• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3A
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• pp.248-255
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• 2008

In an OFDMA system the performance of multi-user scheduling in the frequency domain is affected by the frequency selectivity of the channel. If the channel is too flat in the frequency domain, the multi-user scheduling gain might be degraded. On the contrary, if the frequency selectivity is too high and the magnitude of the frequency response severely fluctuates on the allocation bandwidth, it is also hard to get sufficient scheduling gain. For maximizing the multi-user scheduling gain, a cyclic delay diversity technique can be used to adjust the frequency selectivity of the channel. This paper proposes a method to determine the optimal delay value of cyclic delay diversity according to the allocation bandwidth and the channel characteristics.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.220-223
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• 2011

For successfully commercialized grid systems, it is required to provide an efficient scheduling scheme which is able to optimize benefits for three participants such as consumers, brokers, and providers so that every participant has sufficient benefit to maintain a sustainable market. In this paper, we define this job scheduling problem as an objective optimization problem for three participants. The three objectives are to maximize the success rate of job execution, total achieved profit, and the system utilization. To address the scheduling problem, we propose heuristics referred to as SLA-aware scheduling scheme (SA) for optimal resource allocation. The simulation results show that the improvement and the effectiveness of the proposed scheme and the proposed scheme can outperform well-known scheduling schemes such as first come first serve (FCFS), shortest job first (SJF), and earliest deadline first (EDF).

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.3
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• pp.360-368
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• 1999

We consider the problem of scheduling n jobs with sequence-dependent processing times on a set of parallel-identical machines. The processing time of each job consists of a pure processing time and a sequence-dependent setup time. The objective is to maximize the total remaining machine available time which can be used for other tasks. For the problem, a hybrid genetic algorithm is proposed. The algorithm combines a genetic algorithm for global search and a heuristic for local optimization to improve the speed of evolution convergence. The genetic operators are developed such that parallel machines can be handled in an efficient and effective way. For local optimization, the adjacent pairwise interchange method is used. The proposed hybrid genetic algorithm is compared with two heuristics, the nearest setup time method and the maximum penalty method. Computational results for a series of randomly generated problems demonstrate that the proposed algorithm outperforms the two heuristics.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.2
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• pp.121-134
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• 1999

In this paper, I propose a hybrid genetic algorithm(HGAM) incorporating a greedy interchange local optimization procedure for the multiobjective vehicle scheduling problems with service due times where three conflicting objectives of the minimization of total vehicle travel time, total weighted tardiness, and fleet size are explicitly treated. The vehicle is allowed to visit a node exceeding its due time with a penalty, but within the latest allowable time. The HGAM applies a mixed farming and migration strategy in the evolution process. The strategy splits the population into sub-populations, all of them evolving independently, and applys a local optimization procedure periodically to some best entities in sub-populations which are then substituted by the newly improved solutions. A solution of the HCAM is represented by a diploid structure. The HGAM uses a molified PMX operator for crossover and new types of mutation operator. The performance of the HGAM is extensively evaluated using the Solomons test problems. The results show that the HGAM attains better solutions than the BC-saving algorithm, but with a much longer computation time.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.297-299
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• 2012

In this paper, we introduce a joint flow control and scheduling algorithm for multi-hop cognitive radio networks with spectrum underlay. Our proposed algorithm maximizes the total utility of secondary users while stabilizing the cognitive radio network and still satisfies the total interference from secondary users to primary network is less than an accepted level. Based on Lyapunov optimization technique, we show that our scheme is arbitrarily close to the optimal.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1071-1076
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• 2003

Evolutionary algorithm is recognized as a promising approach to solving multi-objective combinatorial optimization problems. When no preference information of decision makers is given, multi-objective optimization problems have been commonly used to search for diverse and good Pareto optimal solution. In this paper we propose a new multi-objective evolutionary algorithm based on competitive coevolutionary algorithm, and demonstrate the applicability of the algorithm. The proposed algorithm is designed to promote both population diversity and rapidity of convergence. To achieve this, the strategies of fitness evaluation and the operation of the Pareto set are developed. The algorithm is applied to job shop scheduling problems (JSPs). The JSPs have two objectives: minimizing makespan and minimizing earliness or tardiness. The proposed algorithm is compared with existing evolutionary algorithms in terms of solution quality and diversity. The experimental results reveal the effectiveness of our approach.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.41-46
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• 2012

In this paper, An efficient circuit design algorithm considering constraint is proposed. The proposed algorithm sets up in time constraint and area constraint, power consumption constraint for a circuit implementation. First, scheduling process for time constraint. Select the FU(Function Unit) which is satisfied with time constraint among the high level synthesis results. Analyze area and power consumption of selected FUs. Constraint set for area and power constraint. Device selection to see to setting condition. Optimization circuit implementation in selected device. The proposed algorithm compared with [7] and [8] algorithm. Therefore the proposed algorithm is proved an efficient algorithm for optimization circuit implementation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1679-1691
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• 2016

Successive interference cancellation (SIC) is an effective means of multi-packet reception to combat interference at the physical layer. We investigate the joint optimization issue of channel access and power control for capacity maximization in SIC-enabled wireless networks. We propose a new interference model to characterize the sequential detection nature of SIC. Afterward, we formulize the joint optimization problem, prove it to be a nondeterministic polynomial-time-hard problem, and propose a novel approximation approach based on the genetic algorithm (GA). Finally, we discuss the design and parameter setting of the GA approach and validate its performance through extensive simulations.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.314-317
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• 2010

본 연구는 차량 경로 스케줄링 문제(VRSPTW, the Vehicle Routing and Scheduling Problem with Time Window)를 해결하기 위하여, 멀티 비용 함수(Multi Cost Function)를 갖는 개미 군집 최적화(Ant Colony Optimization)을 이용한 휴리스틱을 제안하였다. 멀티 비용 함수는 각 개미가 다음 고객 노드로 이동하기 위해 비용을 평가할 때 거리, 요구량, 각도, 시간제약에 대해 서로 다른 가중치를 반영하여 우수한 초기 경로를 구할 수 있도록 한다. 본 연구의 실험결과에서 제안된 휴리스틱이 Solomon I1 휴리스틱과 기회시간이 반영된 하이브리드 휴리스틱보다 효율적으로 최근사 해를 얻을 수 있음을 보였다.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.141-157
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• 2013

In this paper, we consider the minimization of the total weighted earliness-tardiness penalty of jobs, regarding the partial precedences between jobs. We present an optimal scheduling algorithm in O(n(n+m log m)) where n is the number of jobs and m is the number of partial precedences. In the algorithm, the optimal schedule is constructed iteratively by considering each group of contiguous jobs as a block that is represented by a tree.