scholarly journals Contextual and organizational factors in sustainable supply chain decision making: grey relational analysis and interpretative structural modeling

2021 ◽  
Author(s):  
Zhaojun Yang ◽  
Xiaoting Guo ◽  
Jun Sun ◽  
Yali Zhang
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Grey Relational Analysis ◽  
Organizational Factors ◽  
Structural Modeling ◽  
Sustainable Supply Chain ◽  
Relational Analysis ◽  
Grey Relational

A Pythagorean fuzzy ANP-QFD-Grey relational analysis approach to prioritize design requirements of sustainable supply chain

2022 ◽  
pp. 1-15
Author(s):  
Zhenxing Peng ◽  
Lina He ◽  
Yushi Xie ◽  
Wenyan Song ◽  
Jue Liu ◽  
...  
Keyword(s):  
Supply Chain ◽  
Grey Relational Analysis ◽  
Quality Function Deployment ◽  
Sustainable Supply Chain ◽  
Pythagorean Fuzzy Set ◽  
Fuzzy Anp ◽  
Uncertain Environment ◽  
Relational Analysis ◽  
Design Requirements ◽  
Grey Relational

A sustainable supply chain (SSC) is vital for company’s sustainability success, so it is imperative to identify and prioritize SSC’s design requirements (DRs) for better SSC planning. For customer-centric markets, the customer requirements (CRs) need to be integrated into SSC’s DRs. This paper thus proposes a customer-centric approach based on Analytic Network Process (ANP), Quality Function Deployment (QFD), Grey Relational Analysis (GRA), and Pythagorean Fuzzy Set (PFS) to rank SSC’s DRs, considering CRs and information ambiguity. The PFS is combined with ANP, QFD, and GRA to better handle uncertainty in the SSC. The Pythagorean fuzzy ANP is applied to analyze the correlations among the sustainable CRs and determine the corresponding weights. The sustainable CRs are transformed into the DRs using the Pythagorean fuzzy QFD. The relationships among the resulting DRs are analyzed through Pythagorean fuzzy GRA to prioritize DRs. The approach is validated through a case study. The results obtained in this paper shows that the proposed method is efficient to prioritize DRs of SSC with the consideration of sustainable CRs under uncertain environment. The novelties of proposed method are that it not only offers a customer-oriented SSC planning method through the integration of ANP, QFD and GRA, but also can reflect the uncertain information with a broader membership representation space via PFSs. Based on the proposed method, the decision-maker can conduct comprehensive analysis to prioritize DRs and design appropriate SSC to fulfill CRs under uncertain environment.

A New Method of Multi-Attribute Decision-Making Based on Grey Relational Analysis with Time Series

2013 ◽  
Vol 444-445 ◽  
pp. 666-670
Author(s):  
Yi Zhang
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Grey Relational Analysis ◽  
Uncertain Information ◽  
Decision Method ◽  
Rational Decision Making ◽  
Relational Analysis ◽  
Multi Attribute Decision Making ◽  
Subjective Views ◽  
Grey Relational

For the multi-attribute decision making with time series, based on the comprehensive consideration of various indicators of quality and growth degree, with grey relational analysis on less data, uncertain information can be integrated comparison, so we put forward a new decision method considering the decision maker subjective views, and provide a scientific and rational decision-making method. By the example, that method is viable.

scholarly journals Integrated analytical hierarchy process and grey relational analysis approach to measure supply chain complexity

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sujan Piya ◽  
Ahm Shamsuzzoha ◽  
Mohammed Khadem ◽  
Mahmoud Al Kindi
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Analytical Hierarchy Process ◽  
Grey Relational Analysis ◽  
Extensive Literature ◽  
Content Type ◽  
Relational Analysis ◽  
Grey Relational ◽  
Hierarchy Process

PurposeThe purpose of this paper is to understand the drivers that create complexity in the supply chain and develop a mathematical model to measure the level of supply chain complexity (SCC).Design/methodology/approachThrough extensive literature review, the authors discussed various drivers of SCC. These drivers were classified into five dimensions based on expert opinion. Moreover, a novel hybrid mathematical model was developed by integrating analytical hierarchy process (AHP) and grey relational analysis (GRA) methods to measure the level of SCC. A case study was conducted to demonstrate the applicability of the developed model and analyze the SCC level of the company in the study.FindingsThe authors identified 22 drivers of SCC, which were further clustered into five complexity dimensions. The application of the developed model to the company in the case study showed that the SCC level of the company was 0.44, signifying that there was a considerable scope of improvement in terms of minimizing complexity. The company that serves as the focus of this case study mainly needs improvement in tackling issues concerning government regulation, internal communication and information sharing and company culture.Originality/valueIn this paper, the authors propose a model by integrating AHP and GRA methods that can measure the SCC level based on various complexity drivers. The combination of such methods, considering their ability to convert the inheritance and interdependence of drivers into a single mathematical model, is preferred over other techniques. To the best of the authors' knowledge, this is the first attempt at developing a hybrid multicriteria decision-based model to quantify SCC.

scholarly journals A comparison of TOPSIS, grey relational analysis and COPRAS methods for machine selection problem in the food industry of Turkey

2021 ◽  
Vol 9 (2) ◽  
pp. 81
Author(s):  
Sami Özcan ◽  
Ali Kemal Çelik
Keyword(s):  
Decision Making ◽  
Grey Relational Analysis ◽  
Entropy Method ◽  
Sensitivity Analyses ◽  
Food Sector ◽  
Relational Analysis ◽  
Machine Selection ◽  
Order Preference ◽  
Topsis Technique ◽  
Grey Relational

<div data-canvas-width="540.0693333333334">The paper aims to compare the results of the selection/choice of cream separators by using multi-criteria decision-making methods in an integrated manner for an enterprise with a dairy processing capacity of 80 to 100 tons per day operating in the Turkish food sector. A total of 7 alternative products and 7 criteria for milk processing were determined. Criterion weights were calculated using entropy method and then integrated into TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions), GRA (Grey Relational Analysis) and COPRAS (Complex Proportional Assessment) methods. Sensitivity analyses were carried out on the results obtained from the three methods to check for their reliability. At the end of the study, similar alternative and appropriate results were found from the TOPSIS and COPRAS methods. However, different alternative but appropriate or suitable results were obtained from the GRA method. Sensitivity analysis of the three methods showed that all the methods used were valid. In the review of available and related literature, very few studies on machine selection in the dairy and food sector in general were found. For this reason, it is thought that the study will contribute to the decision-making process of companies in the dairy sector in their choice of machinery selections. As far as is known, this paper is the first attempt in extant literature to compare in an integrated manner the results of TOPSIS, COPRAS and GRA methods considered in the study.</div>

Sign in / Sign up

Export Citation Format

Share Document