Implementing Fuzzy TOPSIS on Project Risk Variable Ranking

Managing construction risks with a large number of risks with small impact can increase the additional effort and cost of inefficient construction. Therefore the variables need to be eliminated. The aim of this study is ranking the risk variable based on its frequency of occurrence by integrating ti...

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Main Authors: Saiful Husin, Fachrurrazi Fachrurrazi, Maimun Rizalihadi, Mubarak Mubarak
Format: Article
Language:English
Published: Wiley 2019-01-01
Series:Advances in Civil Engineering
Online Access:http://dx.doi.org/10.1155/2019/9283409
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author Saiful Husin
Fachrurrazi Fachrurrazi
Maimun Rizalihadi
Mubarak Mubarak
author_facet Saiful Husin
Fachrurrazi Fachrurrazi
Maimun Rizalihadi
Mubarak Mubarak
author_sort Saiful Husin
collection DOAJ
description Managing construction risks with a large number of risks with small impact can increase the additional effort and cost of inefficient construction. Therefore the variables need to be eliminated. The aim of this study is ranking the risk variable based on its frequency of occurrence by integrating time, cost, and quality criteria simultaneously and selecting the top ten variables with the order of the most significant impact. The risk variable ranking based on triple project objective of cost, time, and quality simultaneously is a challenge for particular projects or regions contributing to the risk context. A number of 127 qualitative risk variables of 14 factors occurring in a project to be eliminated require a method/technique. A fuzzy TOPSIS method involving linguistics data is proposed to capture vague conditions. Results show that the top ten rankings of risk variables based on integrating the different weights of cost, time, and quality are successfully identified by concluding that the labour factor is the most dominant variable affecting project risk in context the rehabilitation and reconstruction posttsunami disaster, especially in Aceh-Indonesia. The variables are lack of labour, unskilled labour, undisciplined labour, and low productivity of labour. This condition can differ from different risk contexts. This research is different from other studies that only review cost, time, and quality separately. We stated that to integrate all three criteria of cost, time, and quality simultaneously is more logic to analyze risk variable ranking.
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id doaj-art-83de28b4e0bb4d6db9c45d565b18c0bf
institution Kabale University
issn 1687-8086
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language English
publishDate 2019-01-01
publisher Wiley
record_format Article
series Advances in Civil Engineering
spelling doaj-art-83de28b4e0bb4d6db9c45d565b18c0bf2025-02-03T06:01:09ZengWileyAdvances in Civil Engineering1687-80861687-80942019-01-01201910.1155/2019/92834099283409Implementing Fuzzy TOPSIS on Project Risk Variable RankingSaiful Husin0Fachrurrazi Fachrurrazi1Maimun Rizalihadi2Mubarak Mubarak3Civil Engineering Department, Universitas Syiah Kuala, Banda Aceh, 23111 Aceh, IndonesiaCivil Engineering Department, Universitas Syiah Kuala, Banda Aceh, 23111 Aceh, IndonesiaCivil Engineering Department, Universitas Syiah Kuala, Banda Aceh, 23111 Aceh, IndonesiaCivil Engineering Department, Universitas Syiah Kuala, Banda Aceh, 23111 Aceh, IndonesiaManaging construction risks with a large number of risks with small impact can increase the additional effort and cost of inefficient construction. Therefore the variables need to be eliminated. The aim of this study is ranking the risk variable based on its frequency of occurrence by integrating time, cost, and quality criteria simultaneously and selecting the top ten variables with the order of the most significant impact. The risk variable ranking based on triple project objective of cost, time, and quality simultaneously is a challenge for particular projects or regions contributing to the risk context. A number of 127 qualitative risk variables of 14 factors occurring in a project to be eliminated require a method/technique. A fuzzy TOPSIS method involving linguistics data is proposed to capture vague conditions. Results show that the top ten rankings of risk variables based on integrating the different weights of cost, time, and quality are successfully identified by concluding that the labour factor is the most dominant variable affecting project risk in context the rehabilitation and reconstruction posttsunami disaster, especially in Aceh-Indonesia. The variables are lack of labour, unskilled labour, undisciplined labour, and low productivity of labour. This condition can differ from different risk contexts. This research is different from other studies that only review cost, time, and quality separately. We stated that to integrate all three criteria of cost, time, and quality simultaneously is more logic to analyze risk variable ranking.http://dx.doi.org/10.1155/2019/9283409
spellingShingle Saiful Husin
Fachrurrazi Fachrurrazi
Maimun Rizalihadi
Mubarak Mubarak
Implementing Fuzzy TOPSIS on Project Risk Variable Ranking
Advances in Civil Engineering
title Implementing Fuzzy TOPSIS on Project Risk Variable Ranking
title_full Implementing Fuzzy TOPSIS on Project Risk Variable Ranking
title_fullStr Implementing Fuzzy TOPSIS on Project Risk Variable Ranking
title_full_unstemmed Implementing Fuzzy TOPSIS on Project Risk Variable Ranking
title_short Implementing Fuzzy TOPSIS on Project Risk Variable Ranking
title_sort implementing fuzzy topsis on project risk variable ranking
url http://dx.doi.org/10.1155/2019/9283409
work_keys_str_mv AT saifulhusin implementingfuzzytopsisonprojectriskvariableranking
AT fachrurrazifachrurrazi implementingfuzzytopsisonprojectriskvariableranking
AT maimunrizalihadi implementingfuzzytopsisonprojectriskvariableranking
AT mubarakmubarak implementingfuzzytopsisonprojectriskvariableranking