• 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 Port Economic Association
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• v.33 no.2
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• pp.83-96
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• 2017

As a way to address global warming, among the renewable energy sources, there have been heavy investments worldwide for the development of offshore wind farms. However, such development has a drawback: investment costs are higher than those for onshore wind farms due to required operations such as offshore transportation and installation. In particular, delays in installation due to adverse maritime weather conditions are factors that affect the economics of offshore wind farms' operation. Therefore, in this study, we analyze the optimal schedule of the construction of an offshore wind farm from a macro perspective by considering the weather conditions in Korea. For this purpose, we develop a mathematical model and apply it to a 2.5 GW offshore wind farm project on the southwestern coast of the country. We use data from the Korea Meteorological Agency for maritime weather conditions and attempt to reflect the actual input data based on precedent cases overseas. The results show that it takes 6 months to install 35 offshore wind turbines. More specifically, it is pointed out that it is possible to minimize costs by not working in winter.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.433-440
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• 2014

In this study, a method for the low-carbon active distribution system (ADS) planning is proposed. It takes into account the impacts of both network capacity and demand correlation to the renewable energy accommodation, and incorporates demand response (DR) as an available resource in the ADS planning. The problem is formulated as a mixed integer nonlinear programming model, whereby the optimal allocation of renewable energy sources and the design of DR contract (i.e. payment incentives and default penalties) are determined simultaneously, in order to achieve the minimization of total cost and $CO_2$ emissions subjected to the system constraints. The uncertainties that involved are also considered by using the scenario synthesis method with the improved Taguchi's orthogonal array testing for reducing information redundancy. A novel cuckoo search (CS) is applied for the planning optimization. The case study results confirm the effectiveness and superiority of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

• Information Systems Review
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• v.25 no.4
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• pp.27-45
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• 2023

The importance of information security has grown alongside the development of information and communication technology. However, companies struggle to select suitable countermeasures within their limited budgets. Sönmez and Kılıç (2021) proposed a model using AHP and mixed integer programming to determine the optimal investment combination for mitigating information security breaches. However, their model had limitations: 1) a lack of objective measurement for countermeasure efficacy against security threats, 2) unrealistic scenarios where risk reduction surpassed pre-investment levels, and 3) cost duplication when using a single countermeasure for multiple threats. This paper enhances the model by objectively quantifying countermeasure efficacy using the beta probability distribution. It also resolves unrealistic scenarios and the issue of duplicating investments for a single countermeasure. An empirical analysis was conducted on domestic SMEs to determine investment budgets and risk levels. The improved model outperformed Sönmez and Kılıç's (2021) optimization model. By employing the proposed effectiveness measurement approach, difficulty to evaluate countermeasures can be quantified. Utilizing the improved optimization model allows for deriving an optimal investment portfolio for each countermeasure within a fixed budget, considering information security costs, quantities, and effectiveness. This aids in securing the information security budget and effectively addressing information security threats.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 1995.12a
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• pp.9-21
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• 1995

A planning support system enhances our ability to use water capacity expansion as an urban growth management strategy. This paper reports the development of capacity expansion modeling of water supply as part of the continuing development of such a planning support system (PEGASUS: Planning Environment for Generation and Analysis of Spatial Urban Systems) to incorporate water supply, This system is designed from the understanding that land use and development drive the demand for infrastructure and infrastructure can have a significant influence on the ways in which land is developed and used. Capacity expansion Problems of water supply can be solved in two ways: 1) optimal control theory, and 2) mixed integer nonlinear programming (MINLP). Each method has its strengths and weaknesses. In this study the MINLP approach is used because of its strength of determining expansion sizing and timing simultaneously. A dynamic network optimization model and a water-distribution network analysis model can address the dynamic interdependence between water planning and land use planning. While the water-distribution network analysis model evaluates the performance of generated networks over time, the dynamic optimization model chooses alternatives to meet expanding water needs. In addition, the user and capacity expansion modeling-to-generate-alternatives (MGA) can generate alternatives. A cost benefit analysis module using a normalization technique helps in choosing the most economical among those alternatives. GIS provide a tool for estimating the volume of demanded water and showing results of the capacity expansion model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.15-30
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• 2000

The important considerations in the design of fiber-optic networks are reliability and survivability preparing against a failure. The SDH(Synchronous Digital Hierarchy), the international standard of optical transmission, offers several network reconfiguration methods that enable network to be automatically restored from failure. One of the methods is the SHR(Self Healing Ring), which is a ring topology system. Most network providers have constructed their backbone networks with SHR architecture since it can provide survivability economically. The network architecture has eventually evolved into a multi-ring network comprised of interconnected rings. This paper addresses multi-ring network design problems is to minimize ring-construction cost. This problem can be formulated with MIP(mixed integer programming) model. However, it is difficult to solve the model within reasonable computing time on a large scale network because the model is NP-complete. Furthermore, in practice we should consider the problem of routing demands on rings to minimize total cost. This routing problem involves multiplex bundling at the intermediate nodes. A family of heuristic algorithms is presented for this problem. These algorithms include gateway selection and routing of inter-ring demands as well as load balancing on single rings. The developed heuristic algorithms are applied to some network provider's regional and long-distance transmission networks. We show an example of ring design and compare it with another ring topology design. Finally, we analysis the effect bundling.

• Korean Chemical Engineering Research
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• v.53 no.2
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• pp.156-163
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• 2015

This paper presents a new framework for design and economic evaluation of wind energy-based electricity supply system. We propose a network optimization (mixed-integer linear programming) model to design the underlying energy supply system. In this model we include practical constraints such as land limitations of onshore wind farms and different costs of offshore wind farms to minimize the total annual cost. Based upon the model, we also analyze the sensitivity of the total annual cost on the change of key parameters such as available land for offshore wind farms, required area of a wind turbine and the unit price of wind turbines. We illustrate the applicability of the suggested model by applying to the problem of design of a wind turbines-based electricity supply problem in Jeju. As a result of this study, we identified the major cost-drivers and the regional cost distribution of the proposed system. We also comparatively analyzed the economic performance of on/off shore wind farms in wind energy-based electricity supply system of Jeju.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.

• The KIPS Transactions:PartD
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• v.18D no.5
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• pp.371-384
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• 2011

This paper considers logistics decision support system which deals with transportation mode selection considering transportation and carbon emission cost. Transportation and carbon emission costs vary with the choice of transportation modes and to become competitive companies need to find proper transportation modes for their logistics services. However, due to the restricted capacity of transportation modes, it is difficult to balance transportation and carbon emission costs when designing logistics network including transportation mode choice for each service. Therefore this paper aims to analyze the trade-off relationship between transportation and carbon emission cost in mode selection of intermodal transportation and to provide optimal green logistics strategy. In this paper, the logistics decision support system is designed based on mixed integer programming model. To understand the trade-off relationship of transportation and carbon emission cost, the system is tested with various scenarios including transportation of containers between Seoul and Busan. The analysis results show that, even though sea transportation combined with trucking is competitive in carbon emission per unit distance travelled, the total cost of carbon emission and transportation for the sea transportation may not have competitive advantage over other transportation modes including rail and truck transportation modes. The sea-based intermodal logistics service may induce detours which have negative impacts on the overall carbon emission. The proposed logistics decision support system is expected to play key role in green logistics and supply chain management.