GAUSSIAN FUZZY ANALYTIC HIERARCHY PROCESS FOR THE EVALUATION OF BENEFIT, COST, AND RISK ANALYSIS IN THE INDONESIAN OIL AND GAS PROCESSING AREA

Authors

  • Fermi Dwi Wicaksono Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka, 75300 Malaysia Management of Technology Department, Faculty of Business and Management of Technology, Sepuluh Nopember Institute of Technology, 60264 Indonesia http://orcid.org/0000-0001-8907-2665
  • Yusri Arshad Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka, 75300 Malaysia http://orcid.org/0000-0002-0473-033X
  • Haeryip Sihombing Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka, 75300 Malaysia http://orcid.org/0000-0001-9832-9338
  • Imam Baihaqi Department of Business and Management, Sepuluh Nopember Institute of Technology, 60111 Indonesia http://orcid.org/0000-0002-8769-2016

DOI:

https://doi.org/10.11113/jt.v81.13623

Keywords:

Gas detector technology, Delphi technique, Gaussian fuzzy analytic hierarchy process, Sensitivity analysis

Abstract

In the oil and gas processing area, adequate risk control should be prearranged to prevent serious accidents, such as major gas leak, fire, and explosion. Installing gas detectors at appropriate technology is one of indispensable conditions for an implementation of risk reduction measures. Open-path infrared and ultrasonic leak gas detector provide wide coverage of gas detection. This capability is advantageous to detect unintentionally gas release in wide coverage and windy-climate processing area. On the other hand, the installation and maintenance cost of those gas detectors are relatively high compared to point infrared and catalytic gas detector. Gaussian fuzzy analytic hierarchy process (Gaussian FAHP) is implemented to evaluate the selection of gas detector technology in terms of benefit, cost and risk criteria. Ten expert panelists from production, safety, and maintenance departments are involved in Delphi Technique to assess the sub-criteria of Gaussian FAHP. The Gaussian FAHP evaluation reveals that point infrared gas detector has the highest value among all gas detector technologies. This means that point infrared technology acquires efficient value in delivering service to process safety operation. The obtained result of consistency ratio is always below 0.1 (CR<0.1). By these terms, the Gaussian FAHP is consistent and applicable. By changing 50% amount of weight in all sub-criteria, there is no alternatives rank position change. The sensitivity analysis proves that the Gaussian FAHP evaluation in the research is consistent irrespectively of the sub-criteria change. The integration of Delphi Technique and Gaussian FAHP in the research is the scientific work to evaluate the best applied detector technology in the oil and gas processing area.

Author Biographies

  • Fermi Dwi Wicaksono, Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka, 75300 Malaysia Management of Technology Department, Faculty of Business and Management of Technology, Sepuluh Nopember Institute of Technology, 60264 Indonesia

    Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka

    Department of Business and Management, Sepuluh Nopember Institute of Technology

  • Yusri Arshad, Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka, 75300 Malaysia
    Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka
  • Haeryip Sihombing, Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka, 75300 Malaysia
    Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka
  • Imam Baihaqi, Department of Business and Management, Sepuluh Nopember Institute of Technology, 60111 Indonesia
    Department of Business and Management, Sepuluh Nopember Institute of Technology

References

Mannan, S. 2012. Lee's Loss Prevention in the Process Industries. Fourth Edition. Elsevier, USA.

DOI: https://doi.org/10.1016/B978-0-12-397189-0.00001-X.

Legg, S. W. 2013. Stochastic Programming Approaches for the Placement of Gas Detectors in Process Facilities. (August). Available at: http://oaktrust.library.tamu.edu/handle/1969.1/150991.

Dalkey, N., and Helmer, O. 1963. An Experimental Application of the Delphi Method to the Use of Experts. United States Air Force Project RAND, Copyright 1963 by INFORMS. Santa Monica. California. 458-467.

DOI: http://doi.org/10.1287/mnsc.9.3.458.

Hwang, C. -L., and Lin, M. -J. 2012. Group Decision Making Under Multiple Criteria: Methods and Applications. Vol. 281. Springer, USA.

