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IEEE International Conference on System of Systems Engineering (SoSE)

作者： Samuel Vanfossan Cihan H. Dagli Benjamin Kwasa Engineering Management and Systems Engineering Missouri University of Science and Technology Rolla USA

ISBN: (数字)9781728180502

ISBN: (纸本)9781728180519

Sponsored by expedient technologic innovation, consumers frequently expect manufacturer offerings to exhibit extensive product variety and regular product advancement. These expectations have rendered many traditional production practices less applicable. Chiefly impacted is the notion of mass produced, low-variety artifacts via massive assembly lines. These operations have difficulty meeting the high-customization, short life-cycle requirements imposed by contemporary demand. Many industries and organizations have begun the transformation from these rigid assembly mechanisms to a more versatile, cellular production strategy known as seru production. To facilitate this transition, methods are needed to aid manufacturers in appropriately selecting and arranging seru system components, a critical step in seru system design. Herein, a generalized model is proposed utilizing a system-of-systems architecting approach to determine the component assembly best suiting the needs of the manufacturing entity. Candidate architectures are generated and evaluated using a multi-objective genetic algorithm from which a preferred alternative is selected through a fuzzy inference system. Directing this genetic algorithm, domain-independent objectives are presented, maintaining applications to most seru production design scenarios. The proposed method is then applied to a camera production example, culminating in the identification of a well-performing architecture. The presented method should find increased use as organizations further adopt this flexible production methodology.

关键词： Seru production system meta-architecture generation multi-objective genetic algorithm fuzzy inference system system-of-systems architecting fuzzy inference system Product Design component assemble multi-objective evolutionary algorithm Assembly Lines Production systems

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MOGA-Based Structural Design Method for Diagrid Structural Control System Subjected to Wind and Earthquake Loads

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2018年第5期18卷 1598-1606页

作者： Kim, Hyun-Su Kang, Joo-Won Sunmoon Univ Div Architecture Architectural & Civil Engn Asan South Korea Yeungnam Univ Sch Architecture Gyongsan South Korea

An integrated optimal structural design method for a diagrid structure and control device was developed. A multi-objective genetic algorithm was used and a 60-story diagrid building structure was developed as an example structure. Artificial wind and earthquake loads were generated to assess the wind-induced and seismic responses. A smart tuned mass damper (TMD) was used as a structural control system and an MR (magnetorheological) damper was employed to develop a smart TMD (STMD). The multi-objective genetic algorithm used five objectives including a reduction of the dynamic responses, additional stiffness and damping, mass of STMD, capacity of the MR damper for the integrated optimization of a diagrid structure and a STMD. From the proposed method, integrated optimal designs for the diagrid structure and STMD were obtained. The numerical simulation also showed that the STMD provided good control performance for reducing the wind-induced and seismic responses of a tall diagrid building structure.

关键词： Integrated structural optimal design Diagrid structural system Smart tuned mass damper multi-objective genetic algorithm Vibration control Wind excitation Earthquake load

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Application of entransy-dissipation-based thermal resistance for performance optimization of spiral-wound heat exchanger

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2018年 116卷 743-750页

作者： Wang, Simin Jian, Guanping Wang, Jiarui Sun, Lijuan Wen, Jian Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China

The effects of geometrical parameters on thermal resistance based on entransy dissipation caused by heat transfer (R-ht) and fluid friction (R-ff) of spiral-wound heat exchanger (SWHE) were studied by numerical method. The simulation results show that all geometrical parameters (spiral angle, external diameter, layer pitch, tube pitch) are negatively correlated with R-ff because of flow pattern transition caused by the variation of geometrical parameters and the changing of effective flow area which would cause the decrease of entransy dissipation related to fluid friction. For the entransy dissipation due to the heat transfer, the increase of layer pitch are positive to it while both the tube pitch and external tube diameter are negative to R-ht, and with the increase of the spiral angle, the R-ht decrease at first and then increase. What is more, the MOGA optimization of SWHE was carried out based on different types of objective functions. Compared with the traditional objective functions (minimize Delta P and maximize K), R-ff and R-ht obtained from minimizing the entransy-dissipation-based thermal resistance reduce by an average of 90.51% and 34.13%, respectively. Compared with original structure, the comprehensive performance evaluation factor (Nu/f/(1/3)) of traditional optimal results is improved by an average of 41.02%, while that of optimal structures obtained from entransy theory is strengthened by an average of 76.64%. The results demonstrate that the objective functions of minimizing the entransy-dissipation-based thermal resistance are better than that of traditional objective functions for optimization of spiral wound heat exchanger. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Spiral-wound heat exchanger Entransy theory Entransy-dissipation-based thermal resistance multi-objective genetic algorithm Optimization

