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Method for Generating Multiple MRR Solutions for Application in Cost-Leveling Models

JOURNAL OF INFRASTRUCTURE SYSTEMS 2017年第3期23卷

作者： Yoon, Yoojung Hastak, Makarand Cho, Kyuman West Virginia Univ Dept Civil & Environm Engn Morgantown WV 26506 USA Purdue Univ Div Construct Engn & Management W Lafayette IN 47907 USA Chosun Univ Dept Architectural Engn Gwangju South Korea

Cost-leveling models have been introduced recently to minimize the fluctuations in annual maintenance, repair, and rehabilitation (MRR) costs within a multiyear infrastructure asset-management plan. However, these models were found to produce limited results because they ignored the impacts on all the preceding/succeeding MRR activities in a multiyear plan when the initially identified implementation time of one MRR activity was adjusted for cost-leveling purposes. This paper proposes a method to generate multiple MRR solutions at the facility level for existing cost-leveling models at the network level. The method utilizes two concepts as follows: (1)the fast elitist nondominated sorting genetic algorithm (NSGA-II) to generate a Pareto optimal set of feasible MRR alternatives, and (2)the pseudoweight vector approach with the Euclidean distance and a confidence interval to select multiple MRR solutions from the Pareto optimal set identified by NSGA-II. The proposed method is applied to a sample concrete bridge deck as a case study in this paper;the results show that the proposed method was able to provide multiple MRR solutions having various profiles in terms of the intervention times of the MRR activities and annual MRR costs. Consequently, the proposed method advances the theoretical framework of existing cost-leveling models by combining four approaches to provide multiple MRR solutions at the facility level: (1)NSGA-II algorithm, (2)pseudoweight vector approach, (3)Euclidean distance, and (4)a confidence interval. In addition, it is expected that the proposed method can be well applied to existing multiobjective optimization models to generate a set of nondominated good policies for infrastructure asset management.

关键词： Cost-leveling Decision support system Infrastructure asset management Time float multiobjective genetic algorithm

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Optimization of short-haul aircraft schedule recovery problems using a hybrid multiobjective genetic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第3期37卷 2307-2315页

作者： Liu, Tung-Kuan Chen, Chiu-Hung Chou, Jyh-Horng Natl Kaohsiung First Univ Sci & Technol Inst Engn Sci & Technol Kaohsiung 824 Taiwan

A hybrid multiobjective genetic algorithm is presented in this paper to find an efficient solution for the daily short-haul aircraft schedule recovery problems which usually happen due to some disturbance events and require a time-sensitive solution to meet various hard constraints and soft objectives. The proposed algorithm employs an adaptive evaluated vector (AEV) to guide the solution search and uses the method of inequality-based multiobjective genetic algorithm to provide the multiobjective solution. A simulated disturbance experiment, temporal airport closure, is made and shown that the hybrid method can provide a very efficient short-haul schedule recovery solution under various performance indices. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： multiobjective genetic algorithm Aircraft schedule recovery Short-haul Airline Combinational optimization

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Simultaneous optimization of layout and task schedule for robotic cellular manufacturing systems

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 102卷 396-407页

作者： Suemitsu, Issei Izui, Kazuhiro Yamada, Takayuki Nishiwaki, Shinji Noda, Akio Nagatani, Tatsuya Kyoto Univ Dept Mech Engn & Sci Sakyo Ku Kyoto 6068501 Japan Mitsubishi Electr Corp Tsukaguchi Honmachi 8-1-1 Amagasaki Hyogo Japan

Multi-robot cellular manufacturing systems utilize several articulated industrial robots that cooperatively perform a large number of complex operations when assembling products. To enhance the system performance when designing layouts for such robotic cellular manufacturing systems, the positions of robots and other manufacturing system components must be appropriately determined by considering the sequence of tasks the robots conduct during the assembly process. This paper proposes a new multiobjective layout design optimization technique for robotic cellular manufacturing system layouts that can simultaneously determine the positions of manufacturing components and also task scheduling. First, in this paper, sequence-pair representation is used to specify layout design candidates that inherently avoid interference between assembly system components, and the use of dummy components is introduced to represent layout areas that provide necessary and appropriate spacing between manufacturing system components. In addition, task allocations for each robot are considered as discrete design variables. The design criteria for robot cellular manufacturing system layout designs are clarified and the layout design problem is formulated as a multiobjective optimization problem. To solve the optimization problem, a method based on a multiobjective genetic algorithm is proposed and numerical examples are provided to demonstrate the effectiveness of the proposed method. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Robotic cellular manufacturing system Assembly layout Design optimization multiobjective genetic algorithm Task allocation Sequence-pair

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Multilevel Design Optimization and Operation of a Brushless Double Mechanical Port Flux-Switching Permanent-Magnet Motor

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2016年第10期63卷 6042-6054页

作者： Xiang, Zixuan Zhu, Xiaoyong Quan, Li Du, Yi Zhang, Chao Fan, Deyang Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Jiangsu Key Lab Dr & Intelligent Control Elect Ve Zhenjiang 212013 Peoples R China

This paper proposes a brushless double mechanical port flux-switching permanent-magnet motor for potential application in hybrid electric vehicles. To realize the design objectives of high-torque capability, low-torque ripple, and low-magnetic coupling between the inner and outer motors, a new multilevel design optimization method is proposed to conduct a multiobjectives optimization. First, by adopting the comprehensive sensitivity approach, the whole optimization design process is divided into three levels: nonsensitive level, mild-sensitive level, and strong-sensitive level. Then, to improve the whole design efficiency, the response surface method and multiobjective genetic algorithm are applied in the mild-sensitive level and strong-sensitive level, respectively. Based on the proposed design method, a compromise design among the three design objectives is considered and realized. Finally, a prototype motor is built to verify the validity of the proposed design method and motor operation.

