• Korean Management Science Review
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• v.13 no.1
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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.

• Journal of the Korean Society for Railway
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• v.9 no.1 s.32
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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.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1709-1718
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• 2017

This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS) approach for generation scheduling of a power grid. SDMS considers flexible resource options including conventional generation flexibility in a chance-constrained day-ahead scheduling optimization (DASO). The prime objective of the DASO is the minimization of the daily production cost in power systems with high penetration scenarios of variable generation. Furthermore, energy storage is scheduled in an hourly-ahead deterministic real-time scheduling optimization (RTSO). DASO simulation results are used as the base starting-point values in the hour-ahead online rolling RTSO with a 15-minute time interval. RTSO considers energy storage as another source of grid flexibility, to balance out the deviation between predicted and actual net load demand values. Numerical simulations, on the IEEE RTS test system with high wind penetration levels, indicate the effectiveness of the proposed SDMS framework for managing the grid flexibility to meet the net load demand, in both day-ahead and real-time timescales. Results also highlight the adequacy of the framework to adjust the scheduling, in real-time, to cope with large prediction errors of wind forecasting.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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• pp.1382-1389
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• 2007

This paper presents a new approach for solving the problem of maintenance scheduling of generating units using a binary particle swarm optimization (BPSO). In this paper, we find the optimal solution of the maintenance scheduling of generating units within a specific time horizon using a binary particle swarm optimization algorithm, which is the discrete version of a conventional particle swarm optimization. It is shown that the BPSO method proposed in this paper is effective in obtaining feasible solutions in the maintenance scheduling of generating unit. IEEE reliability test systems(1996) including 32-generators are selected as a sample system for the application of the proposed algorithm. From the result, we can conclude that the BPSO can find the optimal solution of the maintenance scheduling of the generating unit with the desirable degree of accuracy and computation time, compared to other heuristic search algorithm such as genetic algorithms. It is also envisaged that BPSO can be easily implemented for similar optimizations and scheduling problems in power system problems to obtain better solutions and improve convergence performance.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.7
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• pp.655-660
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• 2007

A canard-leading edge flap deflection scheduling laws have been established to enhance the maneuverability of the canard type fighter class aircraft. These scheduling laws are the relation of canard-leading edge flap deflections and flight conditions to maximize the lift-drag ratio. For these purposes, the corrected supersonic panel method has been used to predict the lift-drag characteristics due to canard-leading edge flap deflections. In addition, the high speed wind tunnel test has been conducted with 1/20 scale model to validate the predicted scheduling laws. Good agreements have been obtained compared with the results of high speed wind tunnel test. Based on the results obtained from the experimental and numerical studies, the corrected supersonic panel method has shown to be useful to establish the canard-leading edge flap deflection scheduling law for the aerodynamic design of canard type fighter class aircraft.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.2
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• pp.111-124
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• 1996

The main purpose of this study is to develop an algorithm to solve the scheduling problems of FMS using Petri-net is well suited to model the dynamics of FMS and Petri-net is an ideal tool to formulate scheduling problems with routing flexibility and shared resources. By using the marking of Petri-net, We can model features of discrete even system, such as concurrency, asynchronous, conflict and non-determinism. The proposed algorithm in this paper can handle back-tracking using the marking of Petri-net. The results of the experiment show that marking is one of the best ways that describe exactly movement of the discrete event system. To show the effectiveness of the algorithm suggested here, we compare it with L1 algorithm using the Petri-net through the test on randomly generated test problems.

• Korean Journal of Computational Design and Engineering
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• v.19 no.4
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• pp.402-409
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• 2014

Presented in this paper is a scheduling method for semiconductor backend process considering the backward pegging. It is known that the pegging for frontend is a process of labeling WIP lots for target order which is specified by due date, quantity, and product specifications including customer information. As a result, it gives the release plan to meet the out target considering current WIP. However, the semiconductor backend process includes the multichip package and test operation for the product bin portion. Therefore, backward pegging method for frontend can't give the release plan for backend process in semiconductor. In this paper, we suggest backward pegging method considering the characteristics of multichip package and test operation in backend process. And we describe the backward pegging problem using the examples.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.562-564
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• 1995

This paper proposes a new algorithm for maintenance scheduling with considering load forecast uncertainty. The proposed algorithm is based on the equivalent load of effective load carrying capability(ELCC) of generators. The uncertainty of forecasted load is considered as a normal distribution probability density function. For maintenance scheduling, reserve levelization method and risk levelization method are used in this study. To test the algorithm, we applied the proposed method to IEEE reliability test system(IEEE RTS). As a result, we verified the validity of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.39-60
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• 2017

In the Cloud, for the scheduling of parallel jobs, there are many tasks in a job and those tasks are executed concurrently on different VMs (Visual machines), where each task of the job will be executed synchronously. The goal of scheduling is to reduce the execution time and to keep the fairness between jobs to prevent some jobs from waiting more time than others. We propose a Cloud model which has multiple Clouds, and under this model, jobs are in different lists according to the waiting time of the jobs and every job has different parallelism. At the same time, a new method-ZOMT (the scheduling parallel tasks based on ZERO-ONE scheduling with multiple targets) is proposed to solve the problem of scheduling parallel jobs in the Cloud. Simulations of ZOMT, AFCFS (Adapted First Come First Served), LJFS (Largest Job First Served) and Fair are executed to test the performance of those methods. Metrics about the waiting time, and response time are used to test the performance of ZOMT. The simulation results have shown that ZOMT not only reduces waiting time and response time, but also provides fairness to jobs.