Rating TAs in fuzzy QFD by objective penalty function and fuzzy TOPSIS based on weighted Hamming distance

2020 ◽  
Vol 39 (3) ◽  
pp. 3665-3679
Author(s):  
Jing Wang ◽  
Bing Yan ◽  
Guohao Wang ◽  
Liying Yu
Keyword(s):  
Nonlinear Programming ◽  
Penalty Function ◽  
Quality Function Deployment ◽  
Hamming Distance ◽  
Decision Makers ◽  
Programming Models ◽  
Ideal Solution ◽  
Quality Function ◽  
Order Preference ◽  
Objective Penalty Function

Quality function deployment (QFD) is an useful tool to solve Multi-criteria decision making, which can translate customer requirements (CRs) into the technical attributes (TAs) of a product and helps maintain a correct focus on true requirements and minimizes misinterpreting customer needs. In applying quality function deployment, rating technical attributes from input variables is a crucial step in fuzzy environments. In this paper, a new approach is developed, which rates technical attributes by objective penalty function and fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) based on weighted Hamming distance under the case of uncertain preference characteristics of decision makers in fuzzy quality function deployment. A pair of nonlinear programming models with constraints and a relevant pair of nonlinear programming models with unconstraints called objective penalty function models are proposed to gain the fuzzy important numbers of technical attributes. Then, this paper compares the fuzzy numbers by fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) method based on weighted Hamming distance in consideration of the uncertain preference characteristics of decision makers. To end with, the developed method is examined with the numerical examples.

A fuzzy technique for order preference by similarity to an ideal solution–based quality function deployment for prioritizing technical attributes of new products

2016 ◽  
Vol 230 (12) ◽  
pp. 2249-2263 ◽  
Author(s):  
Xiuzhen Li ◽  
Xinguo Ming ◽  
Wenyan Song ◽  
Siqi Qiu ◽  
Yuanju Qu ◽  
...  
Keyword(s):  
Quality Function Deployment ◽  
Fuzzy Numbers ◽  
New Products ◽  
New Technique ◽  
Relationship Matrix ◽  
Ideal Solution ◽  
Quality Function ◽  
Order Preference ◽  
Fuzzy Quality Function Deployment ◽  
Customer Need

Quality function deployment is a cross-functional decision-making tool that converts customer needs into technical attributes of new products. Fuzzy numbers are usually adopted to evaluate the customer need importance and the customer need–technical attribute relationships. However, the weighted normalized customer need–technical attribute relationship matrix is not always full rank. If the different fuzzy numbers of two technical attributes are defined as the fuzzy negative ideal solutions, both the closeness coefficients are 0, and the traditional technique for order preference by similarity to an ideal solution cannot prioritize the two technical attributes. Actually, the rankings of different fuzzy numbers are not identical. To solve this problem, we present a new technique for order preference by similarity to an ideal solution to prioritize technical attributes in the fuzzy quality function deployment. The fuzzy positive ideal solution, fuzzy negative ideal solution, and distance measurement of the new technique for order preference by similarity to an ideal solution are improved. As a result, the proposed method not only prioritizes various forms of numbers without considering the lower and upper limits, the median, and boundary interval but also deals with the nonfull rank matrix. Besides, the Theory of Inventive Problem Solving is used to solve technical conflicts which are identified by the line-fitting method. The prioritization results from the proposed method can help to reasonably allocate design and manufacturing resources. Finally, a case on phone shell is given to illustrate the application of the proposed quality function deployment method.

Development of an Integrated TOPSIS-Quality Function Deployment Model for Sustainability Assessment of Indian Banks

2019 ◽  
pp. 267-285
Author(s):  
Rishi Dwivedi ◽  
Bhaskar Bhowani ◽  
P. Kritee Rao
Keyword(s):  
Quality Function Deployment ◽  
Sustainability Assessment ◽  
Social Dimensions ◽  
Quality Function ◽  
Wide Range ◽  
Priority Weights ◽  
Order Preference ◽  
Novel Method ◽  
Indian Banks ◽  
Sustainability Principles

The contribution of banks to sustainable advancement is supreme, considering the vital part they play in funding the economic actions of the human race. But, in the fast-changing banking ecosystem, quantifying the level of sustainability attained by financial institutions is a challenging task due to the need of considering a wide range of economic, environmental, and social dimensions concurrently. In this chapter, a novel method based on the technique for order preference by similarity to ideal solution (TOPSIS) and quality function deployment (QFD) is proposed for the first time to evaluate the sustainable efficiency of banking operations. The QFD technique is employed here to provide due importance to the customers' needs with respect to banks' sustainability, and subsequently calculate the priority weights of the considered criteria of sustainability principles. Then, TOPSIS is employed to rank alternatives based on the extent to which they conform to sustainability principles.