关键词： Comprehensive sensitivity flux-switching permanent-magnet motor hybrid electric vehicles (HEVs) multilevel design optimization multiobjective genetic algorithm response surface (RS) torque capability torque ripple

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Modeling and optimization of machining parameters in cylindrical grinding process

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第9-12期82卷 2167-2182页

作者： Rudrapati, Ramesh Pal, Pradip Kumar Bandyopadhyay, Asish Jadavpur Univ Dept Mech Engn Kolkata 700032 India

In the present work, experiments and analyses have been made to investigate the influence of machining parameters on vibration and surface roughness in traverse cut cylindrical grinding of stainless steel material. The experiments have been conducted as per Box-Behnken design matrix with input parameters as infeed, longitudinal feed, and work speed. Mathematical modeling has been done by response surface methodology (RSM) to develop relationships between process parameters and output response(s). The adequacy of the developed models has been tested with analysis of variance. The contour and surface plots for vibration and surface roughness have been made to reveal how output responses vary with change in the machining parameters. Finally, multiobjective genetic algorithm (MOGA) has been applied to optimize vibration and surface roughness simultaneously. And then, predicted parametric condition has been validated by confirmatory experiments. The proposed optimization methodology (RSM cum MOGA) seems to be useful for analyzing and optimizing any manufacturing process where two or more input parameters influence more than one important output responses.

关键词： Cylindrical grinding Surface roughness Vibration Box-Behnken design Response surface methodology multiobjective genetic algorithm

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Collective Personalized Change Classification With multiobjective Search

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IEEE TRANSACTIONS ON RELIABILITY 2016年第4期65卷 1810-1829页

作者： Xia, Xin Lo, David Wang, Xinyu Yang, Xiaohu Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China Singapore Management Univ Sch Informat Syst Singapore 188065 Singapore

Many change classification techniques have been proposed to identify defect-prone changes. These techniques consider all developers' historical change data to build a global prediction model. In practice, since developers have their own coding preferences and behavioral patterns, which causes different defect patterns, a separate change classification model for each developer can help to improve performance. Jiang, Tan, and Kim refer to this problem as personalized change classification, and they propose PCC+ to solve this problem. A software project has a number of developers;for a developer, building a prediction model not only based on his/her change data, but also on other relevant developers' change data can further improve the performance of change classification. In this paper, we propose a more accurate technique named collective personalized change classification (CPCC), which leverages a multiobjective genetic algorithm. For a project, CPCC first builds a personalized prediction model for each developer based on his/her historical data. Next, for each developer, CPCC combines these models by assigning different weights to these models with the purpose of maximizing two objective functions (i.e., F1-scores and cost effectiveness). To further improve the prediction accuracy, we propose CPCC+ by combining CPCC with PCC proposed by Jiang, Tan, and Kim To evaluate the benefits of CPCC+ and CPCC, we perform experiments on six large software projects from different communities: Eclipse JDT, Jackrabbit, Linux kernel, Lucene, PostgreSQL, and Xorg. The experiment results show that CPCC+ can discover up to 245 more bugs than PCC+ (468 versus 223 for PostgreSQL) if developers inspect the top 20% lines of code that are predicted buggy. In addition, CPCC+ can achieve F1-scores of 0.60-0.75, which are statistically significantly higher than those of PCC+ on all of the six projects.

关键词： Cost effectiveness developer machine learning multiobjective genetic algorithm personalized change classification (PCC)

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multiobjective shape optimization of a hypersonic lifting body using a correlation-based transition model

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2016年第12期230卷 2220-2232页

作者： Xia, Chenchao Tao, Yongpeng Jiang, Tingting Chen, Weifang Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou 310027 Zhejiang Peoples R China

Although substantial numbers of aerodynamic shape optimization works have been carried out in the past few decades, the effects of boundary layer transition are not considered in the overwhelmingly majority of those previous studies. For more accurate prediction of the flow field, and for exploration of relations between aerodynamic heating and geometrical features, the commonly used local correlation-based transition model -Ret is implemented into a computational fluid dynamics (CFD) solver. A CFD-based multiobjective shape optimization method is then described and applied to a hypersonic lifting body in this paper. For parametric modeling and grid deformation, the flexible free form deformation and robust transfinite interpolation techniques are used respectively. The response surface model is adopted as the approximation model for the reduction of computational cost, and the multiobjective genetic algorithm is employed for the exploration of optimal solutions. Optimization processes have been conducted by the optimization toolkit DAKOTA and two Pareto fronts are obtained. Besides, typical solutions on the Pareto fronts are compared and analyzed. The results obtained from the case considered in this study indicate that the lift-to-drag ratio and transition onset position are closely related with the volume of the vehicle, and trade-off can be made for aerodynamic and aerothermodynamic performance from the optimal solutions. Moreover, the design problem of lifting body demonstrates the robustness and practicality of the new optimization procedures, which incorporates the transition phenomenon for hypersonic flow.

