• Proceedings of the Korea Concrete Institute Conference
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• 2000.10a
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• pp.359-362
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• 2000

From a practical perspective, sustainable development requires the optimization of current natural resources and the minimization of derived wastes. A major concern with respect to sustainable infrastructure development is the continued depletion of easily-available natural resources and environmental matters are more serious, the concerned about waste materials which are inevitably produced in the manufacturing of the product is getting worse. These wastes must be handled and properly disposed, and many times, although this waste may be environmentally inert, it has been discarded in landfills. But current disposal methods of these by-products create not only a loss of profit for the power industry, but also environmental concerns the breed negative public opinion. therefore, this study evaluates the ECO artificial aggregate and bricks were designed and tested for the end use of fly ash.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.14-20
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• 2003

Supply chain is the link that moves products between suppliers, manufactures, wholesalers, distribution, retailers and ended consumers. Supply chain management(SCM) is a way to supervise the flow of products, materials and information as they move along the supply chain. In the recent years, Most of the companies are in a hurry the introduction of SCM to obtain international competitiveness. The goal of SCM is to optimize the supply chain, which can not only reduce inventories, but may also create a higher profit margin for finished goods by giving customers exactly what they want. There are four major decision areas (location, production, inventory, transportation) in supply chain management, and there are both strategic and operational elements in each of these decision areas. This paper is concerned with the integrated production planning problem including not only the production cost but also the transportation cost in supply chains, and an efficient algorithm using genetic algorithm and quickest path method is presented to solve the problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3413-3430
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• 2017

With the fast increasing popularity of mobile services, ubiquitous mobile devices with enhanced sensing capabilities collect and share local information towards a common goal. The recent Mobile Crowd Sensing (MCS) paradigm enables a broad range of mobile applications and undoubtedly revolutionizes many sectors of our life. A critical challenge for the MCS paradigm is to induce mobile devices to be workers providing sensing services. In this study, we examine the problem of sensing task assignment to maximize the overall performance in MCS system while ensuring reciprocal advantages among mobile devices. Based on the overlapping coalition game model, we propose a novel workload determination scheme for each individual device. The proposed scheme can effectively decompose the complex optimization problem and obtains an effective solution using the interactive learning process. Finally, we have conducted extensive simulations, and the results demonstrate that the proposed scheme achieves a fair tradeoff solution between the MCS performance and the profit of individual devices.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.411-413
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• 2001

This paper discusses on an optimal bidding strategy of a generator in a competitive electricity spot market using the information of predicted spot price with some assumptions. Optimal bidding strategy of a generator is derived by solving a profit-maximizing optimization problem with a constraint where the forecasted spot price is treated as a constant value. The main advantage of this methodology is that the optimal bidding strategy of each generator can be obtained independently where the gaming characteristics of generators are merged into the forecasted spot price.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.3
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• pp.17-31
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• 2007

In this paper, we consider a service system with bi-functional servers, which can switch between the primary service room and the secondary room. A service policy is characterized by the switching paints which depend on the queue length in the primary service room and the service level requirement constraint of the secondary room. The primary service room is modeled as a Markovian queueing system and the throughput of the primary service room is function of the total number of bi-functional servers. the buffer capacity of the primary service room, and the service policy. There is a revenue obtained from throughput and costs due to servers and buffers. We study the problem of simuitaneously determining the optimal number of servers, buffer capacity, and service policy to maximize profit of the service system, and develop an algorithm which can be successfully applied with the small number of computations.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_1
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• pp.985-991
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• 2023

This study proposes a solution to the voltage drop in electric vehicle chargers, due to the parasitic resistance and inductance of power cables when the chargers are separated by large distances. A method using multi-level electric vehicle chargers that can output power in stages, without installing an additional energy supply source such as a reactive power compensator or an energy storage system, is proposed. The voltage drop over the power cables, to optimize the charging scheduling, is derived. The obtained voltage drop equation is used to formulate the constraints of the optimization process. To validate the effectiveness of the obtained results, an optimal charging scheduling is performed for each period in a case study based on the assumed charging demands of three connected chargers. From the calculations, the proposed method was found to generate an annual profit of $20,800 for a $12,500 increase in installation costs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.197-199
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• 2016

In this paper, we investigate a market equilibrium in multi-channel sharing cognitive radio networks (CRNs): it is assumed that every subchannel is orthogonally licensed to a single primary user (PU), and can be shared with multiple secondary users (SUs). We model this sharing as a spectrum market where PUs offer SUs their subchannels with limiting the interference from SUs; the SUs purchase the right to transmit over the subchannels while observing the interference limits set by the PUs and their budget constraints. The utility function of SU is defined as least achievable transmission rate, and that of PU is given by the net profit. We define a market equilibrium in the context of extended Fisher model, and show that the equilibrium is yielded by solving an optimization problem, Eisenberg-Gale convex program.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.157-164
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• 2023

This paper suggests O(n²) polynomial time heuristic algorithm for corporate tax structure optimization problem that has been classified as NP-complete problem. The proposed algorithm constructs tax tree levels that the target holding company is located at root node of Level 1, and the tax code categories(T_e) 1,4,3,2 are located in each level 2,3,4,5 sequentially. To find the maximum tax-relief path from source(S) to target(T), firstly we connect the minimum witholding tax rate _minr_w(u, v) arc of node u point of view for transfer the profit from u to v node. As a result we construct the spanning tree from all of the source nodes to a target node, and find the initial feasible solution. Nextly, we find the alternate path with minimum foreign tax rate _minr_fi(u, v) of v point of view. Finally we choose the minimum tax-relief path from of this two paths. The proposed heuristic algorithm performs better optimal results than linear programming and Tabu search method that is a kind of metaheuristic method.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.785-794
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• 2002

Automatic stock trading systems should be able to solve various kinds of optimization problems such as market trend prediction, stock selection, and trading strategies, in a unified framework. But most of the previous trading systems based on supervised learning have a limit in the ultimate performance, because they are not mainly concerned in the integration of those subproblems. This paper proposes a stock trading system, called R-Trader, based on reinforcement teaming, regarding the process of stock price changes as Markov decision process (MDP). Reinforcement learning is suitable for Joint optimization of predictions and trading strategies. R-Trader adopts two popular reinforcement learning algorithms, temporal-difference (TD) and Q, for selecting stocks and optimizing other trading parameters respectively. Technical analysis is also adopted to devise the input features of the system and value functions are approximated by feedforward neural networks. Experimental results on the Korea stock market show that the proposed system outperforms the market average and also a simple trading system trained by supervised learning both in profit and risk management.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.189-196
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• 2012

This paper proposes Swap algorithm for solving Dynamic Economic Dispatch (DED) problem. The proposed algorithm initially balances total load demand $P_d$ with total generation ${\Sigma}P_i$ by deactivating a generator with the highest unit generation cost $C_i^{max}/P_i^{max}$. It then swaps generation level $P_i=P_i{\pm}{\Delta}$, (${\Delta}$=1.0, 0.1, 0.01, 0.001) for $P_i=P_i-{\Delta}$, $P_j=P_j+{\Delta}$ provided that $_{max}[F(P_i)-F(P_i-{\Delta})]$ > $_{min}[F(P_j+{\Delta})-F(P_j)]$, $i{\neq}j$. This new algorithm is applied and tested to the experimental data of Dynamic Economic Dispatch problem, demonstrating a considerable reduction in the prevalent heuristic algorithm's optimal generation cost and in the maximization of economic profit.