Norm-dist Monte-Carlo integrative method for the improvement of fuzzy analytic hierarchy process

Fermi Dwi Wicaksono; Yusri Bin Arshad; Haeryip Sihombing

doi:10.1016/j.heliyon.2020.e03607

Norm-dist Monte-Carlo integrative method for the improvement of fuzzy analytic hierarchy process

Heliyon. 2020 Apr 22;6(4):e03607. doi: 10.1016/j.heliyon.2020.e03607. eCollection 2020 Apr.

Authors

Fermi Dwi Wicaksono¹, Yusri Bin Arshad^{1

2}, Haeryip Sihombing¹

Affiliations

¹ Fakulti Pengurusan Teknologi Dan Teknousahawan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, 76100, Malaysia.
² Center of Technopreneurship Development (C-TED), Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, 76100, Malaysia.

Abstract

This paper presents the novel approach of the Norm-dist Monte-Carlo fuzzy analytic hierarchy process (NMCFAHP) to incorporate probabilistic and epistemic uncertainty due to human's judgment vagueness in multi-criteria decision analysis. Normal distribution is applied as the most appropriate distribution model to approximate the probability distribution function of the criteria and alternatives within Monte-Carlo simulation. To test the applicability of the proposed NMCFAHP, the case study of non-destructive test (NDT) technology selection is performed in the Petroleum Company in Borneo, Indonesia. When compared with the conventional triangular fuzzy-AHP, the proposed NMCFAHP method reduces the standard error of mean values by 90.4-99.8% at the final evaluation scores. This means that the proposed NMCFAHP significantly involves fewer errors when dealing with fuzzy uncertainty and stochastic randomness. The proposed NMCFAHP delivers reliable performance to overcome probabilistic uncertainty and epistemic vagueness in the group decision making process.

Keywords: Integrative method of fuzzy analytic hierarchy process; Mathematics; Monte-carlo simulation; Multi-criteria decision making; Normal distribution fuzzy number.