Integrated Uncertainty in Knowledge Modelling and Decision by Saleha Raza, Sajjad Haider (auth.), Zengchang Qin, Van-Nam PDF

By Saleha Raza, Sajjad Haider (auth.), Zengchang Qin, Van-Nam Huynh (eds.)

ISBN-10: 3642395147

ISBN-13: 9783642395147

ISBN-10: 3642395155

ISBN-13: 9783642395154

This booklet constitutes the refereed lawsuits of the foreign Symposium on built-in Uncertainty in wisdom Modeling and selection Making, IUKM 2013, held in Beijing China, in July 2013. the nineteen revised complete papers have been rigorously reviewed and chosen from forty nine submissions and are offered including keynote and invited talks. The papers offer a wealth of latest rules and file either theoretical and utilized study on built-in uncertainty modeling and management.

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Additional info for Integrated Uncertainty in Knowledge Modelling and Decision Making: International Symposium, IUKM 2013, Beijing, China, July 12-14, 2013. Proceedings

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J into an overall representative matrix D = [Dnl ]N ×N in DR × DR. Note that the customers and the design team are assigned importance weighting vectors as (γ1 , γ2 , . . , γK ) and (β1 , β2 , . . , βJ ), respectively. Therefore, each preferential combination of a customer and a QFD design team member can be associated with an importance weight P . For the sake of convenience, let us re-denote the individual matrix D(Ck , Ej ) of fuzzy preference relations of each preferential combination in DR × DR as Di with its associated importance weight as Pi = γk · βj , (6) where k = 1, 2, .

Fuzzy logic and optimization models for implementing QFD. Comput. Ind. Eng. R. jp Abstract. It is a crucial step to derive the priority order of design requirements (DRs) from customer requirements (CRs) in quality function deployment (QFD). However, it is not straightforward to prioritize DRs due to two types of uncertainties: human subjective perception and user variability. This paper proposes an OWA based group decision-making approach to uncertain QFD with an application to a flexible manufacturing system design.

The importance of CRs has then to be determined to continue the QFD process. In order to obtain the importance of the CRs, a set of customers to be surveyed in a target market is collected such that C = {C1 , C2 , . . , CK } with a weighting vector (γ1 , γ2 , . . , γK ), k γk = 1. Each customer has to provide his/her judgment of importance toward each CR with linguistic variable LI = LI1 , LI2 , . . , LIGI , where GI is the cardinality of the linguistic variable LI . Formally, the importance judgement for customer requirement CRm provided by customer Ck is denoted by CRImk ∈ LI , as shown in Table 1.

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Integrated Uncertainty in Knowledge Modelling and Decision Making: International Symposium, IUKM 2013, Beijing, China, July 12-14, 2013. Proceedings by Saleha Raza, Sajjad Haider (auth.), Zengchang Qin, Van-Nam Huynh (eds.)


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