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multi-objective decision-making methodology to create an optimal design chain partner combination

COMPUTERS & INDUSTRIAL ENGINEERING 2012年第4期63卷 875-889页

作者： Chiang, Tzu-An Natl Taipei Coll Business Dept Business Adm Taipei 100 Taiwan

In today's highly competitive business environment, many companies adopt the time-to-market strategy to obtain a competitive advantage. To reduce the time and cost of product development and to employ global product development resources, design chain partner evaluation and selection has become a crucial issue. Thus, establishing an optimal design chain partner combination has received significant attention because it has a far-reaching effect on the results of product development. With this perspective, this paper develops an integrated decision-making methodology to assist enterprises as they create an optimal design chain partner combination. First, this study establishes the framework and evaluation models of the criteria for the different roles of design chain partners, including system integration, functional module development and software and component development. Then, this paper applies a weight-restricted DEA (data envelopment analysis) approach to create the models for performance analysis of design chain partners to acquire the performance value of each candidate and select the efficient design chain partners. Moreover, this paper employs the multi-objective performance evaluation model proposed in this paper to analyze the synthesized performance of design chain combinations. Moreover, this research uses a multi-objective genetic algorithm (GA) to search efficiently for the optimal design chain partner combination to minimize product development cost and time and maximize product reliability. Finally, this study employs a derivative new product development project for a digital TV box as a case study to illustrate the efficacy of the proposed methodology. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Design chain partner combination DEA multi-objective genetic algorithm Product development

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Neural networks based predictive control for thermal comfort and energy savings in public buildings

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ENERGY AND BUILDINGS 2012年 55卷 238-251页

作者： Ferreira, P. M. Ruano, A. E. Silva, S. Conceicao, E. Z. E. Univ Algarve P-8005139 Faro Portugal IDMEC IST Ctr Intelligent Syst P-1049001 Lisbon Portugal

The paper addresses the problem of controlling a Heating Ventilation and Air Conditioning (HVAC) system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort, assessed using the predicted mean vote (PMV) index, as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the minimisation of energy, which in most conditions are conflicting goals requiring an optimisation method to find appropriate solutions over time. A discrete model-based predictive control methodology is applied, consisting of three major components: the predictive models, implemented by radial basis function neural networks identified by means of a multi-objective genetic algorithm: the cost function that will be optimised to minimise energy consumption and maintain thermal comfort;and the optimisation method, a discrete branch and bound approach. Each component will be described, with special emphasis on a fast and accurate computation of the PMV indices. Experimental results obtained within different rooms in a building of the University of Algarve will be presented, both in summer and winter conditions, demonstrating the feasibility and performance of the approach. Energy savings resulting from the application of the method are estimated to be greater than 50%. (C) 2012 Elsevier B.V. All rights reserved.

关键词： HVAC predictive control Predicted mean vote Neural networks multi-objective genetic algorithm Thermal comfort Wireless sensor networks

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multi-objective optimization of active steering system with force and displacement coupled control

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Journal of Central South University 2012年第4期19卷 974-981页

作者：赵万忠孙培坤刘顺林逸 epartment of Vehicle Engineering Nanjing University of Aeronautics and Astronautics State Key Laboratory of Mechanical Transmission Chongqing University Department of Vehicle Engineering Nanjing University of Aeronautics and Astronautics School of Mechanical and Vehicular Engineering Beijing Institute of Technology

A novel active steering system with force and displacement coupled control(the novel AFS system) was introduced,which has functions of both the active steering and electric power *** on the model of the novel AFS system and the vehicle three-degree of freedom system,the concept and quantitative formulas of the novel AFS system steering performance were *** steering road feel and steering portability were set as the optimizing targets with the steering stability and steering portability as the constraint *** to the features of constrained optimization of multi-variable function,a multi-variable genetic algorithm for the system parameter optimization was *** simulation results show that based on parametric optimization of the multi-objective genetic algorithm,the novel AFS system can improve the steering road feel,steering portability and steering stability,thus the optimization method can provide a theoretical basis for the design and optimization of the novel AFS system.