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HIV–TB co-infection treatment control using multi-objective optimized sliding mode

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Informatics in Medicine Unlocked 2020年 19卷

作者： Lakmesari, S. Hadipour Mahmoodabadi, M.J. Hadipour, S. Department of Mechanical Engineering Sirjan University of Technology Sirjan Iran Department of Infectious Diseases School of Medicine Guilan University of Medical Sciences Rasht Iran

Mycobacterium tuberculosis (MTB) and Human immunodeficiency virus (HIV) are two causes of infection which threaten human health over recent decades. There is abundant evidence indicating that these diseases are related to each other. People living with HIV have a 30-fold increase in tuberculosis infection compared to healthy people. In recent years, medical researchers and other specialists including engineers and basic scientists have tried to propose treatments for dangerous diseases by means of mathematical simulation. In this paper, a mathematical model of the HIV-TB co-infection individuals is regarded in order to represent different characteristics of such people. In the next step, a sliding mode controller is implemented on first and second HIV treatment rates to minimize the Total Burden resulted from the co-infection. Moreover, the multi-objective genetic algorithm (MOGA) is used to optimize the sliding mode controller coefficients. The MOGA is accurate and resilient and its Pareto front has a reasonable variety. In this method, designers acceptably use defeated points with more than one objective function and are also able to choose appropriate solutions according to their preferences. The results suggest that disease deaths, new infections, and immune reconstitution inflammatory syndrome cases play an important role in the problem. © 2020 The Authors

关键词： HIV-TB co-Infection Human immunodeficiency virus multi-objective genetic algorithm Sliding mode control Tuberculosis

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multi-objective demand fulfillment problem for solar cell industry

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 125卷 688-694页

作者： Zheng, Jia-Nian Chien, Chen -Fu Wu, Jei-Zheng Asia Univ Dept Business Adm 500 Lioufeng Rd Taichung 41354 Taiwan Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu Taiwan Soochow Univ Dept Business Adm 56 Sect 1Kuei Yang St Taipei 10048 Taiwan

With the growth of green energy market, there are many companies with vertical integration and outsourcing process. Their business models in the solar cell industry cause different influence on the business objectives including revenue, profit, and gross margin. The sales managers are hard to generate fast and reliable ATP (Available-to-promise) which covered all business objectives. Under the various products types with limited quantity, the loading of factory is uncertain and the quantity of order becomes huger. This study formulates multi-objective demand fulfillment problem in solar cell industry and applies a multi-objective genetic algorithm (moGA) to optimize the ATP. The model trade-offs objectives with by Pareto approach without sacrificing other benefits. The multi-population strategy used for stability of solutions. Results of empirical study show that the proposed moGA can improve gross margin while maintaining the desired revenue and profit. With effective solutions and reasonable computing time, the decision support system with proposed moGA has become a regular communication bridge between sales and production departments in the case company.

