A Novel Approach to Multi-criteria Route Selection Problem Based on Fuzzy AHP and Amoeba Algorithm

2013 ◽  
Vol 10 (16) ◽  
pp. 5217-5224
Author(s):  
Chuan Cui
2021 ◽  
pp. 1-11
Author(s):  
Aysu Melis Buyuk ◽  
Gul T. Temur

In line with the increase in consciousness on sustainability in today’s global world, great emphasis has been attached to food waste management. Food waste is a complex issue to manage due to uncertainties on quality, quantity, location, and time of wastes, and it involves different decisions at many stages from seed to post-consumption. These ambiguities re-quire that some decisions should be handled in a linguistic and ambiguous environment. That forces researchers to benefit from fuzzy sets mostly utilized to deal with subjectivity that causes uncertainty. In this study, as a novel approach, the spherical fuzzy analytic hierarchy process (SFAHP) was used to select the best food treatment option. In the model, four main criteria (infrastructural, governmental, economic, and environmental) and their thirteen sub-criteria are considered. A real case is conducted to show how the proposed model can be used to assess four food waste treatment options (composting, anaerobic digestion, landfilling, and incineration). Also, a sensitivity analysis is generated to check whether the evaluations on the main criteria can change the results or not. The proposed model aims to create a subsidiary tool for decision makers in relevant companies and institutions.


2009 ◽  
Vol 9 (2) ◽  
pp. 641-646 ◽  
Author(s):  
Zülal Güngör ◽  
Gürkan Serhadlıoğlu ◽  
Saadettin Erhan Kesen

2021 ◽  
Vol 18 (1) ◽  
pp. 34-57
Author(s):  
Weifeng Pan ◽  
Xinxin Xu ◽  
Hua Ming ◽  
Carl K. Chang

Mashup technology has become a promising way to develop and deliver applications on the web. Automatically organizing Mashups into functionally similar clusters helps improve the performance of Mashup discovery. Although there are many approaches aiming to cluster Mashups, they solely focus on utilizing semantic similarities to guide the Mashup clustering process and are unable to utilize both the structural and semantic information in Mashup profiles. In this paper, a novel approach to cluster Mashups into groups is proposed, which integrates structural similarity and semantic similarity using fuzzy AHP (fuzzy analytic hierarchy process). The structural similarity is computed from usage histories between Mashups and Web APIs using SimRank algorithm. The semantic similarity is computed from the descriptions and tags of Mashups using LDA (latent dirichlet allocation). A clustering algorithm based on the genetic algorithm is employed to cluster Mashups. Comprehensive experiments are performed on a real data set collected from ProgrammableWeb. The results show the effectiveness of the approach when compared with two kinds of conventional approaches.


2019 ◽  
Vol 32 (5) ◽  
pp. 1039-1057
Author(s):  
Kunal Ganguly

Purpose The purpose of this paper is to present a comprehensive framework for quality-related performance measures linked to supply chain risk (SCR) by analyzing and framing them into a hierarchical structure. Design/methodology/approach In this paper, quality-related performance measures (QM) are identified on the basis of literature survey and expert opinion. The quality measures are formulated as hierarchy structure and fuzzy AHP as a multi attribute decision-making tool is applied to judge the viable candidates. Findings Based on a fuzzy AHP approach, a revised risk matrix with a continuous scale was proposed to assess the QMs’ classes. The result classifies the QMs in different categories (extreme, high, medium and low). Based on this result, some management implications and suggestions are proposed. Originality/value The present work proposes an assessment methodology for quality-related performance measures linked to SCR. The revised risk matrix with continuous scale for risk assessment in this field is a novel approach. This study contributes to the supply chain management and quality management literature, and provides suggestions for managers to adopt different strategies for different risk classes.


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