A hybrid method using experiment design and grey relational analysis for multiple criteria decision making problems

Wang, Peng; Meng, Ping; Zhai, Ji-Ying; Zhu, Zhouquan

doi:10.1016/j.knosys.2013.08.025

Cited by 103 publications

(55 citation statements)

References 23 publications

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“…However, for future research the weighting method may be proposed. Furthermore, both quantitative and qualitative measurements can be applied to consider a more comprehensive evaluation of layout performance [11]. Future research may examine the contribution of physical design of computer laboratory on education achievement since little has been written in the topic [16].…”

Section: Discussionmentioning

confidence: 99%

“…Selection of optimal layout in industrial setting requires for systematic procedure that takes into account the optimization of evaluation factors [9], [10]. The application of grey relational analysis is suitable for multiple attributes decision problems, especially for those problems with very unique characteristic [11].This paper proposes the optimization of computer laboratory layout using multiple performance measures. The use of grey relational analysis is presented to evaluate and select the best among the proposed four new layouts as a case of computer facility management in Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia.…”

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confidence: 99%

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Optimising Computer Laboratory Layout Based on Multiple Performance Measures

Judi¹,

Mohamed²

2016

IJIET

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Abstract-Awareness to select computer laboratory layout has been increased in recent years as more academic administration have come to realize its potential for contributing to conducive teaching and learning environment. Studies on physical design of computer laboratory including optimizing its layout to achieve comfort and fulfill its function are yet little. This paper proposes the optimization of computer laboratory layout using multiple performance measures. The use of grey relational analysis is presented to evaluate and select the best among the proposed four new layouts. The evaluation and selection were performed based on three performance parameters: capacity, total surface area and utility of space. The performance measures were considered to be of equal weight. The use of grey relational analysis shows reliable solution could be suggested that provide a better distinction among the given alternatives.Index Terms-Modeling, learning environment, grey relational analysis. I. INTRODUCTIONAcademic organizations are facing challenges in using their facilities to support main activities: teaching, learning and research. This issue is related to layout management i.e. the arrangement of tools, and usage of space to achieve comfort and fulfill the purpose [1]. Layout plays an important role in developing a conducive environment that supports learning. Among important facilities that require good layout planning is computer laboratory. Computer labs must be designed intelligently and serve the purpose they were intended to serve [2], [3]. Generally, there are two types of users in academic computer laboratory: students and instructors [4]. The needs and requirements of these users need to be identified to ensure this facility supports the purpose especially in current development of learning environment.The recent development of computer technology and availability has led to the rise of laptop and other computer ownership among students and eventually eliminated the need for campus computing laboratories [5], [6]. However, the continued need of computer laboratories in teaching and learning process is reasonable due to the need for standardized software and hardware configurations, high cost and complexity of many specialized software applications and limited number of software license to support educational Manuscript received December 23, 2014; revised April 10, 2015 activities [7].Awareness to select computer laboratory layout has been increased in recent years as more academic administration have come to realize its potential for contributing to conducive teaching and learning environment. Selecting the best arrangement may become crucial issue considering the number of students to occupy the room and hence the number of computers to cater the needs. For example, the frequency of student-to-student and student-to-teacher interactions indicated that the pod arrangement supported more collaboration than the straight row of laboratory arrangement [8].Usually, as many as 40 students register for a certain pr...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Optimising Computer Laboratory Layout Based on Multiple Performance Measures

Judi¹,

Mohamed²

2016

IJIET

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“…Gray Relation Analysis (GRA)-based similarity measuring method is adopted to compute the gray relational degree as measures of similarity. GRA is capable of solving the complicated interrelationships among designated feature values [21,27]. The gray relational degrees of the cases to the current problem are computed as follows:…”

Section: Division Of the Flange Control Surfacementioning

confidence: 99%

“…Multiple similarity measure metrics have been used such as ratio function, Euclidian distance function, Manhattan distance function, and so on [21]. The one which shows the similarity directly can be selected to assess the similarity of current part and retained cases [8,27]. For example, correlative value in gray relation analysis (GRA) can be used [21].…”

Section: Introductionmentioning

confidence: 99%

A rapid and intelligent approach to design forming shape model for precise manufacturing of flanged part

Liu

Yang

et al. 2017

Int J Adv Manuf Technol

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Adjusting the part shape with complex flanges to compensate springback deformation is key to forming shape design for manufacturing rapidly and precisely. Classical forming shape design by displacement adjustment (DA) method using finite element (FE) simulation is usually time-consuming and not accurate enough for complex surface part in industrial application. In this paper, the forming shape is modeled by changing the relations of geometric features of part model with the new flange control surfaces directly. Control surface processing (CSP) method is presented including control surface trimming, cross section division, springback compensation, and extending to design forming shape model of doubly curved flange part with joggles rapidly. The algorithms of cross section curves division of control surfaces and subsequent subdivision of each curve with circular arc and line segments are proposed. A case-based reasoning (CBR) technique and gray relation analysis (GRA) are used to support the intelligent springback prediction of each bending segment of the cross section curve. The geometric data of control surface is expressed in XML format to realize the integration of the CAD-based tools of control surface division and compensation with the Web-based springback prediction system. The approach is demonstrated on an industrial aircraft wing rib part. The forming shape model could be designed rapidly by comparison with DA method. The part shape deviations of flange angle (−0.465°~0.528°) and surface position (−0.3 mm~0.3 mm) were detected by comparing the desired geometry with the actual digital formed part shape, and the results indicate that the approach can achieve the industrial part manufacturing rapidly and precisely.

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“…In order to validate our method by comparing other proposed ones, we collected all the attributes being considered in the above AM process selection literature (see Appendix I). This paper selects the example of Byun and Lee [7], as it is a typical case which has been utilized and compared by many other research work [33,57,20,21,19,29,22]. According to Section 4, the procedures are described in six steps.…”

Section: An Illustrative Examplementioning

confidence: 99%

A weighted rough set based fuzzy axiomatic design approach for the selection of AM processes

Zheng

Wang

et al. 2016

Int J Adv Manuf Technol

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Additive manufacturing (AM) or 3D printing, as an enabling technology for mass customisation or personalization, has been developed rapidly in recent years. Va r io u s design tools, materials, machines and service bureaus can be found in the market. Clearly, the choices are abundant, but users can be easily confused as to which AM process they should use. This paper first reviews the existing multi-attribute decision-making methods for AM process selection and assesses their suitability with regards to two aspects, preference rating flexibility and performance evaluation objectivity. We propose that an approach that is capable of handling incomplete attribute information and objective assessment within inherent data has advantages over other approaches. Based on this proposition, this paper proposes a weighted preference graph method for personalized preference evaluation, and a rough set based fuzzy axiomatic design approach for performance evaluation and the selection of appropriate AM processes.An example based on the previous research work of AM machine selection is given to validate its robustness for the priori articulation of AM process selection decision support.

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A hybrid method using experiment design and grey relational analysis for multiple criteria decision making problems

Cited by 103 publications

References 23 publications

Optimising Computer Laboratory Layout Based on Multiple Performance Measures

Optimising Computer Laboratory Layout Based on Multiple Performance Measures

A rapid and intelligent approach to design forming shape model for precise manufacturing of flanged part

A weighted rough set based fuzzy axiomatic design approach for the selection of AM processes

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