Decision Mechanism for Supplier Selection Under Sustainability

2017 ◽  
Vol 16 (01) ◽  
pp. 87-115 ◽  
Author(s):  
Congjun Rao ◽  
Mark Goh ◽  
Junjun Zheng
Keyword(s):  
Evaluation System ◽  
Supplier Selection ◽  
Three Dimensions ◽  
Sustainable Supply Chain ◽  
Interval Numbers ◽  
Grey Correlation ◽  
Decision Mechanism ◽  
Correlation Degree ◽  
Sustainable Supplier Selection ◽  
Grey Correlation Degree

Against the backdrop of responsible economic development, sustainable supply chain management (SSCM) is key to achieving the sustainable development for enterprise and industry. In this regard, sustainable supplier selection is crucial in SSCM. By integrating the three dimensions of sustainability, economic, environmental and social, this paper presents a new evaluation system for supplier selection from a sustainability perspective. Specifically, we design a decision mechanism for sustainable supplier selection based on linguistic 2-tuple grey correlation degree. In this proposed mechanism, the hybrid attribute values whereby real numbers, interval numbers and linguistic fuzzy variables coexist are transformed into linguistic 2-tuples. A ranking method based on linguistic 2-tuple grey correlation degree is then presented to rank the suppliers. An application example is presented to highlight the implementation, availability and feasibility of the proposed decision making mechanism.

Research on Strategic Supplier Selection and Evaluation Method of Manufacturing Enterprise Based on AHP-GCA Method

2015 ◽  
Vol 752-753 ◽  
pp. 1384-1392
Author(s):  
Bang Lei Wu ◽  
Xiao Juan Chen ◽  
Jing Yu ◽  
Qing Sun
Keyword(s):  
Supplier Selection ◽  
Manufacturing Industry ◽  
Evaluation Method ◽  
Manufacturing Enterprise ◽  
Quantitative Diagnosis ◽  
Grey Correlation ◽  
Correlation Degree ◽  
Grey Correlation Analysis ◽  
Grey Correlation Degree ◽  
Hierarchy Process

With application of intelligence technology in the manufacturing industry, the supply chain of manufacturing industry is more and more competitive. Strategic supplier is becoming more and more key element for the success of manufacturing enterprise. At present, there are many methods on selection and evaluation of strategic supplier of manufacturing enterprise. Analytical hierarchy process and grey correlation analysis is method which is used to choose strategic supplier of manufacturing enterprise by building optimum reference date and solving grey correlation degree. These have great effects in the enterprise practice in the aspects of qualitative diagnosis, quantitative diagnosis and grey information processing.

A Study on Grey Correlation Degree Model of Venture Capital Intrinsic Value

2013 ◽  
Vol 756-759 ◽  
pp. 4710-4715
Author(s):  
Lin Sen Yin
Keyword(s):  
Evaluation System ◽  
Intrinsic Value ◽  
Degree Theory ◽  
Actual State ◽  
Primary Analysis ◽  
Grey Correlation ◽  
State Evaluation ◽  
Correlation Degree ◽  
Grey Correlation Degree ◽  
Venture Enterprise

In view of the disfigurement that the former research mostly focus on the evaluation of the investment value before investment, not only this paper uses for reference to the former evaluation system, but also successfully integrates it into a unity that the evaluation before investment and after investment. This paper designs a set of evaluation criterion and a set of actual state evaluation system. Comparing both of them is the essential start of evaluation. Then according to it, and based on the grey correlation degree theory, this paper constructs a set of compositive evaluation indexgrowth imitating degree, The method provides a primary analysis frame for monitoring venture enterprise fostering.

scholarly journals Research on Clearing Model of Long-Term Transaction Considering Matching Degree between Generation and Load

2020 ◽  
Vol 165 ◽  
pp. 06033
Author(s):  
Zhang Lin ◽  
Zhang Qinghe ◽  
Zhang Xue ◽  
Gao Hui
Keyword(s):  
Similarity Analysis ◽  
Load Curve ◽  
Market Clearing ◽  
Grey Correlation ◽  
Correlation Degree ◽  
New Energy ◽  
Matching Degree ◽  
Flexible Load ◽  
Grey Correlation Degree

With the development of new energy and flexible load, there are many kinds of power and load characters in the power grid. It’s necessary to considerate the similarity between generation output and load when it’s market clearing. First, similarity analysis of generation-load curve based on the grey correlation degree is introduced, correlation between power generation enterprises and power users can be calculated. Then, market clearing model is built. market clearing mechanism is set which considering the matching degree between generation and load. The technological process of market clearing is given. Last, the effectiveness of the proposed clearing model is verified by an example.

A Model of Weight Absolute Grey Correlation Degree for the Transformer Bushing State Assessment

2013 ◽  
Vol 345 ◽  
pp. 507-510
Author(s):  
Rong Gu ◽  
Jin Sha Yuan ◽  
Fei Lv
Keyword(s):  
Correlation Coefficients ◽  
State Assessment ◽  
The State ◽  
Condition Based Maintenance ◽  
Normal Operation ◽  
Power Equipment ◽  
Accurate Assessment ◽  
Grey Correlation ◽  
Correlation Degree ◽  
Grey Correlation Degree

Accurate assessment for the operational status of the transformer bushing is not only a prerequisite for the implementation of condition-based maintenance, but also to ensure the normal operation of the transformer and the whole power equipment conditions. In the paper, a model of weight absolute grey correlation degree was used to assess the transformer bushing state. Firstly, determined the assessment indicators and handled them. Secondly, the weights, correlation coefficients and each association were calculated. Thirdly, to compare the largest association of assessment indicators in grades as the transformer bushing final run state. Finally, give the state assessment results, according to the remark set. The result shows the model works well.

A multi-objective optimization model for sustainable supply chain network with using genetic algorithm

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Reza Ehtesham Rasi ◽  
Mehdi Sohanian
Keyword(s):  
Supply Chain ◽  
Performance Indicators ◽  
Supplier Selection ◽  
Supply Chain Network ◽  
Sustainable Supply Chain ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Sustainability Performance ◽  
Multi Objective ◽  
Sustainable Supplier Selection

Purpose The purpose of this paper is to design and optimize economic and environmental dimensions in a sustainable supply chain (SSC) network. This paper developed a mixed-integer linear programing (MILP) model to incorporate economical and environmental data for multi-objective optimization of the SSC network. Design/methodology/approach The overall objective of the present study is to use high-quality raw materials, at the same time the lowest amount of pollution emission and the highest profitability is achieved. The model in the problem is solved using two algorithms, namely, multi-objective genetic and multi-objective particle swarm. In this research, to integrate sustainable supplier selection and optimization of sustainability performance indicators in supply chain network design considering minimization of cost and time and maximization of sustainability indexes of the system. Findings The differences found between the genetic algorithms (GAs) and the MILP approaches can be explained by handling the constraints and their various logics. The solutions are contrasted with the original crisp model based on either MILP or GA, offering more robustness to the proposed approach. Practical implications The model is applied to Mega Motor company to optimize the sustainability performance of the supply chain i.e. economic (cost), social (time) and environmental (pollution of raw material). The research method has two approaches, namely, applied and mathematical modeling. Originality/value There is limited research designing and optimizing the SSC network. This study is among the first to integrate sustainable supplier selection and optimization of sustainability performance indicators in supply chain network design considering minimization of cost and time and maximization of sustainability indexes of the system.

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