Fuzzy decision-making model for process quality improvement of machine tool industry chain

2021 ◽  
pp. 1-11
Author(s):  
Kuen-Suan Chen ◽  
Chun-Min Yu
Keyword(s):  
Decision Making ◽  
Quality Improvement ◽  
Machine Tool ◽  
Process Quality ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Machine Tool Industry ◽  
Industry Chain ◽  
The World ◽  
Decision Making Model

Industry 4.0 has fostered innovation in industries around the world. Manufacturing industries in particular are advancing towards smart manufacturing by integrating and applying relevant technologies. The output value of machine tools in Taiwan is among the top of the world and the central region is a key area for this industry chain, which supplies manufacturers in Taiwan and their international downstream customers. To support innovation in this industry, the current study used the Six Sigma quality indices for smaller-the-better, larger-the-better, and nominal-the-best quality characteristics to construct a fuzzy decision-making model. Based on this model, we propose a process quality fuzzy analysis chart (PQFAC) for process quality improvement. Our use of fuzzy decision values to replace lower confidence limits decreases the probability of misjudgment made by sampling errors. The proposed fuzzy model also offers a more accurate assessment of process improvement requirements. We provide a real-world example to demonstrate the applicability of the proposed approach. Machine tool manufacturers can apply the platform and proposed model to evaluate their process capabilities for the vital parts suppliers and downstream customers, determine optimal machine parameter settings for processes with inadequate accuracy or precision, establish more suitable machine repair and maintenance systems, and combine the improvement experiences of customers to create an improvement knowledge base. This will enhance product value and industry competitiveness for the entire machine tool industry chain.

Grey assessment and prediction of the financial agglomeration degree in central five cities

2014 ◽  
Vol 4 (1) ◽  
pp. 95-103 ◽  
Author(s):  
Li Li ◽  
Guo-hui Hu
Keyword(s):  
Decision Making ◽  
Scientific Development ◽  
Fuzzy Decision Making ◽  
Grey Prediction ◽  
Fuzzy Decision ◽  
Content Type ◽  
Multiple Dimension ◽  
The Government ◽  
Development Tendency ◽  
Decision Making Model

Purpose – At present, financial agglomeration tendency in domestic and foreign countries is increasingly evident. Therefore, from a comparative perspective, this paper aims to assess and predict the financial agglomeration degree in central five cities. Design/methodology/approach – According to the diversity of evaluating indexes and the uncertainty of financial agglomeration, this paper constructs a set of indexes of evaluating the financial agglomeration degree, comprehensively evaluates the financial agglomeration degree of the five cities – Wuhan, Changsha, Zhengzhou, Nanchang and Hefei – in China's middle region from 2001 to 2010 by using the multiple dimension grey fuzzy decision-making model, and predicts their development tendency by using the GM (1, 1, β) model. Findings – The results show that the multiple dimension grey fuzzy decision-making pattern cannot only be used to determine the weights of evaluating indexes, but also get the fuzzy partition and ranking order of the financial agglomeration in central five cities. The grey prediction results can objectively reflect the development tendency of the financial agglomeration in central five cities. Practical implications – From the results, it is necessary for any competitive city to clarify their relative strengths and weaknesses in order for the accurate location and scientific development, and it also provides a reference for the government decision-making. Originality/value – The paper succeeds in using the multiple dimension grey fuzzy decision-making model to measure the financial agglomeration degree of the five central cities and the grey prediction model to predict future trends.

A Class of Level-2 Fuzzy Decision-Making Model with Expected Objectives and Chance Constraints: Application to Supply Chain Network Design

2015 ◽  
Vol 16 (04) ◽  
pp. 907-938 ◽  
Author(s):  
Xiaoyang Zhou ◽  
Yan Tu ◽  
Jing Han ◽  
Jiuping Xu ◽  
Xionghui Ye
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Network Design ◽  
General Level ◽  
Fuzzy Decision Making ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Fuzzy Decision ◽  
Decision Making Model ◽  
Level 2

In this paper, we concentrate on dealing with a class of decision-making problems with level-2 fuzzy coefficients. We first discuss how to transform a level-2 fuzzy decision-making model with expected objectives and chance constrained into crisp equivalent models, then an interactive fuzzy satisfying method is introduced to obtain the decision makers satisfying solution. In addition, the technique of level-2 simulations is applied to deal with general level-2 fuzzy models which are usually hard to be converted into their crisp equivalents. Furthermore, based on the level-2 fuzzy programming, we focus on the supply chain network design problem where the total transport costs and the customer demands are assumed to be level-2 fuzzy numbers, a hybrid intelligent algorithm based on GA is used to solve the general supply chain design model. Finally, a numerical example and a case study are presented to illustrate the effectiveness of the model and the algorithm.

scholarly journals A Multi-criteria Decision-making Model for Evaluating Suppliers in Green SCM

2018 ◽  
Vol 13 (3) ◽  
pp. 337-352 ◽  
Author(s):  
Wen Jiang ◽  
Chan Huang
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Weighted Averaging ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Environment Impact ◽  
Recycle Economy ◽  
Fuzzy Decision Model ◽  
Green Suppliers ◽  
Decision Making Model

In order to develop recycle economy and friendly saving environment, many business enterprises have deployed green supply chain management (GSCM) practices. By employing related theorise of GSCM, organizations expect to minimize the environment impact caused by their commercial and industrial activities in supply chain. Different suppliers may provide different GSCM practices, so evaluating their GSCM performance to rank the green suppliers is an important aspect in practice. In this paper, a novel decision method named fuzzy generalized regret decision-making method is proposed. The fuzzy generalized regret decision-making method is based on ordered weighted averaging (OWA) operator, which is used to effectively aggregate individual regrets related to all stats of nature for an alternative under fuzzy decision-making environment. By combing the proposed method with the application background of GSCM practices, a novel fuzzy decision model for evaluating GSCM performance is further proposed. In the proposed model, the regret of decision maker is taken into consideration with an aim of minimizing the dissatisfaction when choosing the best green supplier. Individual regrets related to all criteria for a green supplier are aggregated to obtain effective regret. Finally, the green suppliers can be ranked according to the effective regrets. A numerical example is used to illustrate the effectiveness of the proposed method.

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