Multiple criteria decision making with life cycle assessment for material selection of composites

Milani, Abbas S.; Eskicioğlu, Çiğdem; Robles, K.; Bujun, K.; Hosseini-Nasab, Hasan

doi:10.3144/expresspolymlett.2011.104

Cited by 54 publications

(28 citation statements)

References 26 publications

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“…Alternatively in our research utilizing RSO [79,82,83,84], which advocates learning for optimizing, the algorithm selection, adaptation and integration, are done in an automated way and the user is kept in the loop for subsequent refinements and final decisionmaking [64]. Here one of the crucial issue in MCDM is to critically analyzing a mass of tentative solutions related to materials and draping simulation, which is visually mined to extract useful information.…”

Section: Visualization; An Effective Approach To Mcdm and Materials Sementioning

confidence: 99%

“…Moreover the mechanical behavior of woven textiles during the draping process has not been yet fully integrated to the MCDM algorithms. Although many applications and algorithms of MCDM [64] have been previously presented to deal with decision conflicts often seen among design criteria in materials selection. However many drawbacks and challenges are identified associated with their applicability [67].…”

Section: Drapingmentioning

confidence: 99%

“…In an optimal engineering design process for the textile composites, the materials selection integrated with draping can well determine the durability, cost, and manufacturability of final products [61,62]. The process would naturally involve the identification of multiple criteria properties of mechanical, electrical, chemical, thermal, environmental and life cycle costs of candidate materials [64]. In fact multiple criteria from different disciplines which are to be satisfied in a materials selection problem, often because of the criteria conflicts the complexities are even increased.…”

Section: Drapingmentioning

confidence: 99%

“…The results of all the draping simulations of different drape angles are gathered as a data-set for consideration, in addition to already existed data-sets from the earlier case study [64], including the other criteria i.e. mechanical, electrical, chemical, cost, life cycle assessment and environmental.…”

Section: Integration the Mcdm-assisted Materials Selection With Drapinmentioning

confidence: 99%

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A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making

Mosavi¹

2013

IJCA

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A novel environment for optimization, analytics and decision support in general engineering design problems is introduced. The utilized methodology is based on reactive search optimization (RSO) procedure and its recently implemented visualization software packages. The new set of powerful integrated data mining, modeling, visualiztion and learning tools via a handy procedure stretches beyond a decisionmaking task and attempts to discover new optimal designs relating to decision variables and objectives, so that a deeper understanding of the underlying problem can be obtained. In an optimal engineering design environment as such solving the multicriteria decision-making (MCDM) problem is considered as a combined task of optimization and decisionmaking. Yet in solving real-life MCDM problems often most of attention has been on finding the complete Pareto-optimal set of the associated multiobjective optimization (MOO) problem and less on decision-making. In this paper, along with presenting two case studies, the proposed interactive procedure which involves the decision-maker (DM) in the process addresses this issue effectively. Moreover the methodology delivers the capablity of handling the big data often associated with production decision-making as well as materials selection tasks in engineering design problems.

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Section: Visualization; An Effective Approach To Mcdm and Materials Sementioning

confidence: 99%

Section: Drapingmentioning

confidence: 99%

Section: Drapingmentioning

confidence: 99%

Section: Integration the Mcdm-assisted Materials Selection With Drapinmentioning

confidence: 99%

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A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making

Mosavi¹

2013

IJCA

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“…Then we minimize the risk of these coefficients (α 1,k ,...,α n,k ) by adjusting them by a meaningful quantity such as unitized risk factor so-called variation coefficient [36]. The variation coefficient or CV, which is sometimes called relative standard deviation, is defined as a standardized measure of dispersion of a probability distribution in probability theory.…”

Section: Proposed Feature Selection Approachmentioning

confidence: 99%

A fast feature selection approach based on extreme learning machine and coefficient of variation

Ertuğrul¹,

Tağluk²

2017

Turk J Elec Eng & Comp Sci

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Abstract:Feature selection is the method of reducing the size of data without degrading their accuracy. In this study, we propose a novel feature selection approach, based on extreme learning machines (ELMs) and the coefficient of variation (CV). In the proposed approach, the most relevant features are identified by ranking each feature with the coefficient obtained through ELM divided by CV. The achieved accuracies and computational costs, obtained with the use of features selected via the proposed approach in 9 classification and 26 regression benchmark data sets, were compared to those obtained with all features, as well as those obtained with the features selected by a wrapper and a filtering method. The achieved accuracy values obtained with the proposed approach were generally higher than when using all features. Furthermore, high feature reduction ratios were obtained with the proposed approach, including the achieved feature reduction ratios in epilepsy, liver, EMG, shuttle, and abalone. Stock data sets were 90.48%, 90%, 70.59%, 66.67%, 75%, and 77.78%, respectively. This approach is an extremely fast process that is independent of the employed machine-learning methods.

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Introduction to Biobased Composites

AL‐Oqla

2021

Biobased Composites

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Multiple criteria decision making with life cycle assessment for material selection of composites

Cited by 54 publications

References 26 publications

A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making

A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making

A fast feature selection approach based on extreme learning machine and coefficient of variation

Introduction to Biobased Composites

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