关键词： Demand fulfillment Solar cell multi-objective genetic algorithm Decision support system

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Pareto design of Load Frequency Control for interconnected power systems based on multi-objective uniform diversity genetic algorithm (MUGA)

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年第0期80卷 333-346页

作者： Nikmanesh, E. Hariri, O. Shams, H. Fasihozaman, M. Univ Illinois Dept Mech & Ind Engn Chicago IL 60607 USA Islamic Azad Univ Dept Elect Engn South Tehran Branch Tehran Iran Univ Guilan Dept Mech Engn Rasht Iran

In this study, it is tried to employ a state-of-the-art multi-objective uniform-diversity genetic algorithm (MUGA) for Pareto optimization of PI/PID controllers in Load Frequency Control (LFC) of power systems. At first, multi objective optimization of a linear non-reheat two-area interconnected power system is conducted with respect to three conflicting objective functions. Gains of PI and PID controllers are considered as design variables while the objective functions are Integral Time multiply Absolute Error (ITAE), minimum damping ratio of dominant eigenvalues, and settling times in frequency and tie-line power deviations. To illustrate superiority of MUGA in finding optimum values of the deign variables, the proposed designs by MUGA are compared with those proposed by single and multi-objective optimization methods such as BFOA, hBFOA-PSO, and NSGA-II;the results indicate there is a noticeable improvement in response of the system. Further, robustness of the proposed designs is demonstrated by varying the system parameters from their nominal values and monitoring sensitivity of the system response to the variations. At the end, to take nonlinearities and physical constraints into account and to evaluate performance of MUGA in more complex system, a three unequal area hydro thermal system with generation rate constraints is considered. (c) 2016 Elsevier Ltd. All rights reserved.

关键词： multi-area power systems Load Frequency Control (LFC) PID controller Generation rate constraint (GRC) Pareto optimization multi-objective genetic algorithm

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The multi-objective Optimization Model of Flue Aimed Temperature of Coke Oven

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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2018年第8期51卷 683-694页

作者： Tian, Zhongda Li, Shujiang Wang, Yanhong Shenyang Univ Technol Sch Informat Sci & Engn Shenyang 110870 Liaoning Peoples R China

At present, the flue aimed temperature in coke oven production is mainly determined by artificial experience, which leads to the lack of theoretical guidance in the control process, and the parameters are difficult to adjust in real time. In this paper, the optimization model of the flue aimed temperature of coke oven is established. The correlation model between coke quality, coke yield, coking energy consumption and the flue aimed temperature is analyzed. Firstly, the factors affecting coke quality, coke yield and coking energy consumption are determined by mechanism analysis. Secondly, an improved grey relational analysis method is proposed to quantitatively analyze the correlation between coke quality, coke yield, coking energy consumption and their influencing factors, so as to determine the input of multi-objective optimization model. Because the neural network has a strong nonlinear approximation effect, the RBF neural network is introduced to establish a prediction model of coke quality, coke yield and coking energy consumption. Then, maximizing coke yield and minimizing coking energy consumption are chosen as the optimization object, the coke quality and production boundary are chosen as constraints, and the local optimization targets as decision variables, whereby the multi-objective optimization model is designed. The multi-objective genetic algorithm is applied to solve this multi-objective optimization problem. The optimal value of the flue aimed temperature of coke oven is obtained. Finally, the practical running results show that the proposed multi-objective optimization model has good applicability.

关键词： Coke Oven multi-objective genetic algorithm Flue Aimed Temperature Improved Grey Relational Analysis RBF Neural Network

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Thermal management of GaN HEMT devices using serpentine minichannel heat sinks

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APPLIED THERMAL ENGINEERING 2018年 140卷 622-636页

作者： Al-Neama, Ahmed F. Kapur, Nikil Summers, Jonathan Thompson, Harvey M. Univ Leeds Sch Mech Engn Inst Thermofluids Leeds LS2 9JT W Yorkshire England Univ Mosul Dept Mech Engn Fac Engn Mosul Iraq