Zangenehmadar, Z., and Moselhi, O. 2016. Prioritizing Deterioration Factors of Water Pipelines using Delphi Method. Measurement: Journal of the International Measurement Confederation. 90: 491-499.

DOI: https://doi.org/10.1016/j.measurement.2016.05.001.

Reza, M., Yilmaz, O., Reza, Y., Mohammad, A., and Seyed, M. H. 2013. Ranking the Sawability of Ornamental Stone Using Fuzzy Delphi and Multi-criteria Decision-making Techniques. International Journal of Rock Mechanics and Mining Sciences. 58: 118-126.

DOI: https://doi.org/10.1016/j.ijrmms.2012.09.002.

Ouyang, X. and Guo, F. 2018. Intuitionistic Fuzzy Analytical Hierarchical Processes for Selecting the Paradigms of Mangroves in Municipal Wastewater Treatment. Chemosphere. 197: 634-642.

DOI: https://doi.org/10.1016/j.chemosphere.2017.12.102.

Qiu, M., Shi, L., Teng, C., and Zhou, T. 2017. Assessment of Water Inrush Risk Using the Fuzzy Delphi Analytic Hierarchy Process and Grey Relational Analysis in the Liangzhuang Coal Mine, China. Mine Water and the Environment. 36(1): 39-50. DOI: https://doi.org/10.1007/s10230-016-0391-7.

Wu, H. -Y., Wu, H. -S., Chen, I. -S, Chen, H. -C. 2014. Exploring The Critical Influential Factors of Creativity For College Students: A Multiple Criteria Decision-making Approach. Thinking Skills and Creativity. 11(2014): 1-21.

DOI: https://doi.org/10.1016/j.tsc.2013.09.004.

Zhu, K. 2014. Fuzzy Analytic Hierarchy Process: Fallacy of the Popular Methods. European Journal of Operational Research. 236: 209-217

DOI: https://doi.org/10.1016/j.ejor.2013.10.034.

Sirisawat, P., and Kiatcharoenpol, T. 2018. Fuzzy AHP-TOPSIS Approaches to Prioritizing Solutions for Reverse Logistics Barriers. Computers & Industrial Engineering. 117: 303-318. DOI: https://doi.org/10.1016/j.cie.2018.01.015.

Ho, W., and Ma, X. 2018. The State-of-the-art Integrtions and Applications of the Analytic Hierarchy Process. European Journal of Operational Research. 267: 399-414

DOI : https://doi.org/10.1016/j.ejor.2017.09.007.

Shapiro, A. F., and Koissi, M. -C. 2017. Fuzzy Logic Modifications of the Analytic Hierarchy Process. Insurance: Mathematics and Economics. 75: 189-202

DOI: https://doi.org/10.1016/j.insmatheco.2017.05.003.

Mardani, A., Jusoh, A., and Zavadskas, E. K. 2015. Fuzzy Multiple Criteria Decision-making Techniques and Applications – Two Decades Review From 1994 to 2014. Expert Systems with Applications. 42: 4126-4148

DOI: https://doi.org/10.1016/j.eswa.2015.01.003.

Kaya, I,. Çolak, M., and Terzi, F. 2019. A Comprehensive Review of Fuzzy Multi Criteria Decision Making Methodologies for Energy Policy Making. Energy Strategy Reviews. 24: 207-228.

DOI: https://doi.org/10.1016/j.esr.2019.03.003.

Xu, Z. 2014. Intuitionistic Fuzzy Analytic Hierarchy Process. IEEE Transcations on Fuzzy Systems. 22(4): 749-761

DOI: 10.1109/TFUZZ.2013.2272585

Hefny, H. A., Elsayed, H. M., and Aly, H. F. 2013. Fuzzy Multi-criteria Decision Making Model for Different Scenarios of Electrical Power Generation in Egypt. Egyptian Informatics Journal. 14(2): 125-133

DOI: http://dx.doi.org/10.1016/j.eij.2013.04.001.

Sahin, B., and Yip, T. S. 2017. SHipping Technology Selection for Dynamic Capability Based on improved Gaussian Fuzzy AHP Model. Ocean Engineering. 136: 233-242. DOI : https://doi.org/10.1016/j.oceaneng.2017.03.032.