关键词： vehicle engineering active steering electric power steering multi-objective genetic algorithm

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Optimization of zigzag flow channels of a printed circuit heat exchanger for nuclear power plant application

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JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY 2012年第3期49卷 343-351页

作者： Lee, Sang-Moon Kim, Kwang-Yong Inha Univ Dept Mech Engn Inchon 402751 South Korea

Printed circuit heat exchanger (PCHE) is recently considered as a recuperator for the high-temperature gas cooled reactor. In this study, shape optimization of zigzag flow channels in a PCHE has been performed to enhance heat transfer performance and reduce the friction loss based on three-dimensional Reynolds-averaged Navier-Stokes analysis with the Shear Stress Transport Turbulence model. A multiobjective genetic algorithm is used for the multi-objective optimization. Two non-dimensional objective functions related to heat transfer performance and friction loss are employed. The shape of a flow channel is defined by two geometric design variables, viz. the cold channel angle and the ellipse aspect ratio of the cold channel. The experimental points within the design space are selected using Latin hypercube sampling as the design of the experiment. The response surface approximation model is used to approximate the Pareto-optimal front. Five optimal designs on the Pareto-optimal front have been selected using k-means clustering. The flow and heat transfer characteristics, as well as the objective function values, of these designs have been compared with those of the reference design.

关键词： printed circuit heat exchanger design optimization multi-objective genetic algorithm Reynolds-averaged Navier-Stokes analysis shear stress transport model

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Energy-based design optimization of steel building frameworks using nonlinear response history analysis

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JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH 2012年第1期68卷 43-50页

作者： Gong, Yanglin Xue, Yusong Xu, Lei Grierson, Donald E. Lakehead Univ Dept Civil Engn Thunder Bay ON P7B 5E1 Canada Univ Waterloo Dept Civil & Environm Engn Waterloo ON N2L 3G1 Canada

Presented in this paper is a design optimization method for steel building frameworks subjected to seismic loading using a nonlinear response history analysis procedure. Minimum weight, minimum seismic input energy and maximum hysteretic energy of fuse members are identified as the three design objectives. Design constraints include the limits on inter-story drift and plastic rotation of member sections. The design optimization method employs a multi-objective genetic algorithm to search for optimal member section sizes from among commercially available steel section shapes. The design method is illustrated for a moment-resisting steel frame of a three-story building. It is concluded the proposed optimization methodology is an effective and efficient application of the capacity-design principle to building frameworks under earthquake loading. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Seismic design Capacity-design Steel frames Moment-resisting frames Nonlinear response history analysis Design optimization multi-objective genetic algorithm

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Protein secondary structure prediction using DWKF based on SVR-NSGAII

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NEUROCOMPUTING 2012年 94卷 87-101页

作者： Zangooei, Mohammad Hossein Jalili, Saeed Tarbiat Modares Univ Elect & Comp Engn Fac Dept Comp Engn SCS LabSch Elect & Comp Engn Tehran Iran

Prediction of protein secondary structure is an important step towards elucidating its three dimensional structure and its function. This is a challenging problem in bioinformatics. By introduction of machine learning for protein structure prediction, a solution has brought to this challenge to some extent. In the literature of Machine learning or data mining, regression and classification problems are typically viewed as two distinct problems differentiated by continuous or categorical dependent variable. There are endeavors to use regression methods to solve the classification problem and vice versa. To regard a classification problem as a regression one, we proposed a method which is based on Support Vector Regression (SVR) classification model as one of the powerful methods in the field of machine intelligence. We applied non-dominated Sorting genetic algorithm II (NSGAII) to find mapping points (MPs) for rounding a real-value to an integer one. Also NSGAII is used for finding out and tuning SVR kernel parameters optimally to enhance the performance of our model and achieve better results. At the other hand, using a suitable SVR kernel function for a particular problem can improve the prediction results remarkably but there is not a kernel which can predict all protein secondary structure classes with acceptable accuracy. Therefore we use a Dynamic Weighted Kernel Fusion (DWKF) method for fusing of three SVR kernels to achieve a supreme performance. Also to improve our method, Position Scoring Matrix (PSSM) profiles are used as the input information to it. The goals of this research are to regulate SVR parameters and fuse different SVR kernel outputs in order to determine protein secondary structure classes accurately. The obtained classification accuracies of our method are 85.79% and 84.94% on RS126 and CB513 datasets respectively and they are promising with regard to other classification methods in the literature. Moreover, for gauging our method behavior in

关键词： Protein secondary structure prediction Machine learning approach Support vector regression multi-objective genetic algorithm

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Hybrid evolutionary optimal MEMS design

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第1-4期63卷 305-317页

作者： Zhang, Ying Agogino, Alice M. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

A hybrid evolutionary design synthesis and optimization process for microelectromechanical systems (MEMS) devices has been developed. The process integrates a MEMS design component library with multiple simulation modules and two levels of design optimization: global multi-objective genetic algorithms (MOGA) and local gradient-based refinement. During the hybrid evolutionary design process, MOGA randomly searches the design space and approaches the desirable design solutions using probabilistic transition rules, and gradient-based local optimization refines promising design candidates with computational efficiency. To efficiently apply hybrid evolutionary optimization techniques on MEMS designs, a hierarchical tree-structured component-based genotype representation has been developed, which incorporates specific engineering knowledge into the design synthesis and optimization process. The MEMS design component library serves as a source of practical and efficient genotypes for the evolutionary process, with each component associated with its instructions and restrictions on genetic operations. The component-based genotype incorporated with engineering knowledge constrains evolutionary searching in appropriate and promising regions of the search space, allowing a deeper search in a given amount of time. Hybrid evolutionary MEMS design synthesis and optimization are demonstrated with surface-micromachined resonator and accelerometer designs.

