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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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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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Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design

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COMPUTERS & INDUSTRIAL ENGINEERING 2011年第4期60卷 760-768页

作者： Yang, Chih-Chieh So Taiwan Univ Dept Multimedia & Entertainment Sci Yung Kang 71005 Tainan County Taiwan

This study proposes an expert system, which is called hybrid Kansei engineering system (HKES) based on multiple affective responses (MARs), to facilitate the development of product form design. HKES is consists of two sub-systems, namely forward Kansei engineering system (EKES) and backward Kansei engineering system (BKES). FKES is utilized to generate product alternatives and BKES is utilized to predict affective response of new product designs. Although the idea of HKES and similar hybrid systems have already been applied in various fields, such as product design, engineering design, and system optimization, most of existing methodologies are limited by searching optimal design solutions using single-objective optimization (SOO), instead of multi-objective optimization (MOO). Hence the applicability of HKES is limited while adapting to real-world problems, such as product form design discussed in this paper. To overcome this shortcoming, this study integrates the methodologies of support vector regression (SVR) and multi-objective genetic algorithm (MOGA) into the scheme of HEKS. BKES was constructed by training SVR prediction model of every single affective response (SAR). The form features of these product samples were treated as input data while the average utility scores obtained from all the consumers were used as output values. EKES generates optimal design alternatives using the MOGA-based searching method according to MARS specified by a product designer as the system supervisor. A case study of mobile phone design was given to demonstrate the analysis results. The proposed HKES based on MARs can be applied to a wide variety of product design problems, as well as other MOO problems involving with subjective human perceptions. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Product form design Kansei engineering Support vector regression multi-objective genetic algorithm

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Tribological performance evaluation on brake friction material by using multi-objective optimization methods

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INDUSTRIAL LUBRICATION AND TRIBOLOGY 2021年第4期73卷 599-605页

作者： Oktem, Hasan Shinde, Dinesh Kocaeli Univ Hereke Asim Vocat Sch Div Machine & Met Technol Kocaeli Turkey Gujarat Technol Univ Ahmadabad Gujarat India

Purpose The purpose of this study is to present a novel approach for the evaluation of tribological properties of brake friction materials (BFM). Design/methodology/approach In this study, a BFM was newly formulated with 16 different ingredients and produced using an industrial hot compression molding process. Experimentation was carried out on the brake tester, which was developed for this purpose according to SAE J661 standards. The braking applications, sliding speed and braking pressure were considered as performance parameters, whereas coefficient of friction (CoF) and wear rate as output parameters. The influence of the performance parameters on the output was studied using response surface plots. Analysis of variance and regression analysis was accomplished for post-experimental evaluation of results. multi-criteria decision-making (MCDM) and multi-objective genetic algorithm (MOGA) were applied for estimating the most critical performance parameter combination to evaluate the BFM. Findings The present experimental model was significant and effectively used to predict the performance. MCDM generates the optimal values for the parameters braking applications, braking pressure (Bar) and sliding speed (rpm) as 1000, 30 and 915, whereas MOGA as 1008, 10.503 and 462.8202, respectively. Originality/value An efficient model for performance evaluation of the BFM considering maximum CoF and minimum wear rate was experimentally presented and statistically verified. Also, the two multi-objective optimization methodologies were implemented and compared. A comparison between the results of MCDM and MOGA reveals that MOGA yields 30% better results than MCDM.

关键词： Optimization multi-objective genetic algorithm Coefficient of friction multi-criteria decision-making Brake friction materials Wear rate

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An improved NSGA-II based control allocation optimisation for aircraft longitudinal automatic landing system

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INTERNATIONAL JOURNAL OF CONTROL 2019年第4期92卷 705-716页

作者： Bian, Qi Nener, Brett Wang, Xinmin Northwestern Polytech Univ Sch Automat Xian Shaanxi Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn Perth WA Australia

In this paper, an Improved Nondominated Sorting genetic algorithm II is proposed for control allocation optimisation in the field of aircraft automatic landing. The initial chromosomes are generated by using a quasi-random sequence to get a better initial searching ability. The adaptive crossover operator and mutation operator are proposed for the dynamic searching area by adaptively relocating the target according to the current searching results. The corresponding online boundary adjustment strategy is created to maintain the robustness of the algorithm during the whole searching process. The control relationships between the elevator and engine channels are presented and a set of feasible solutions are chosen as a reasonable control range for the ALS design. Finally, a 6 Degrees of Freedom (6DoF) rigid model of the F/A-18 with external wind perturbation is used as a test bed to demonstrate the feasibility of the proposed method.

关键词： Aircraft automatic landing control allocation optimisation multi-objective genetic algorithm

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A Time-Varying Weight MPC-Based Motion Cueing algorithm for Motion Simulation Platform

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 11767-11778页

作者： Qazani, Mohammad Reza Chalak Asadi, Houshyar Mohamed, Shady Lim, Chee Peng Nahavandi, Saeid Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3125 Australia

The motion cueing algorithm (MCA) is playing the most critical role in motion simulation platform (MSP) to reproduce the realistic motion sensation of the real car for the MSP's users while taking into cogitation of the physical boundaries of the platform. Recently, the model predictive control (MPC) is employed for designing MCAs which led to the formation of MPC-based MCAs. The purpose of the MPC-based MCA is to recalculate the optimal values of the input signals with consideration of the MSP's physical restrictions. All the current MPC-based MCAs have fix weights that can cause the conservative and inefficient utilization of the MSP's workspace limitations as they have been tuned based on the worst-case scenarios to keep the platforms within their physical limitations. Then, the error of motion sensation between the real car and MSP users increases due to the conservative utilization of the MSP's workspace boundaries. The main objective of this study is to provide more efficient workspace utilization to minimise the error of motion feeling between the real car and MSP users while respecting the workspace boundaries. A procedure according to the optimised fuzzy logic-based units is employed to calculate the appropriate MPC weights online while considering the sensed specific force error, sensed angular velocity error and the current motion status of the MSP including linear position, linear velocity and angular position of the cockpit. The proposed MPC-based MCA is designed and developed using MATLAB. The outcomes show a better motion feeling compared with the current MPC-based MCAs.

