristic generates near-optimal solutions within reasonable computation time, which implies that the heuristic is a viable optimization tool for the service parts inventory management. In addition, sensitivity analyses indicate that deciding an appropriate price of disassembled parts and an appropriate collection amount of EOLs are very important for sustainable service parts systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.153-162
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• 2013

This paper proposes the most simple method for optimal solution of the transshipment problem. Usually the transshipment problem is solved by direct linear programming or TSM (Transportation Simplex Method). The method using TSM has two steps. First it is to get a initial solution using NCM, LCM, or VAM, second to refine the initial solution using MOD or SSM. However the steps is complex and difficult. The proposed method applies the method that transforms transshipment problem to transportation problem. In the proposed method it simply selects the minimum cost of rows about transportation problem, and then it applies the method that assigns a transported volume as an ascending sort of the costs of rows about the selected costs. Our method makes to be very fast got the initial value. Also we uses the method that controls assignment volume, if a heavy item of cost is assigned to a transported volume and it has a condition to be able to transform to more lower cost. The proposed algorithm simply got the optimal solution with applying to 11 transshipment problem.