关键词： Hypersonic flow lifting body aerodynamic shape optimization aerodynamic heating boundary layer transition multiobjective genetic algorithm surrogate model

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Signal coordination scheme based on traffic emission

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IET INTELLIGENT TRANSPORT SYSTEMS 2016年第2期10卷 89-96页

作者： Ji, Yanjie Hu, Bo Hill, Graeme Guo, Weihong Blythe, Phil Gao, Liangpeng Southeast Univ Jiangsu Key Lab Urban ITS Nanjing Peoples R China Newcastle Univ Sch Civil Engn & Geosci Transport Operat Res Grp Newcastle Upon Tyne NE1 7RU Tyne & Wear England

This study presents a new optimal signal coordination scheme with an emphasis on emission reduction as a result of minimised passenger delay. By analysing the limitation of the algebraic method, a new algorithm using arrival-departure curves was proposed. A microscopic emission simulation platform based on VISSIM (a microscopic multi-modal traffic flow simulation model) and comprehensive modal emission model (CMEM) was established to estimate traffic emission reduction. A multi-objective genetic algorithm was used to find Pareto solutions that simultaneously reduce both passenger delay and vehicle emissions. Using the real-world traffic data collected in Changzhou city, an example of the signal coordination scheme was developed and its impact on emissions was simulated in the VISSIM-CMEM platform. The simulation results show that the proposed scheme achieved an average emission reduction of 28.8% on public transport vehicles and 18.7% on all vehicles over existing signal schemes.

关键词： signal processing air pollution control genetic algorithms algebra Pareto optimisation optimal signal coordination scheme algebraic method arrival-departure curves microscopic emission simulation platform VISSIM microscopic multimodal traffic flow simulation model traffic emission reduction multiobjective genetic algorithm comprehensive modal emission model CMEM Pareto solutions real-world traffic data Changzhou city

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Multi-objective optimisation of wavelet features for phoneme recognition

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IET SIGNAL PROCESSING 2016年第6期10卷 685-691页

作者： Daniel Vignolo, Leandro Leonardo Rufiner, Hugo Humberto Milone, Diego FICH UNL CONICET Sinci Res Inst Signals Syst & Computat Intelligence Ciudad Univ UNLS3000 Santa Fe Argentina Lab Cibernet FI UNER E3100 Oro Verde Entre Rios Argentina

State-of-the-art speech representations provide acceptable recognition results under optimal conditions, though their performance in adverse conditions still needs to be improved. In this direction, many advances involving wavelet processing have been reported, showing significant improvements in classification performance for different kinds of signals. However, for speech signals, the problem of finding a convenient wavelet-based representation is still an open challenge. This study proposes the use of a multi-objective genetic algorithm for the optimisation of a wavelet-based representation of speech. The most relevant features are selected from a complete wavelet packet decomposition in order to maximise phoneme classification performance. Classification results for English phonemes, in different noise conditions, show significant improvements compared with well-known speech representations.

关键词： speech recognition wavelet transforms genetic algorithms signal representation signal classification multiobjective optimisation phoneme recognition signal classification performance speech signals multiobjective genetic algorithm wavelet-based speech representation wavelet packet decomposition feature selection English phonemes noise conditions

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Improving the Long Lead-Time Inundation Forecasts Using Effective Typhoon Characteristics

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WATER RESOURCES MANAGEMENT 2016年第12期30卷 4247-4271页

作者： Jhong, Bing-Chen Wang, Jhih-Huang Lin, Gwo-Fong Natl Taiwan Univ Dept Civil Engn Taipei 10617 Taiwan

In this paper, a new type of inundation forecasting model with the effective typhoon characteristics is proposed by integrating support vector machine (SVM) with multi-objective genetic algorithm (MOGA). Firstly, a comparison of the proposed model and an existing model based on back-propagation network (BPN) is made to highlight the improvement in forecasting performance. Next, the proposed model is compared with the SVM-based model without typhoon characteristics to investigate the influence of typhoon characteristics on inundation forecasting. Effective typhoon characteristics for improving forecasting performance are identified as well. An application to Chiayi City, Taiwan, is conducted to demonstrate the superiority of the proposed model. The results confirm that the proposed model with the effective typhoon characteristics does improve the forecasting performance and the improvement increases with increasing lead-time, especially for long lead-time forecasting. The proposed model is capable of optimizing the input to decrease the negative impact when increasing forecast lead time. In conclusion, effective typhoon characteristics are recommended as key inputs for inundation forecasting during typhoons.

关键词： Inundationforecasting Typhooncharacteristics Longlead-timeforecasting multiobjective genetic algorithm Support vector machine Neural network

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