关键词： Motion cueing algorithm time-varying weight model predictive control fuzzy logic multi-objective genetic algorithm hexapod platform

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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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Data-driven design of ternary alloy catalysts for enhanced oxide-based gas sensors' performance

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COMPUTATIONAL MATERIALS SCIENCE 2019年 161卷 255-260页

作者： Ghosal, Suman Dey, Swati Bhattacharyya, Partha Chattopadhyay, Partha Pratim Datta, Shubhabrata Indian Inst Engn Sci & Technol Dept Elect & Telecommun Engn Howrah 711103 W Bengal India Pailan Coll Management & Technol Dept Elect & Elect Engn Amgachia Rd Kolkata 700104 India Indian Inst Engn Sci & Technol Dept Aerosp Engn & Appl Mech Howrah 711103 W Bengal India Natl Inst Foundry & Forge Technol Ranchi 834003 Bihar India SRM Inst Sci & Technol Dept Mech Engn Chennai 603203 Tamil Nadu India

Informatics based approach was employed to design ternary catalytic alloy electrode from a data base of binary alloys for oxide-based gas sensor to detect methane gas efficiently. Semiconducting Zinc Oxide was used for sensing methane, where alloys of noble metals, mostly pure or binary alloys, were used to increase the sensor parameters of the device. Experimental results of such noble metals or their binary alloys were used to develop the corresponding artificial neural network model describing the three pivotal attributes of the sensor device, viz. response magnitude, response time and recovery time, in respect to ZnO size and the catalyst composition. The models were used in a novel approach to design ternary alloys with superior performance using multi-objective optimization technique, where the Pareto front thus developed was used for designing ternary catalysts. The present scheme of prescriptive data analytics seems to provide some definite clue for experimental study aiming the pre-determined set of sensor parameters.

关键词： Oxide based gas sensor Zinc oxide Ternary alloy catalyst design Response characteristics Artificial neural network multi-objective genetic algorithm

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Fuzzy delay model based fault simulator for crosstalk delay fault test generation in asynchronous sequential circuits

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2015年第1期40卷 107-119页

作者： Jayanthy, S. Bhuvaneswari, M. C. Sri Ramakrishna Engn Coll Dept Elect & Commun Engn ECE Coimbatore 641022 Tamil Nadu India PSG Coll Technol Dept Elect & Elect Engn EEE Coimbatore 641004 Tamil Nadu India

In this paper, a fuzzy delay model based crosstalk delay fault simulator is proposed. As design trends move towards nanometer technologies, more number of new parameters affects the delay of the component. Fuzzy delay models are ideal for modelling the uncertainty found in the design and manufacturing steps. The fault simulator based on fuzzy delay detects unstable states, oscillations and non-confluence of settling states in asynchronous sequential circuits. The fuzzy delay model based fault simulator is used to validate the test patterns produced by Elitist Non-dominated sorting genetic algorithm (ENGA) based test generator, for detecting crosstalk delay faults in asynchronous sequential circuits. The multi-objective genetic algorithm, ENGA targets two objectives of maximizing fault coverage and minimizing number of transitions. Experimental results are tabulated for SIS benchmark circuits for three gate delay models, namely unit delay model, rise/fall delay model and fuzzy delay model. Experimental results indicate that test validation using fuzzy delay model is more accurate than unit delay model and rise/fall delay model.

关键词： Asynchronous sequential circuits crosstalk delay faults fuzzy delay model multi-objective genetic algorithm fault coverage transitions

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Fractional order frequency proportional-integral-derivative control of microgrid consisting of renewable energy sources based on multi-objective grasshopper optimization algorithm

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2022年第2期44卷 378-392页

作者： Tabak, Abdulsamed Necmettin Erbakan Univ Dept Mech Engn Engn Fac 42-A Meram Konya Turkey

In recent years, fractional order proportional-integral-derivative (FOPID) controllers have been applied in different areas in the academy due to their superior performance over conventional proportional-integral-derivative (PID) controllers. When the literature is reviewed, it has been observed that lack of studies that use swarm-based and multi-objective optimization algorithms together with FOPID controllers in frequency control of micro-grid. The load frequency control (LFC) problem is considered as two objectives in order to eliminate the complications that occur when only the frequency deviation is minimized. In our study, a method called MOGOA-FOPID in which both the frequency deviation and the control signal are minimized together for the frequency control in the microgrid is proposed. By using the multi-objective grasshopper optimization algorithm (MOGOA), both the frequency deviation and the control signal are minimized together, and thus, it is aimed to limit the battery capacity, reduce the flywheel jerk and avoid high diesel fuel consumption as well as an effective frequency control. In order to obtain a more realistic system, not only the photovoltaic (PV) solar and wind power but also the load demand is taken in a stochastic structure. Then, the results of the proposed MOGOA-FOPID are compared with the results of multi-objective genetic algorithm (MOGA)-based PID/FOPID and MOGOA-PID and its superiority is demonstrated. Finally, robustness tests of the system are performed under the perturbed parameters and outperform of MOGOA-FOPID over other methods is seen.

关键词： Fractional order PID controller multi-objective grasshopper optimisation algorithm load frequency control microgrid multi-objective genetic algorithm renewable energy sources

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