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Journal ArticleDOI

A framework for water loss management in developing countries under fuzzy environment

TLDR
The results showed that pressure management and control strategy was the most prevalent one, followed by employing advanced techniques and establishment of district metered areas, which could be attributed to the strong consensus in strengthening the best option and neglecting the worst option.
Abstract
A multi-criteria decision analysis method for water loss management is proposed.The method integrates AHP and TOPSIS methods under fuzzy environment.It is applied to a real water distribution system in a developing country.The prevalent strategies were highly connected to the local conditions. Facing water scarcity conditions water utilities cannot longer tolerate inefficiencies in their water systems. To guarantee sustainable water management one central task is reducing water losses from the supply systems. There are numerous challenges in managing water losses, manifested in a variety of options, their complexities, multiple evaluation criteria, inherent uncertainties and the conflicting objectives and interests of different stakeholders. This study demonstrates the effectiveness of multi criteria decision analysis (MCDA) approaches for decision support in this complex topic. The study covers identifying the key options among a set of options that have been proposed within a framework of strategies to reduce water losses in water distribution systems of developing countries. The proposed methodology was initiated by developing a hierarchical structure of the decision problem that consists of four levels: Overall objective, main criteria, evaluation criteria and options. Different stakeholders were engaged in the process of structuring and evaluating the decision problem. An integrated methodology that combines fuzzy set theory with Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods was then employed. This methodology has the potential to transform qualitative data into equivalent quantitative measures. Fuzzy AHP was used to create weights for main and evaluation criteria, while Fuzzy TOPSIS was used to aid the ranking of options in terms of their potential to meet the overall objective based on the evaluations and preferences of decision makers. The results showed that pressure management and control strategy was the most prevalent one, followed by employing advanced techniques and establishment of district metered areas. Their dominance was highly connected to the local and boundary conditions of the case study. The sensitivity analysis results showed that strongest and weakest options were less sensitive to changes in weights of evaluation criteria, which could be attributed to the strong consensus in strengthening the best option and neglecting the worst option. This study emphasized the successful application of MCDA in dealing with complicated issues in the context of water loss management. It is anticipated that, the integration of this developed framework in the planning policies of water utilities in developing countries can help in conducting better control over water losses.

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Citations
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Journal ArticleDOI

A review of fuzzy AHP methods for decision-making with subjective judgements

TL;DR: This paper reviews the literature published since 2008 where fuzzy AHP is applied to decision-making problems in industry, particularly the various selection problems.
Journal ArticleDOI

A bibliometric-based survey on AHP and TOPSIS techniques

TL;DR: A bibliometric based survey on AHP and TOPSIS methods has been conducted and shows increasing recognition of powerful of MCDA techniques to support strategic decisions.
Journal ArticleDOI

Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers

TL;DR: A methodology based on fuzzy analytical hierarchy process (Fuzzy AHP and fuzzy technique for order performance by similarity to ideal solution) in which fuzzy AHP is applied to get the weights of each barrier by using pairwise comparison, and fuzzy TOPSIS is applied for the final ranking of the solutions of reverse logistics implementation.
Journal ArticleDOI

Effects of normalization on the entropy-based TOPSIS method

TL;DR: It is proved that vector normalization and sum normalization will not change the DAD, whereas min-max normalization (MMN) not only will change theDAD, but also may cause the appearance of numerous zero values, finally resulting in the fact that the calculated results of IE cannot represent the diversity of raw data.
Journal ArticleDOI

Application of SAW, TOPSIS and fuzzy TOPSIS models in cultivation priority planning for maize, rapeseed and soybean crops

TL;DR: In this article, an improved method of multi-criteria decision making for evaluating areas suitable for cultivation priority planning of maize, rape and soybean crops was proposed. But the results showed that the proposed method was not suitable for maize, rice and soybeans.
References
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Book

Fuzzy sets

TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Book

Multiple Attribute Decision Making: Methods and Applications

TL;DR: In this paper, the authors present a classification of MADM methods by data type and propose a ranking method based on the degree of similarity of the MADM method to the original MADM algorithm.
Journal ArticleDOI

Biopython: freely available Python tools for computational molecular biology and bioinformatics

TL;DR: Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning.
Journal ArticleDOI

Applications of the extent analysis method on fuzzy AHP

TL;DR: The use of triangular fuzzy numbers for pairwise comprison scale of fuzzy AHP is introduced, and the use of the extent analysis method for the synthetic extent value S i of the pairwise comparison is used.