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Maintenance is important to manufacturing process as it helps improve the efficiency of production. Although different models of joint deterioration and learning effects have been studied extensively in various areas, it has rarely been studied in the context of scheduling with maintenance activities. This paper considers scheduling with jointly the deterioration and learning effects and multi-maintenance activities on a single-machine setting. We assume that the machine may have several maintenance activities to improve its production efficiency during the scheduling horizon, and the duration of each maintenance activity depends on the running time of the machine. The objectives are to determine the optimal maintenance frequencies, the optimal maintenance locations, and the optimal job schedule such that the makespan and the total completion time are minimized, respectively, when the upper bound of the maintenance frequencies on the machine is known in advance. We show that all the problems studied can be solved by polynomial time algorithms.


Chang Y.-P.,Nan Kai University of Technology
Expert Systems with Applications | Year: 2010

This paper presents an approach of combined sequential quadratic programming and particle swarm optimization (SQP-PSO) to optimize the planning of large-scale passive harmonic filters (PHF). The optimal filter scheme can be obtained for a system under abundant harmonic current sources where harmonic amplification problems should be avoided. The objective is to minimize the cost of the filter, filters loss, the total demand distortion of harmonic currents and the total harmonic distortion of voltages at each bus simultaneously. Meanwhile, based on the concept of multi-objective optimization, how to define the fitness function of the PSO to include different performance criteria is also discussed. The designed heuristic SQP-PSO is applied to a practical harmonic problem in a steel plant, where both AC and DC arc furnaces are used and a static var compensator (SVC) is installed. Three design schemes are compared to demonstrate the performance of the SQP-PSO. © 2009 Elsevier Ltd. All rights reserved.


Chang Y.-P.,Nan Kai University of Technology
International Journal of Electrical Power and Energy Systems | Year: 2010

A particle-swarm optimization method with nonlinear time-varying evolution (PSO-NTVE) is employed in determining the tilt angle of photovoltaic (PV) modules in Taiwan. The objective is to maximize the output electrical energy of the modules. In this study, seven Taiwanese cities were selected for analysis. First, the position of the Sun at any time and location was predicted by the mathematical procedure of Julian dating; then, the solar irradiation was obtained at each site under a clear sky. By combining the temperature effect, the PSO-NTVE method is adopted to calculate the optimal tilt angles for fixed south-facing PV modules. In this method, the parameters are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments have an effect that approximates the full factorial experiments. A comparison of the measurement results in electrical energy between the four PSO methods and the PV modules set a six tilt angles. The results indicated that the annual solar radiation has a maximum value of 2658.69 kW h/m2 in Hengchun, and the conversion efficiency of the modules ranges from 13.14% for Taitung to 13.39% for Hengchun. Also, it should be noticed that the annual efficiency for Hengchun is higher than that for Kaohsiung because the average monthly PV-module temperature of Kaohsiung is higher than of Hengchun at this time. The results show that the annual optimal angle for the Taipei area is 18.16°; for Taichung, 17.3°; for Tainan, 16.15°; for Kaosiung, 15.79°; for Hengchung, 15.17°; for Hualian, 17.16°; and for Taitung, 15.94°. © 2010 Elsevier Ltd.


A particle-swarm optimization method with nonlinear time-varying evolution (PSO-NTVE) is employed in determining the tilt angle of photovoltaic (PV) modules in Taiwan. The objective is to maximize the output electrical energy of the modules. In this study, seven Taiwanese cities were selected for analysis. First, the sun's position at any time and location was predicted by the mathematical procedure of Julian dating, and then the solar irradiation was obtained at each site under a clear sky. By combining the temperature effect, the PSO-NTVE method is adopted to calculate the optimal tilt angles for fixed south-facing PV modules. In this method, the parameters are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments have an effect that approximates the full factorial experiments. Statistical error analysis was performed to compare the results between the four PSO methods and experimental results. Hengchun city in which the minimum total error value of 6.12% the reasons for the weather more stability and less building shade. A comparison of the measurement results in electrical energy between the four PSO methods and the PV modules set a six tilt angles. Obviously four types of PSO methods simulation of electrical energy value from 231.12 kWh/m2 for Taipei to 233.81 kWh/m2 for Hengchun greater than the measurement values from 224.71 kWh/m2 for Taichung to 228.47 kWh/m2 for Hengchun by PV module which is due to instability caused by climate change. Finally, the results show that the annual optimal angle for the Taipei area is 18.16°; for Taichung, 17.3°; for Tainan, 16.15°; for Kaosiung, 15.79°; for Hengchung, 15.17°; for Hualian, 17.16°; and for Taitung, 15.94°. It is evident that the authorized Industrial Technology Research Institute (ITRI) recommends that tilt angle of 23.5° was not an appropriate use of Taiwan's seven cities. PV modules with the installation of the tilt angle should be adjusted in different locations. © 2010 Elsevier Ltd. All rights reserved.