Legg, S. W., Wang, C., Benavides-Serrano, A. J., and Laird, C. D. 2012. Optimal Gas Detector Placement under Uncertainty Considering Conditional-Value-at-Risk. Journal of Loss Prevention in the Process Industries. 26(3): 410-417.

DOI: https://doi.org/10.1016/j.jlp.2012.06.006.

Naranjo, E., and Baliga, S. 2012. Early Detection of Combustible Gas Leaks Using Open-path Infrared (IR) Gas Detectors. Proceedings of SPIE, 8366 (Advanced Environmental. Chemical, and Biological Sensing Technologies (IX). 83660V/1-83660V/6.

Naranjo, E., and Baligha, S. 2009. Expanding the Use of Ultrasonic Gas Leak Detectors: A Review of Gas Release Characteristics for Adequate Detection. Gases and Instrumentation. (November/December): 24-29.

Sizeland, E. 2014. Ultrasonic Devices Improve Gas Leak Detection in Challenging Environments, World Oil. (October): 133-135.

Black, N., Murphy, M., Lamping, D., McKee, M., Sanderson, C., Ashkam, J., and Marteau, T. 1999. Consensus Development Methods: A Review of Best Practice in Creating Clinical Guidelines. Social Science Citation Index Journal of Health Services Research & Policy. 4(4): 236-248.

DOI: http://doi.org/10.1177/135581969900400410.

Zadeh, L. A. 1965. Fuzzy Sets. Information and Control. 8(3): 338-353. DOI: https://doi.org/10.1016/S0019-9958(65)90241-X.

Zimmermann, H. J. 2001. Fuzzy Set Theory - and Its Applications. Fourth Edition. New York: Springer Science+Business Media, LLC.

Laarhoven, P. J. M., and Pedrycz, W. 1983. A Fuzzy Extension of Saaty's Priority Theory. Fuzzy Sets, and Systems. 11(1-3): 229-241.

DOI: http://doi.org/10.1016/S0165-0114(83)80082-7.

Zimmermann, H. -J. 2012. Fuzzy Sets, Decision Making, and Expert Systems. Vol. 10. Springer Science & Business Media, USA.

Sadi-Nezhad, S., and Damghani, K. K. 2010. Application of a Fuzzy TOPSIS Method Based on Modified Preference Ratio and Fuzzy Distance Measurement in Assessment of Traffic Police Centers Performance. Applied Soft Computing. 10: 1028-1039.

DOI: http://doi.org/10.1016/j.asoc.2009.08.036.

Zhu, K. 2014. Fuzzy Analytic Hierarchy Process: Fallacy of the Popular Methods. European Journal of Operational Research. 236(1): 209-217.

DOI: https://doi.org/10.1016/j.ejor.2013.10.034.

Jing, L., Chen, B., Zhang, B., & Peng, H. 2013. A Hybrid Fuzzy Stochastic Analytical Hierarchy Process (FSAHP) Approach for Evaluating Ballast Water Treatment Technologies. Environmental Systems Research. 2(10): 1-10.

DOI: https://doi.org/10.1186/2193-2697-2-10.

Zheng, G., Zhu, N., Tian, Z., Chen, Y., and Sun, B. 2011. Application of a Trapezoidal Fuzzy AHP Method for Work Safety Evaluation and Early Warning Rating of Hot and Humid Environments. Safety Science. 50(2): 228-239.

DOI: https://doi.org/10.1016/j.ssci.2011.08.042.

Downloads

Published

2019-09-22

Issue

Vol. 81 No. 6: November 2019

Section

Science and Engineering

License

Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.

How to Cite

GAUSSIAN FUZZY ANALYTIC HIERARCHY PROCESS FOR THE EVALUATION OF BENEFIT, COST, AND RISK ANALYSIS IN THE INDONESIAN OIL AND GAS PROCESSING AREA. (2019). Jurnal Teknologi (Sciences & Engineering), 81(6). https://doi.org/10.11113/jt.v81.13623