• KIEE International Transactions on Power Engineering
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• 제5A권4호
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• pp.331-338
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• 2005

In this paper, an improved maintenance scheduling approach suitable for the competitive environment is proposed by taking account of profits and costs of generation companies and the formulated combinatorial optimization problem is solved by using Reactive Tabu search (RTS). In competitive power markets, electricity prices are determined by the balance between demand and supply through electric power exchanges or by bilateral contracts. Therefore, in decision makings, it is essential for system operation planners and market participants to take the volatility of electricity price into consideration. In the proposed maintenance scheduling approach, firstly, electricity prices over the targeted period are forecasted based on Artificial Neural Network (ANN) and also a newly proposed aggregated bidding curve. Secondary, the maintenance scheduling is formulated as a combinatorial optimization problem with a novel objective function by which the most profitable maintenance schedule would be attained. As an objective function, Opportunity Loss by Maintenance (OLM) is adopted to maximize the profit of generation companies (GENCOS). Thirdly, the combinatorial optimization maintenance scheduling problem is solved by using Reactive Tabu Search in the light of the objective functions and forecasted electricity prices. Finally, the proposed maintenance scheduling is applied to a practical test power system to verify the advantages and practicability of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.5013-5034
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• 2019

In the Peer-to-Peer (P2P) streaming systems, peers randomly form a network overlay to share video resources with a data scheduling scheme. A data scheduling scheme can have a great impact on system performance, which should achieve two optimal objectives at the same time ideally. The two optimization objectives are to improve the perceived video quality and maximize the network throughput, respectively. Maximizing network throughput means improving the utilization of peer's upload bandwidth. However, maximizing network throughput will result in a reduction in the perceived video quality, and vice versa. Therefore, to achieve the above two objects simultaneously, we proposed a new data scheduling scheme based on multi-objective particle swarm optimization data scheduling scheme, called MOPSO-DS scheme. To design the MOPSO-DS scheme, we first formulated the data scheduling optimization problem as a multi-objective optimization problem. Then, a multi-objective particle swarm optimization algorithm is proposed by encoding the neighbors of peers as the position vector of the particles. Through extensive simulations, we demonstrated the MOPSO-DS scheme could improve the system performance effectively.

• ETRI Journal
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• 제27권6호
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• pp.777-787
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• 2005

In this paper, we propose an urgency- and efficiency-based wireless packet scheduling (UEPS) algorithm that is able to schedule real-time (RT) and non-real-time (NRT) traffics at the same time while supporting multiple users simultaneously at any given scheduling time instant. The UEPS algorithm is designed to support wireless downlink packet scheduling in an orthogonal frequency division multiple access (OFDMA) system, which is a strong candidate as a wireless access method for the next generation of wireless communications. The UEPS algorithm uses the time-utility function as a scheduling urgency factor and the relative status of the current channel to the average channel status as an efficiency indicator of radio resource usage. The design goal of the UEPS algorithm is to maximize throughput of NRT traffics while satisfying quality-of-service (QoS) requirements of RT traffics. The simulation study shows that the UEPS algorithm is able to give better throughput performance than existing wireless packet scheduling algorithms such as proportional fair (PF) and modified-largest weighted delay first (M-LWDF), while satisfying the QoS requirements of RT traffics such as average delay and packet loss rate under various traffic loads.

• 한국경영과학회지
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• 제13권1호
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• pp.57-57
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• 1988

This paper develops an adaptive scheduling policy for flexible manufacturing systems. The inductive learning methodology used for constructing this state-dependent scheduling policy provides and understanding of the relative importance of the various system parameters in determining the appropriate scheduling rule. Experimental studies indicated the superiority of the suggested approach over the alternative approach involving the repeated application of a single scheduling rule for randomly generated test problems as well as a real system, and under both stationary and nonstationary conditions. In particular, its relative performance improves further when there are frequent disruptions, and when disruptions are caused by the introduction of tiiight due date jobs, one of the most common surces of disruptions in most manufacturing systems.

