Grey TOPSIS Research Articles

An integrated urgency evaluation approach of relief demands for disasters based on social media data

When huge disasters strike, the afflicted areas need help from all sides. However, it is difficult for relevant departments to quickly obtain relief needs of disaster areas and accurately distribute relief materials on demand. To solve this problem, this paper proposes a relief demands urgency evaluation approach which integrates Natural Language Processing (NLP), Analytic Hierarchy Process (AHP), EWM (the entropy weight method), and the Grey Relational Technique for Order Preference by Similarity to Ideal Solution (Grey TOPSIS). First, the evaluation index system of disaster relief demand is constructed from four aspects, emergency support demands, emergency rescue demands, basic life support demands, and public infrastructure support demands. Then, the indices are assigned based on social media data and real-time reports, and the weight is assigned based on AHP and EWM. At last, the Grey TOPSIS is used to evaluate the relief demand urgency of different disaster areas. Due to the high timeliness of social media data, our approach is efficient. Taking Typhoon Lekima as an example, we evaluate the disaster relief needs of cities in Zhejiang Province, Jiangsu Province, and Shandong Province and compare the evaluation values with official post disaster statistics. Results show that the urgency of disaster relief needs calculated by our proposed method is significantly correlated with actual economic losses. Moreover, the method can identify specific disaster relief needs, so as to improve the timeliness and accuracy of emergency rescue.

Benchmarking smart manufacturing drivers using Grey TOPSIS and COPRAS-G approaches

PurposeThe objective of the study is to identify and analyse drivers of smart manufacturing using integrated grey-based approaches. The analysis facilitates industry practitioners in the identification of preference of drivers through which smart manufacturing can be implemented. These drivers are explored based on existing literature and expert opinion.Design/methodology/approachModern manufacturing firms have been adopting smart manufacturing concepts to sustain in the global competitive landscape. Smart manufacturing incorporates integrated technologies with a flexible workforce to interlink the cyber and physical world. In order to facilitate the effective deployment of smart manufacturing, key drivers need to be analysed. This article presents a study in which 25 drivers of smart manufacturing and 8 criteria are analysed. Integrated grey Technique for Order Preference by Similarity to Ideal Solution (grey TOPSIS) is applied to rank the drivers. The derived ranking is validated using “Complex Proportional Assessment – Grey” (COPRAS-G) approach.FindingsIn total, 25 drivers with 8 criteria are being considered and an integrated grey TOPSIS approach is applied. The ranking order of drivers is obtained and further sensitivity analysis is also done.Research limitations/implicationsIn the present study, 25 drivers of smart manufacturing are analysed. In the future, additional drivers could be considered.Practical implicationsThe study presented has been done with inputs from industry experts, and hence the inferences have practical relevance. Industry practitioners need to focus on these drivers in order to implement smart manufacturing in industry.Originality/valueThe analysis of drivers of smart manufacturing is the original contribution of the authors.

Application of Grey-TOPSIS approach to evaluate value chain performance of tea processing chains

Article history: Received October 25, 2015 Received in revised format: December 12, 2015 Accepted January 2, 2016 Available online January 4 2016 This study develops an effective method to measure value chain performance and rank them based on qualitative criteria and to determine the ranking order of the various forms of performance under study. This approach integrates the advantage of grey systems theory and TOPSIS to evaluate and rank value chain performance. Grey-TOPSIS approach has been applied to measure and rank the value chain performance of various firms. The results indicate that the proposed model is useful to facilitate multi-criteria decision-making (MCDM) problem under the environment of uncertainty and vagueness. The model also provides an appropriate ranking order based on the available alternatives. The Grey-TOPSIS approach that will be useful to the managers to use for solving the similar type of decision-making problems in their firms in the future has been discussed. Even though, the problem of choosing a suitable performance option is often addressed in practice and research, very few studies are available in the literature of Grey-TOPSIS decision models. Also, Grey-TOPSIS model application in the tea processing firms is non-existence hence this study is the very first to apply this model in evaluating value chain performance in the tea processing firms. Growing Science Ltd. All rights reserved. 6 © 201

A new tool for assessing sediment quality based on the Weight of Evidence approach and grey TOPSIS

Sediment is an important part of an aquatic ecosystem, so it is essential to develop an effective sediment quality assessment tool. This study aims to develop a new sediment quality assessment tool using a Weight of Evidence approach in combination with the grey TOPSIS (Technique for Order Preference by Similarity, a mathematical calculation of multi-criteria decision analysis). This tool can analyze data from chemical analyses, laboratory toxicity tests and benthic community structure analyses to generate individual results from each line of evidence, and integrate data from these three lines of evidence to obtain an overall assessment through an Excel Visual Basic for Application program. The tool can compare the relative magnitude of risks among sites and rate each site with high, moderate, or low ecological risk, thus guiding us to take pertinent measures toward polluted sediment. A case study of the sediment of Dongjiang River basin, south China, demonstrated the successful application of this tool. It proved that this assessment tool can provide a comprehensive and accurate assessment of sediment quality and efficiently discriminate risks among different sites, suggesting it is a powerful tool for environment risk assessment.

Application of Modified TOPSIS for Coach Performance Evaluation

A coach performance evaluation model based on objective statistic makes for the efficiency of coach selection. In this paper, the coach performance evaluation model takes Grey Relating TOPSIS Method which focuses on the curve trend and position into consideration thus the result is the relative closeness, namely score of each coach. And in comprehensive view of the subjective and objective factors and operational factors, AHP and Principal Component Analysis are introduced to confirm the decision matrix in TOPSIS. In addition, the Standard Deviation Standardized Method is employed to eliminate the overall level of different sports. Finally, Modified TOPSIS model is applied to evaluate American college basketball, football and hockey coaches comprehensively.

An Integrated Product Design and Selection Approach Using Grey QFD and Grey TOPSIS Methods

Quality function deployment (QFD) is a successful product design tool for analyzing customer demands. Recently, researchers have combined QFD and grey system theory for solving small size, uncertain and incomplete data problems. However, some problems, such as single grey values, competitive analysis and prototype product selections, require further investigation. To resolve these issues, this study integrates interval grey numbers, QFD and TOPSIS techniques to develop a grey product design and selection (GPDS) method. GPDS can help product developers identify important engineering characteristics, perform a competitive analysis and select the best prototype product(s) under a small size and uncertain data environment. A real-world case study (paper facial mask) is offered to explain the research process of the GPDS method. The case results indicate that GPDS helps the case company identify important engineering characteristics, understand the intended market position of a product and select the best prototype product.

Method Based on Interval-value Vague Set for Multi-sensor Object Recognition

Interval-value Vague set is used to represent the problem of multi-sensor object recognition, a new recognition method is proposed combining the grey relational theory and TOPSIS procedure. The distance between two interval-value Vague sets is defined. The model of multi-sensor object recognition is constructed based on interval-values Vague set. The method uses the multiple objective s programming to determine objectively the weight vector of sensors. The recognition result is given by the closeness coefficient of each target. The experimental result shows that the distinguish ability of target recognition for the method increases greatly.