Pai M.-C.,Nan Kai University of Technology
Asian Journal of Control | Year: 2012

This paper investigates the synchronization problem for a class of uncertain chaotic systems. Only partial information of the system states is known. An adaptive sliding mode observer-based slave system is designed to synchronize a given chaotic master system with unknown parameters and external disturbances. Based on the Lyapunov stability theorem, the global synchronization between the master and slave systems is ensured. Furthermore, the structure of the slave system is simple and the proposed adaptive sliding mode observer-based synchronization scheme can be implemented without requiring a priori knowledge of upper bounds on the norm of the uncertainties and external disturbances. Simulation results demonstrate the effectiveness and robustness of the proposed scheme. Copyright © 2010 John Wiley and Sons Asia Pte Ltd.


Pai M.C.,Nan Kai University of Technology
Journal of the Franklin Institute | Year: 2010

The problem of the robust tracking and model following for a class of linear systems with time-varying parameter uncertainties, multiple delayed state perturbations and external disturbance is investigated in this paper. The algorithm is based on the adaptive sliding mode control. The proposed method does not need a priori knowledge of upper bounds on the norm of the uncertainties, but estimates them by using the adaptation technique so that the reaching condition can be satisfied. This scheme guarantees the closed-loop system stability and zero-tracking error in the presence of time-varying parameter uncertainties, multiple delayed state perturbations and external disturbance. Finally, simulation results demonstrate the efficacy of the proposed control methodology. © 2010 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.


This paper proposes a discrete-time neuro-sliding mode control (NSMC) scheme to realize the problem of robust tracking and model following for a class of uncertain time-delay systems. It is shown that the proposed scheme guarantees the stability of closed-loop system and achieves zero-tracking error in the presence of state delays, input delays, parameter uncertainties, and external disturbances. The selection of sliding surface and the existence of sliding mode are two important issues, which have been addressed. This scheme not only assures robustness against time-delays, system uncertainties and disturbances, but also avoids chattering phenomenon and reaching phase. Moreover, the knowledge of upper bound of uncertainties is not required. Both the theoretical analysis and illustrative example demonstrate the validity of the proposed scheme. © 2013 Springer Science+Business Media Dordrecht.


Ko C.-N.,Nan Kai University of Technology | Lee C.-M.,Nan Kai University of Technology
Energy | Year: 2013

Accurate load forecasting is an important issue for the reliable and efficient operation of the power system. This paper presents a hybrid algorithm which combines SVR (support vector regression), RBFNN (radial basis function neural network), and DEKF (dual extended Kalamn filter) to construct a prediction model (SVR-DEKF-RBFNN) for short-term load forecasting. In the proposed model, first, the SVR model is employed to determine both the structure and initial parameters of the RBFNN. After initialization, the DEKF is used as the learning algorithm to optimize the parameters of the RBFNN. Finally, the optimal RBFNN model is adopted to predict short-term load. The performance of the proposed approach is evaluated on real-load data from the Taipower Company, and compared with DEKF-RBFNN and GRD-RBFNN (gradient decent RBFNN) models. Simulation results of three cases show that the proposed method has better forecasting performance than the other methods. © 2012 Elsevier Ltd.


Pai M.-C.,Nan Kai University of Technology
International Journal of Control, Automation and Systems | Year: 2013

This paper presents a methodological approach to design an observer-based adaptive sliding mode control to realize the problem of robust tracking and modeling following for a class of uncertain linear systems. Only partial information of the system states is known. Based on Lyapunov stability theorem, it will be shown that the proposed scheme guarantees the stability of closed-loop system and achieves zero-tracking error in the presence of parameter uncertainties and external disturbances. The proposed observer-based adaptive sliding mode control scheme can be implemented without requiring a priori knowledge of upper bounds on the norm of the uncertainties and external disturbances. This scheme assures robustness against system uncertainties and disturbances. Both the theoretical analysis and illustrative example demonstrate the validity of the proposed scheme. © 2013 Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg.


Ko C.-N.,Nan Kai University of Technology
Engineering Applications of Artificial Intelligence | Year: 2012

This paper presents an annealing dynamical learning algorithm (ADLA) to train wavelet neural networks (WNNs) for identifying nonlinear systems with outliers. In ADLAWNNs, wavelet-based support vector regression (WSVR) is adopted to determine the initial translation and dilation of a wavelet kernel and the weights of WNNs due to the similarity between WSVR and WNNs. After initialization, ADLA with nonlinear time-varying learning rates is applied to train the WNNs. In the ADLA, the determination of the learning rates would be a key work for the trade-off between stability and speed of convergence. A computationally efficient optimization method, particle swarm optimization (PSO), is adopted to find the optimal learning rates to overcome the stagnation in the training procedure of WNNs. Due to the advantages of WSVR and ADLA (WSVRADLA), the WSVR-based ADLAWNNs (WSVRADLAWNNs) can robust against outliers and achieve the promising efficiency of system identifications. Three examples are simulated to confirm the performance of the proposed algorithm. From the simulated results, the feasibility and superiority of the proposed WSVRADLAWNNs for identifying nonlinear systems with artificial outliers are verified. © 2011 Published by Elsevier Ltd. All rights reserved.

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