关键词： Hybrid evolutionary process multi-objective genetic algorithm Microelectromechanical systems Design synthesis and optimization

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multi-objective optimization of a cogeneration plant for supplying given amount of power and fresh water

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DESALINATION 2012年 286卷 225-234页

作者： Shakib, Seyed Ehsan Hosseini, Seyed Reza Amidpour, Majid Aghanajafi, Cyrus KN Toosi Univ Technol Fac Mech Engn Tehran *** Iran

Today, the required energy of thermal desalination is widely supplied by power generation cycles as dual-purpose plants. One of the most considerable issues in combining gas turbine cycle (GT) with thermal desalination is how the operating parameters of GT affect technical and economic aspects of power and water production. Therefore, some researchers have studied dual-purpose plants from the thermodynamic point of view without economic or thermoeconomic considerations. According to the lack of studies in this area, in this paper, a dual-purpose plant is modeled which consisted of a GT with and without an air preheater (APH), heat recovery steam generator (HRSG) and multi-effect evaporation thermal vapor compression desalination (METVC). The system is supposed to supply 40 MW of power and 14,000 m(3)/day of fresh water for an industrial plant near the seashore in the south of Iran. After the simulation stage, a thermoeconomic analysis is performed and, then, a heuristic optimization algorithm, namely, multi-objective genetic algorithm (MOGA) is applied in order to achieve the optimal design. Eventually, it could be concluded that, by applying the optimization approach and using APH, the total cost of products decreases by 19% while the exergy efficiency of the combined cycle increases by 19%. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Gas turbine cycle multi-effect evaporation desalination Thermoeconomic analysis Optimization multi-objective genetic algorithm

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Optimal single input PID-type fuzzy logic controller

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2012年第4期35卷 413-420页

作者： Lee, Shih-Chih Shih, Ching-Long Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei Taiwan Army Acad ROC Dept Elect Engn Tao Yuan Taiwan

Despite the demonstrated effectiveness of fuzzy logic controllers in linear and nonlinear systems, the major limitation is that there exists a steady-state error. This work presents a novel single input PID-type fuzzy logic controller that minimizes the steady-state error. It adopts the function of the error, derivative of the error, and the integral of the error to produce a single input variable of the single input fuzzy logic controller. Controller parameters are then optimized by multiobjective genetic algorithm. The proposed framework was tested on a system that has a third-order transfer function and on a permanent magnet linear synchronous motor system. The proposed framework has obtained good performance in minimizing the steady-state error. Additionally, there are only three rules required.

关键词： multi-objective genetic algorithm single input fuzzy logic controller steady-state error fuzzy rule base

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multi-objective supply planning for two-level assembly systems with stochastic lead times

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COMPUTERS & OPERATIONS RESEARCH 2012年第7期39卷 1325-1332页

作者： Sakiani, R. Ghomi, S. M. T. Fatemi Zandieh, M. Amirkabir Univ Technol Dept Ind Engn Tehran Iran Shahid Beheshti Univ Management & Accounting Fac Dept Ind Management GC Tehran Iran

This paper considers two-level assembly systems whose lead times of components are stochastic with known discrete random distributions. In such a system, supply planning requires determination of release dates of components at level 2 in order to minimize expected holding cost and to maximize customer service. Hnaien et al. [Hnaien F, Delorme X, Dolgui A. multi-objective optimization for inventory control in two-level assembly systems under uncertainty of lead times. Computers and Operations Research 2010: 37:1835-43] have recently examined this problem, trying to solve it through multi-objective genetic algorithms. However, some reconsideration in their paper is unavoidable. The main problem with Hnaien et al. proposal is their wrong mathematical model. In addition, the proposed algorithms do not work properly in large-scale instances. In the current paper, this model is corrected and solved via a new approach based on NSGA-II that is called Guided NSGA-II. This approach tries to guide search toward preferable regions in the solution space. According to the statistical analyses, the guided NSGA-II has the higher performance in comparison with the basic NSGA-II used by Hnaien et al. Moreover, the wrongly reported characteristics of the Pareto front shape provided by Hnaien et al. are modified. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Assembly system Supply planning Stochastic lead time Customer service multi-objective genetic algorithm

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