• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.2863-2874
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• 1999

Apartment auction is a system that is used for the citizens to get a house. This paper deals with the implementation of a web-based intelligent decision support system using OLAP technique and data mining technique for auction decision support. The implemented decision support system is working on a real auction database and is mainly composed of OLAP Knowledge Extractor based on data warehouse and Auction Data Miner based on data mining methodology. OLAP Knowledge Extractor extracts required knowledge and visualizes it from auction database. The OLAP technique uses fact, dimension, and hierarchies to provide the result of data analysis by menas of roll-up, drill-down, slicing, dicing, and pivoting. Auction Data Miner predicts a successful bid price by means of applying classification to auction database. The Miner is based on the lazy model-based classification algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm to reflect the characteristics of auction database.

• Advances in robotics research
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• v.2 no.3
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• pp.247-257
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• 2018

Various studies have been performed to coordinate robots in transporting objects and different artificial intelligence algorithms have been considered in this field. In this paper, we investigate and solve Multi-Robot Transportation problem by using a combined auction algorithm. In this algorithm each robot, as an agent, can perform the auction and allocate tasks. This agent tries to clear the auction by studying different states to increase payoff function. The algorithm presented in this paper has been applied to a multi-robot system where robots are responsible for transporting objects. Using this algorithm, robots are able to improve their actions and decisions. To show the excellence of the proposed algorithm, its performance is compared with three heuristic algorithms by statistical simulation approach.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.03a
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• pp.11-16
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• 1998

The classical algorithm for solving liner network flow problems are primal cost improvement method, including simplex method, which iteratively improve the primal cost by moving flow around simple cycles, which iteratively improve the dual cost by changing the prices of a subset of nodes by equal amounts. Typical iteration/shortest path algorithm is used to improve flow problem of liner network structure. In this paper we stdudied about the implemental method of shortest path which is a practical computational aspects. This method can minimize the best neighbor node and also implement the typical iteration which is $\varepsilon$-CS satisfaction using the auction algorithm of linear network flow problem

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.6
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• pp.11-18
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• 2009

In this paper, a hierarchical link allocation algorithm between mobile stations (MSs) and the corresponding base station (BS) by an optimal utilization of relay stations (RSs) is proposed to improve throughput of RS systems. In the proposed hierarchical algorithm, each RS operates cognitive radio functions to sense the degree of satisfaction in the quality of services (QoSs) and then selects the candidate set of MSs to have links with the RS. Such information is reported to the BS, where an auction process is performed to get an optimal allocation of communication links between the MSs and the BS. To maximize system throughput, the proposed auction algorithm is conducted upon bidding prices of communication links, considering both the co-channel interference (CCI) information shared among adjacent cells and the QoS enhancement information for each MS collected from RSs. The BS then switches the communication links of the auction winner MSs through the corresponding RS. The computer simulation shows that the proposed algorithm enhances the user QoS more than the conventional algorithm, especially for RS systems with more users requiring higher QoS. The proposed algorithm has also been proved to have more robust performance than the conventional one when the traffic load is higher and the CCI becomes stronger.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.592-601
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• 2007

In the conventional double auction approaches, two basic assumptions are usually applied - (1) each trader has a linear or quasi-linear utility function of price and quantity, (2) buyers as well as sellers have identical utility functions. However, in practice, these assumptions are unrealisitc. Therefore, a flexible and integrated double auction mechanism that can integrate all traders' diverse utility functions is necessary. We propose a double auction mechanism with resource allocation based on nonlinear utility functions, namely a flexible synchronous double auction system where each participant can express a diverse utility function on the price and quantity. In order to optimize the total market utility consists of multiple complex utility functions of traders, our study proposes a genetic algorithm (GA) We show the viability of the proposed mechanism through several simulation experiments.

• Journal of Electrical Engineering and Technology
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• v.1 no.1
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• pp.1-7
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• 2006

In an electricity market, the spot market is normally integrated with a forward or future market. The advantage of the forward market is to allow the market participants to deal in a part or the whole trading portfolio at a fix price in advance and to avoid risk associated to the uncertain price of the spot market. Japan has introduced a continuous auction base forward market from April 2005. This paper analyzes the Japanese forward market rules and operations, and introduces a new algorithm that may improve the efficiency of the market itself. The proposed algorithm enables us to give consideration to the specific characteristics of the power system and to integrate them in the auction mechanism. The benefits of the proposed algorithm are verified on an electronic simulation platform and the results described in this paper.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.197-202
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• 2014

In order to operate multiple UAVs for multiple tasks efficiently, we need a task allocation algorithm with minimum cost, i.e.,total moving distance required to accomplish the whole mission. In this paper, we have proposed the MCBAA (Modified Consensus Based Auction Algorithm) which can be suitably applied to the operation of multiple UAVs. The key idea of proposed algorithm is to minimize sum of distance from current location of agents to location of tasks based on the conventional CBAA. Several simulation test performed on three UAV agents with multiple tasks demonstrates the overall efficiency both in time and total distance.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.1
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• pp.19-33
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• 2008

In the previous double auction research for the market optimization, two basic assumptions are usually applied - (1) each trader has a linear or quasi-linear utility function of price and quantity, and (2) buyers as well as sellers have identical utility functions. However, in practice, each buyer and seller in a double auction market may have diverse utility functions for trading goods. Therefore, a flexible and integrated double auction mechanism that can integrate all traders' diverse utility functions is necessary. In particular, the flexible mechanism is more useful in a synchronous double auction because traders can properly change utilities in each round. Therefore, in this paper, we propose a flexible synchronous double auction mechanism in which traders can express diverse utility functions for the price and quantity of the goods, and optimal total market utility is guaranteed. In order to optimize the total market utility which consists of multiple complex utility functions of traders. We show the viability of the proposed mechanism through a several simulation experiments.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.337-340
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• 1999

At each iteration, the path is either extended by adding a new node, or contracted by deleting its terminal node. When the destination becomes the terminal node of the path, the algorithm terminate. In the process of finding the shortest path to given destination, the algorithm visits other node, there by obtaining a shortest path from the origin to them. We show here that when the auction algorithm is applied to this equivalent program with some special rules for choosing the initial object prices and the person submitting a bid at each iteration, one obtains the generic form of the $\varepsilon$-relaxation method. Thus, the two methods are mathematically equivalent

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.11
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• pp.944-954
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• 2013

Cognitive radio technology gives secondary users chances that they can use specific spectrum of the primary user when the primary user doesn't use it. This paper proposes the algorithm that maximizes the benefit of the primary user considering spectrum price and auction period by using the auction game theory. According to the ratio of spectrum that secondary users bid, primary user allocates spectrum to secondary users. In the process of repeated auction, the primary user gets to find the optimal price of spectrum. Using the price and the proposed utility function of primary user, we derive the optimal auction period. At the same time, the primary user finally determines the price of spectrum appropriate for the optimal period.