scholarly journals A Novel Extension of the Technique for Order Preference by Similarity to Ideal Solution Method with Objective Criteria Weights for Group Decision Making with Interval Numbers

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1460
Author(s):  
Dariusz Kacprzak
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Ease Of Use ◽  
Decision Makers ◽  
Ideal Solution ◽  
Interval Numbers ◽  
Group Matrix ◽  
Order Preference ◽  
Criteria Weights

This paper presents an extension of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method with objective criteria weights for Group Decision Making (GDM) with Interval Numbers (INs). The proposed method is an alternative to popular and often used methods that aggregate the decision matrices provided by the decision makers (DMs) into a single group matrix, which is the basis for determining objective criteria weights and ranking the alternatives. It does not use an aggregation operator, but a transformation of the decision matrices into criteria matrices, in the case of determining objective criteria weights, and into alternative matrices, in the case of the ranking of alternatives. This ensures that all the decision makers’ evaluations are taken into account instead of their certain average. The numerical example shows the ease of use of the proposed method, which can be implemented into common data analysis software such as Excel.

Interval Rough Neutrosophic TOPSIS Strategy for Multi-Attribute Decision Making

2020 ◽  
pp. 98-118
Author(s):  
Rumi Roy ◽  
Surapati Pramanik ◽  
Tapan Kumar Roy
Keyword(s):  
Decision Making ◽  
Euclidean Distance ◽  
Hamming Distance ◽  
Ideal Solution ◽  
Numerical Example ◽  
New Strategy ◽  
Order Preference ◽  
Multi Attribute Decision Making ◽  
Ranking Order ◽  
Negative Ideal Solution

In this chapter, the authors present a new strategy for multi-attribute decision making in interval rough neutrosophic environment. They define Hamming distance and Euclidean distance between interval rough neutrosophic numbers. They also define interval rough neutrosophic relative positive ideal solution (IRNRPIS) and interval rough neutrosophic relative negative ideal solution (IRNRNIS). Then the ranking order of the alternatives is obtained by the technique for order preference by similarity to ideal solution (TOPSIS) strategy. Finally, a numerical example is provided to demonstrate the applicability and effectiveness of the proposed interval rough neutrosophic TOPSIS strategy.

A circular economy model for waste management in India

2021 ◽  
pp. 0734242X2110291
Author(s):  
Chandrakant B Kamble ◽  
Ramasamy Raju ◽  
Raman Vishnu ◽  
Raju Rajkanth ◽  
Agamuthu Pariatamby
Keyword(s):  
Waste Management ◽  
Circular Economy ◽  
Success Factors ◽  
Decision Makers ◽  
Ideal Solution ◽  
Economy Model ◽  
Critical Success ◽  
Order Preference ◽  
Closing The Loop ◽  
Structured Questionnaire

Management of waste is one of the major challenges faced by many developing countries. This study therefore attempts to develop a circular economy (CE) model to manage wastes and closing the loop and reducing the generation of residual wastes in Indian municipalities. Through extant literature review, the researchers found 30 success factors of CE implementation. Using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) SIMOS approach, the rating and weight of decision makers (DMs) for each factor were collected. A structured questionnaire has been developed incorporating all these 30 factors, to extract the most important factors. The data was collected from top 10 officials (DMs) from the Chennai municipality, who handle three regions (metropolitan, suburbia and industrial). Based on the TOPSIS SIMOS analysis, nine CE implementing factors (critical success factors (CSFs)) among the 30 variables that were significant based on the cut-off value was identified. A CE model has been proposed based on these nine CSFs for waste management in India.

scholarly journals The Extension of Quality Function Deployment Based on 2-Tuple Linguistic Representation Model for Product Design under Multigranularity Linguistic Environment

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Ming Li
Keyword(s):  
Product Design ◽  
Quality Function Deployment ◽  
Decision Makers ◽  
Linguistic Information ◽  
Quality Function ◽  
Discrimination Ability ◽  
Linguistic Representation ◽  
Analysis Process ◽  
Representation Model ◽  
Evaluation Information