An experimental and numerical investigation of water-cooled serpentine rectangular minichannel heat sinks (MCHS) has been performed to assess their suitability for the thermal management of gallium nitride (GaN) high-electron-mobility transistors (HEMTs) devices. A Finite Element-based conjugate heat transfer model is developed, validated experimentally and used to determine the optimal minichannel width and number of minichannels for a case with a uniform heat flux of 100 W/cm(2). The optimisation process uses a 30 point Optimal Latin Hypercubes Design of Experiments, generated from a permutation genetic algorithm, and accurate metamodels built using a Moving Least Square approach. A Pareto front is then constructed to enable the compromises available between designs with a low pressure drop and those with low thermal resistance to be explored and an appropriate minichannel width and number of minichannels to be chosen. These parameters are then used within conjugate heat transfer models of a serpentine MCHS with silicon, silicon carbide, diamond and graphene heat spreaders placed above a GaN HEMT heating source of area 4.8 x 0.8 mm(2), generating 1823 W/cm(2). A nanocrystalline diamond (NCD) layer with thickness of 2 pm is mounted on the top surface of the GaN HEMT to function as a heat spreader to mitigate the hot spots. The effect of volumetric flow rate and heat spreader thickness on the chip temperature has been investigated numerically and each of these has been shown to be influential. For example, at a volumetric flow rate of 0.10 l/min, the maximum chip temperature can be reduced from 124.7 degrees C to 96.7 degrees C by employing a 25 pm thick graphene heat spreader attached to the serpentine MCHS together with a NCD layer compared with a serpentine MCHS without these heat spreaders.

关键词： Serpentine minichannel heat sink Conjugate heat transfer CFD GaN HEMTs Heat spreader multi-objective genetic algorithm Nanocrystalline diamond layer

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Fluid-thermal-structural analysis and structural optimization of spiral-wound heat exchanger

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2018年 95卷 42-52页

作者： Wang, Simin Jian, Guanping Xiao, Juan Wen, Jian Zhang, Zaoxiao Tu, Jiyuan Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Shanxi Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China Tsinghua Univ Minist Educ Key Lab Adv Reactor Engn & Safety Inst Nucl & New Energy Technol Beijing 100084 Peoples R China

Based on the liquid-solid-thermal coupling method, the effects of configuration parameters on the flow characteristic, the heat transfer performance and the stress distribution on the tube bundle of spiral-wound heat exchanger (SWHE) were numerically studied. The results show that the shell-side flow patterns change from cross flow into oblique flow and the overall heat transfer coefficient increases firstly and then decreases with the increase of the wingding angle. The maximum stress intensity decreases firstly with the increase of the inlet flow rate and then decreases. The thermal stress takes a leading position in the tube bundle stress distribution attributed to the higher heat transfer temperature difference. With the inlet flow rate increasing, the heat transfer is enhanced and the temperature difference decreases which result in the decrease of the proportion of thermal stress and the increase of the impact of the primary stress. The optimal structure was obtained by the multi-objective genetic algorithm based on the genetic Aggregation response surface. Compared with the original structure, the average comprehensive performance of three optimal candidates of SWHE increase by 57.64% and the maximum stresses intensity of the tube bundle are far less than the material allowable stress.

关键词： Spiral-wound heat exchanger Fluid-thermal-structural analysis Parametric modeling multi-objective genetic algorithm

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A Markov chain approach for double-objective economic statistical design of the variable sampling interval (X)over-bar control chart

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2018年第2期47卷 277-288页

作者： Safe, H. Kazemzadeh, R. B. Kanani, Y. Gholipour Islamic Azad Univ Qaemshahr Branch Dept Ind Engn Qaemshahr Iran Tarbiat Modares Univ Fac Ind & Syst Engn Tehran *** Iran

An (X) over bar chart with variable sampling interval (VSI) has been shown superior to the traditional (X) over bar chart with fixed ratio sampling (FRS). A constraint approach is not an efficient method for Economic Statistical Design (ESD) of VSI control charts because statistical properties are of the same importance as economic properties and should be optimized simultaneously. Then, a multi-objective genetic algorithm for ESD is proposed for identifying the Pareto optimal solutions of control chart design. The proposed method allows the practitioner to be provided with a set of optimal designs rather than a single solution, and they can select the locally optimal solution according to the process information. Through an illustrative example, the advantages of the proposed approach are shown by providing a list of viable optimal solutions and graphical representations, which indicate the advantage of flexibility and adaptability of our approach. Performance measures of these adaptive control charts are obtained through a Markov chain approach.

关键词： Economic statistical design Markov chain multi-objective genetic algorithm Pareto front Variable sampling interval (VSI)

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