International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 10, Issue 1 (January 2023), Pages: 168-174

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 Original Research Paper

 Application of fuzzy analytical hierarchy process for choosing the best project cost estimation in the Gresik district

 Author(s): Nia Saurina 1, *, Siswoyo Siswoyo 2, Lestari Retnawati 1

 Affiliation(s):

 1Department of Informatics, Universitas Wijaya Kusuma Surabaya, Surabaya, Indonesia
 2Department of Civil Engineering, Universitas Wijaya Kusuma Surabaya, Surabaya, Indonesia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-5715-9037

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2023.01.021

 Abstract:

The purpose of this research is to make an application for cost estimation of road construction projects in the Gresik district. This project is a collaboration with civil engineering and informatics to make an application using the fuzzy analytical hierarchy process (FAHP). Many times the project manager gets bids from many contractors to complete a single project. Cost estimation is a determinant element and becomes a guide to formulating policies that can be taken primarily in determining the number of investment costs or the budget that must be allocated annually and can be made the best suggestion to the project manager which contractor can provide the greatest benefit to the project manager. There are several studies that have developed applications for cost estimation, and some have even involved experts to validate the output of the application. However, this study combines five studies as FAHP calculations and two experts to assess the results of the application. FAHP in this research has five criteria, there are drainage, earthworks, grained pavement and cement pavement, paved pavement, and structure. The FAHP method can be implemented in selecting the best project that can provide the lowest raw material purchase price and give the best profit to the project manager, which can be shown by the Application FAHP with the lowest Total Score value. This process is carried out by the admin doing pairwise comparisons with the AHP scale, transforming the pairwise comparison matrix into the TFN scale, calculating the fuzzy synthesis value (Si), the vector value (V), and the defuzzification ordinate (d'), input the project budget that has been implemented (or last year), normalization, calculating the consistency ratio and calculating the best cost estimation as a total score FAHP.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Best project, Project cost estimation, Fuzzy analytical hierarchy process

 Article History: Received 1 May 2022, Received in revised form 11 August 2022, Accepted 2 October 2022

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Saurina N, Siswoyo S, and Retnawati L (2023). Application of fuzzy analytical hierarchy process for choosing the best project cost estimation in the Gresik district. International Journal of Advanced and Applied Sciences, 10(1): 168-174

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9

 Tables

 Table 1 Table 2

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 References (25)

