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: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/9283409 |
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| _version_ | 1832551550324572160 |
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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. |
| format | Article |
| id | doaj-art-83de28b4e0bb4d6db9c45d565b18c0bf |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| 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 |