• 경영과학
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• 제13권1호
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• pp.57-70
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• 1996

This paper develops an adaptive scheduling policy for flexible manufacturing systems. The inductive learning methodology used for constructing this state-dependent scheduling policy provides and understanding of the relative importance of the various system parameters in determining the appropriate scheduling rule. Experimental studies indicated the superiority of the suggested approach over the alternative approach involving the repeated application of a single scheduling rule for randomly generated test problems as well as a real system, and under both stationary and nonstationary conditions. In particular, its relative performance improves further when there are frequent disruptions, and when disruptions are caused by the introduction of tiiight due date jobs, one of the most common surces of disruptions in most manufacturing systems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.334-336
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• 2000

In Korea, the CCGTs have been installed to about 25% of the total generating capacity. Generally CCGTs determine the System Marginal Prices(SMP) in Cost Based Generation Pool. So the scheduling of CCGTs is very important in daily generation scheduling. This paper describes the scheduling of CCGTs which considers the operating characteristics of them. We use lagrangian relaxation method which decomposes the unit committment problem into the subproblems of the individual unit. In the CCGT subproblem, we define the cost function of CCGT in two way. In Case study, the daily generation scheduling is performed using the data of Korean thermal system.

• 제어로봇시스템학회논문지
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• 제16권6호
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• pp.539-545
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• 2010

Through the AUTOSAR methodology, the embedded software shall become more flexible, reusable, maintainable than ever. However, it is not mentioned about specific timing constraints of software components in AUTOSAR. There are a few basic principles for mapping runnable entities. At this point, AUTOSAR software design with optimal scheduling method is one of the enabling technologies in vehicle embedded system. This paper presents an approach based on mapping runnable entities and task scheduling design method for EPS (Electric Power Steering) software components, based on AUTOSAR. In addition, the experimental results of concurrent simulation show that the proposed scheduling technique and timing synchronization in the software component design can achieve the improved torque ripple performance and it well suited for EPS application software.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2012년도 춘계 학술논문 발표대회
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• pp.303-305
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• 2012

This paper presents a system called a Cash-flow based Construction Scheduling Optimization (CfSO). The existing CPM is biased on schedule and cost management. For a profitable and successful project management, the cash-flow which occurred actually by contractual conditions should be considered in the project scheduling. Therefore, this study provides a method to estimate the amount of a cash-flow occurred periodically by integrating the terms of contract into scheduling. The proposed methodology is implemented as a system prototype in Microsoft Excel. CfSO helps a site manager as a decision-maker to establish a optimized project scheduling and decide profitable contractual conditions against a construction owner.

• 한국철도학회논문집
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• 제9권1호
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• pp.50-56
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• 2006

This paper presents a mathematical model of the long-term track tamping scheduling problem in the Korean highspeed railway system. The presented model encompasses various operational field constraints, moreover improves a state-of-the-art model in extending the feasible space. We show the model is sized up to intractable scale, then propose another approximation model that is possible to handle with the present computer system and commercial optimization package, directly. The aggregated index, lot, is selected, considering the resolution of the planning horizon as well as scheduling purpose. Lastly, this paper presents two test results for the approximation model. The results expose the approximation model to quite promising in deploying it into an operational software program for the long-term track tamping scheduling problem.

• 대한산업공학회지
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• 제17권2호
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• pp.63-74
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• 1991

An expert scheduling system on real time basis for computer-aided manufacturing systems has been developed. In developing expert scheduling system, the most time-consuming job is to obtain rules from expert schedulers. An efficient process of obtaining rules directly form the schedules produced by expert schedulers is proposed. By the process, a set of complete and minimal set of rules is obtained. During a real time scheduling, when given information on possible values of elements, the rules produce possible values of decision elements, where logical explanations of the result may be offered in terms of chaining rules. The learning and scheduling processes have been simulated with an automated manufacturing line engaged in the production of circuit boards.