  1. Abbasimehr H and Tarokh M (2017). A combined approach based on fuzzy AHP and fuzzy inference system to rank reviewers in online communities. Turkish Journal of Electrical Engineering and Computer Sciences, 25(2): 862-876. https://doi.org/10.3906/elk-1505-193   [Google Scholar]
  2. Basligil H (2005). The fuzzy analytic hierarchy process for software selection problems. Journal of Engineering and Natural Sciences, 2: 24–33.   [Google Scholar]
  3. Bilgen B and Şen M (2012). Project selection through fuzzy analytic hierarchy process and a case study on six sigma implementation in an automotive industry. Production Planning and Control, 23(1): 2-25. https://doi.org/10.1080/09537287.2010.537286   [Google Scholar]
  4. Brenning A, Andrey J, and Mills B (2011). Indirect modeling of hourly meteorological time series for winter road maintenance. Environmetrics, 22(3): 398-408. https://doi.org/10.1002/env.1072   [Google Scholar]
  5. Chang DY (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3): 649-655. https://doi.org/10.1016/0377-2217(95)00300-2   [Google Scholar]
  6. Enrica M, Purba HH, and Purba A (2021). Risks leading to cost overrun in construction projects: A systematic literature review. Advance Researches in Civil Engineering, 3(1): 43-60.   [Google Scholar]
  7. Erdogan SA, Šaparauskas J, and Turskis Z (2019). A multi-criteria decision-making model to choose the best option for sustainable construction management. Sustainability, 11(8): 2239. https://doi.org/10.3390/su11082239   [Google Scholar]
  8. Fragkakis N, Marinelli M, and Lambropoulos S (2015). Preliminary cost estimate model for culverts. Procedia Engineering, 123: 153-161. https://doi.org/10.1016/j.proeng.2015.10.072   [Google Scholar]
  9. Hatefi SM and Tamošaitienė J (2018). Construction projects assessment based on the sustainable development criteria by an integrated fuzzy AHP and improved GRA model. Sustainability, 10(4): 991. https://doi.org/10.3390/su10040991   [Google Scholar]
  10. Kahraman C, Onar SC, and Oztaysi B (2015). Fuzzy multicriteria decision-making: A literature review. International Journal of Computational Intelligence Systems, 8(4): 637-666. https://doi.org/10.1080/18756891.2015.1046325   [Google Scholar]
  11. Kim BS (2011). The approximate cost estimating model for railway bridge project in the planning phase using CBR method. KSCE Journal of Civil Engineering, 15(7): 1149-1159. https://doi.org/10.1007/s12205-011-1342-2   [Google Scholar]
  12. Kubler S, Robert J, Derigent W, Voisin A, and Le Traon Y (2016). A state-of the-art survey and testbed of fuzzy AHP (FAHP) applications. Expert Systems with Applications, 65: 398-422. https://doi.org/10.1016/j.eswa.2016.08.064   [Google Scholar]
  13. Kwon TJ, Fu L, and Melles SJ (2017). Location optimization of road weather information system (RWIS) network considering the needs of winter road maintenance and the traveling public. Computer‐Aided Civil and Infrastructure Engineering, 32(1): 57-71. https://doi.org/10.1111/mice.12222   [Google Scholar]
  14. Mahamid I (2011). Early cost estimating for road construction projects using multiple regression techniques. Australasian Journal of Construction Economics and Building, 11(4): 87-101. https://doi.org/10.5130/AJCEB.v11i4.2195   [Google Scholar]
  15. Makovšek D (2014). Systematic construction risk, cost estimation mechanism and unit price movements. Transport Policy, 35: 135-145. https://doi.org/10.1016/j.tranpol.2014.04.012   [Google Scholar]
  16. Marinelli M, Dimitriou L, Fragkakis N, and Lambropoulos S (2015). Non-parametric bill-of-quantities estimation of concrete road bridges' superstructure: An artificial neural networks approach. In: Raidén AB and Aboagye-Nimo E (Eds.), Proceedings 31st annual ARCOM conference: 853-862. Association of Researchers in Construction Management, Lincoln, UK.   [Google Scholar]
  17. Masoumi S, Hadji Molana SM, Javadi M, and Azizi A (2022). Designing integrated model of decision-making-robust optimisation to manage the maintenance of inter-urban routes under uncertainty. International Journal of Pavement Engineering, 23(10): 3522-3535. https://doi.org/10.1080/10298436.2021.1904238   [Google Scholar]
  18. Nguyen LD and Tran DQ (2017). FAHP-based decision making framework for construction projects. In: Emrouznejad A and Ho W (Eds.), Fuzzy analytic hierarchy process: 327-346. Chapman and Hall/CRC, New York, USA. https://doi.org/10.1201/9781315369884-14   [Google Scholar]
  19. Roche M (2017). Road maintenance–patching a hole in mobilities‐roading research: A case study of the Long Beach road board, Canterbury, New Zealand, 1911–1938. New Zealand Geographer, 73(2): 119-128. https://doi.org/10.1111/nzg.12160   [Google Scholar]
  20. Saaty TL (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3): 234-281. https://doi.org/10.1016/0022-2496(77)90033-5   [Google Scholar]
  21. Saaty TL (1980). The analytic hierarchy process. McGraw-Hill, New York, USA. https://doi.org/10.21236/ADA214804   [Google Scholar]
  22. Sayadi AR, Hamidi JK, Monjezi M, and Najafzadeh M (2015). A preliminary cost estimation for short tunnels construction using parametric method. In: Lollino G, Manconi A, Clague J, Shan W, and Chiarle M (Eds.), Engineering geology for society and territory: 461-465. Volume 1, Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-319-09300-0_88   [Google Scholar]
  23. Shane JS, Molenaar KR, Anderson S, and Schexnayder C (2009). Construction project cost escalation factors. Journal of Management in Engineering, 25(4): 221-229. https://doi.org/10.1061/(ASCE)0742-597X(2009)25:4(221)   [Google Scholar]
  24. Vaidya OS and Kumar S (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1): 1-29. https://doi.org/10.1016/j.ejor.2004.04.028   [Google Scholar]
  25. Van Damme O, Van Geelen H, and Courange P (2016). The evaluation of road infrastructure development projects. Transportation Research Procedia, 14: 467-473. https://doi.org/10.1016/j.trpro.2016.05.099   [Google Scholar]