Quality function deployment (QFD) is a customer-driven approach for product design and development. A QFD analysis process includes a series of subprocesses, such as determination of the importance of customer requirements (CRs), the correlation among engineering characteristics (ECs), and the relationship between CRs and ECs. Usually more than group of one decision makers are involved in the subprocesses to make the decision. In most decision making problems, they often provide their evaluation information in the linguistic form. Moreover, because of different knowledge, background, and discrimination ability, decision makers may express their linguistic preferences in multigranularity linguistic information. Therefore, an effective approach to deal with the multi-granularity linguistic information in QFD analysis process is highly needed. In this study, the QFD methodology is extended with 2-tuple linguistic representation model under multi-granularity linguistic environment. The extended QFD methodology can cope with multi-granularity linguistic evaluation information and avoid the loss of information. The applicability of the proposed approach is demonstrated with a numerical example.

scholarly journals ANALYSIS OF TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION IN DETECTING COFFEE BEAN QUALITY

2021 ◽  
Author(s):  
Solly Aryza ◽  
Lavenia Ulandari
Keyword(s):  
Decision Support ◽  
Good Method ◽  
Decision Makers ◽  
Coffee Bean ◽  
Ideal Solution ◽  
Best Value ◽  
Coffee Beans ◽  
Order Preference ◽  
Coffee Farm ◽  
Analysis Of Technique

Decision support system is a science that can be applied in various fields to be able to assistdecision makers in supporting not as absolute decision makers. As a decision has a reliablemethod in each case or data processed. Like TOPSIS is a very good method in helpingdecision making that is implemented well in a system. In this paper, detection is carried out tohelp support decisions on quality coffee beans. Coffee is a typical drink from variouscountries. Coffee produced from quality coffee beans and coffee farm fields. So that the finalresult of this paper is to get the best value for detecting quality coffee beans based on 3 coffeebean farming fields, in which the three fields are labeled with A1 and the optimal criterionvalue is 0.61.

scholarly journals Adding value to satisficing decisions using TOPSIS in service provider selection problems

2016 ◽  
Vol 7 (1) ◽  
pp. 34-39
Author(s):  
Solly Matshonisa Seeletse
Keyword(s):  
Service Provider ◽  
Selection Process ◽  
Decision Makers ◽  
Added Value ◽  
Ideal Solution ◽  
Scientific Methods ◽  
Selection Problems ◽  
Selection Processes ◽  
Order Preference ◽  
Reduced Costs

Selection processes of credible candidates in competitions are often flawed. The flaws may be deliberate when there is corruption. In other cases the flaws occur because of the decision makers’ inadequacies. Many competitors do their best in developing exceptional proposals, but unfairness of the decision makers undermines these efforts. Ideally, undeserving candidates should be disqualified, and deserving ones be allowed to contest. Systematic methods should be used in the proposal evaluation, and the process should be verifiable. This paper discusses scientific methods proposed for use to select a criterion-based worthy competitor in service provider selection problems. The method is a technique for order preference by similarity to ideal solution (TOPSIS). TOPSIS is a mathematically-derived statistical method useful to offset the biases in the selection process. Features that address both added value and reduced costs are incorporated in the TOPSIS selection process. A numerical example is included to demonstrate TOPSIS fortes

Improving the weight of Technical Attributes in Quality Function Deployment by the Integration of Techniques for Order of Preference by Similarity to Ideal Solution method

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ajit Kumar Singh ◽  
A.M. Rawani
Keyword(s):  
Customer Satisfaction ◽  
Quality Function Deployment ◽  
Solution Method ◽  
Ideal Solution ◽  
Quality Function ◽  
Content Type ◽  
Product Service ◽  
Practical Implications

PurposeThis study aims to integrate the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method with quality function deployment (QFD) which helps to improve the weight of technical attributes by calculating the closeness of particular attributes with the best attributes and farthest from the worst technical attributes simultaneously.Design/methodology/approachFor the fulfilment of the aim of the study, detailed literature is reviewed and a suitable tool for score calculation has been selected. Further, the proposed methodology is applied in a literature-based case study, and a new weight is calculated and compared with the previous weight.FindingsThe finding of the study suggests that higher weightage is assigned to those technical attributes which is very close to the best technical attribute, and lower weightage is assigned to the technical attributes which are very close to worst technical attributes. Therefore, the weight calculated with the help of the proposed methodology will suggest to optimally invest the resources on technical attributes so that the maximum customer satisfaction is achieved.Practical implicationsThe proposed method will help in better score calculation of QFD. Therefore, the use of the proposed method will help in better product and service design for maximum customer satisfaction.Social implicationsProposed methodology aims to help managers, administrators, QFD practitioners and product/service designers to design a new product/service or mange the quality of existing product/services in an effective way.Originality/valueThis is the first kind of study, in which modification in score calculation has been proposed. This modification will help in the better assignment of resources for maximum customer satisfaction.

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