An Enhanced Interval Rough Numbers-Based Outranking Technique for Evaluation of Technology for Sustainable Mining

Akram, Muhammad; Ilyasa, Farwa

doi:10.22111/ijfs.2025.49980.8832

Document Type : Research Paper

Authors

Muhammad Akram ¹
Farwa Ilyasa ²

¹ Department of Mathematics, University of the Punjab, Lahore, Pakistan

² Department of Mathematics, University of the Punjab, New Campus, Lahore 54590, Pakistan

https://doi.org/10.22111/ijfs.2025.49980.8832

Abstract

This study pioneers an innovative decision-making framework, integrating interval rough numbers with the elimination and choice translating reality method, enhanced by the level-based weight assessment approach, specifically designed to assess sustainable mining technologies. The proposed model overcomes the limitations of traditional multi-criteria decision-making methods by effectively addressing uncertainty and imprecision through interval rough numbers. In contrast to the conventional versions of outranking methods, which proceed upon the principles of concordance and discordance sets, the proposed approach has the additional potential to
meticulously handle the pseudo criteria to facilitate the proper examination of the outranking relations. By closely evaluating each criterion’s explicit behavior in reference to the veto, preference, and indifference threshold values, the suggested technique has the advantage of being able to infer preferences in terms of strong and weak preference relations. The level-based weight assessment technique ensures a systematic and balanced determination of criteria weights, reflecting their relative importance. A comprehensive case study on sustainable mining technology evaluation demonstrates the efficacy and robustness of our approach, yielding more precise
and consistent decision outcomes. A comparison study with existing techniques is carried out to elucidate the validity, rationality, superiority, and effectiveness of the results.

Keywords

Main Subjects

Decision Making

References

[1] Z. Akram, U. Ahmad, J. C. R. Alcantud, Multi-criteria decision-making for the selection of best airport ground
access mode with a new fuzzy rough-entropy based method, Engineering Applications of Artificial Intelligence, 135
(2024), 108843. https://doi.org/10.1016/j.engappai.2024.108843

[2] M. Akram, S. Azam, M. M. A. Al-Shamiri, D. Pamucar, An outranking method for selecting the best gate security
system using spherical fuzzy rough numbers, Engineering Applications of Artificial Intelligence, 138 (2024), 109411.https://doi.org/10.1016/j.engappai.2024.109411

[3] M. Akram, F. Ilyas, M. Deveci, Interval rough integrated SWARA-ELECTRE model: An application to machine
tool remanufacturing, Expert Systems with Applications, 238 (2024), 122067. https://doi.org/10.1016/j.eswa.
2023.122067

[4] R. Benayoun, B. Roy, B. Sussman, ELECTRE: Une m´ethode pour guider le choix en pr´esence de points de vue
multiples, Note de Travail 49, Sema-Metra International, Direction Scientifique, 1966.

[5] M. Deveci, E. ¨ Ozcan, R. John, D. Pamucar, H. Karaman, Offshore wind farm site selection using interval rough
numbers based Best-Worst Method and MARCOS, Applied Soft Computing, 109 (2021), 107532. https://doi.
org/10.1016/j.asoc.2021.107532

[6] F. Ecer, D. Pamucar, A. Mardani, M. Alrasheedi, Assessment of renewable energy resources using new interval
rough number extension of the level based weight assessment and combinative distance-based assessment, Renewable
Energy, 170 (2021), 1156-1177. https://doi.org/10.1016/j.renene.2021.02.004

[7] J. R. Figueira, V. Mousseau, B. Roy, ELECTRE methods, In: S. Greco, M. Ehrgott, J. Figueira, (eds) Multiple
criteria decision analysis, International Series in Operations Research and Management Science, 233 (2016), 155-185.
https://doi.org/10.1007/0-387-23081-5_4

[8] S. Ghosh, S. Chakraborty, S. Chakraborty, An integrated IRN-SWARA-MABAC-based approach for evaluation
of tourism websites of the Indian states, OPSEARCH, 59 (2022), 974-1017. https://doi.org/10.1007/
s12597-022-00583-3

[9] J. Grolleau, J. Tergny, Manuel de r´eference du programme ELECTRE II, Document de Travail 24, Sema-Metra
International, Direction Scientifique, 1971.

[10] M. Iordache, D. Pamucar, M. Deveci, D. Chisalita, Q. Wu, I. Iordache, Prioritizing the alternatives of the natural
gas grid conversion to hydrogen using a hybrid interval rough based Dombi MARCOS model, International Journal
of Hydrogen Energy, 47(19) (2022), 10665-10688. https://doi.org/10.1016/j.ijhydene.2022.01.130

[11] H. F. Li, J. J. Wang, An improved ranking method for ELECTRE III, International Conference on Wireless
Communications, Networking and Mobile Computing, (2007), 6659-6662. doi:10.1109/WICOM.2007.1634

[12] J. C. L. Lopez, E. Solares, J. R. Figueira, An evolutionary approach for inferring the model parameters of the
hierarchical ELECTRE III method, Information Sciences, 607 (2022), 705-726. https://doi.org/10.1016/j.ins.
2022.06.014

[13] D. Mardanya, G. Maity, S. K. Roy, V. F. Yu, Solving the multi-modal transportation problem via the rough interval
approach, RAIRO Operations Research, 56(4) (2022), 3155-3185. https://doi.org/10.1051/ro/2022131

[14] R. E. Moore, Interval analysis, Englewood Cliffs, New Jersey: Prentice-Hall, 1966.

[15] D. Pamucar, M. Deveci, I. Gokasar, P. R. Brito-Parada, L. Martinez, Evaluation of process technologies for sustainable
mining using interval rough number based Heronian and power averaging functions, Knowledge-Based Systems,
289 (2024), 111494. https://doi.org/10.1016/j.knosys.2024.111494

[16] D. Pamucar, I. Gokasar, A. E. Torkayesh, M. Deveci, L. Martinez, Q.Wu, Prioritization of unmanned aerial vehicles
in transportation systems using the integrated stratified fuzzy rough decision-making approach with the Hamacher
operator, Information Sciences, 622 (2023), 374-404. https://doi.org/10.1016/j.ins.2022.11.143

[17] D. Pamucar, M. Mihajlovi´c, R. Obradovi´c, P. Atanaskovi´c, Novel approach to group multi-criteria decision making
based on interval rough numbers: Hybrid DEMATEL-ANP-MAIRCA model, Expert Systems with Applications, 88
(2017), 58-80. https://doi.org/10.1016/j.eswa.2017.06.037

[18] D. Pamucar, ˇZ. Stevi´c, E. K. Zavadskas, Integration of interval rough AHP and interval rough MABAC methods
for evaluating university web pages, Applied Soft Computing, 67 (2018), 141-163. https://doi.org/10.1016/j.
asoc.2018.02.057

[19] Z. Pawlak, Rough sets, International Journal of Computer and Information Sciences, 11(5) (1982), 341-356. http:
//dx.doi.org/10.1007/BF01001956

[20] M. Rasoulzadeh, S. A. Edalatpanah, M. Fallah, S. E. Najafi, A hybrid model for choosing the optimal stock
portfolio under intuitionistic fuzzy sets, Iranian Journal of Fuzzy Systems, 21(2) (2024), 161-179. 10.22111/ijfs.
2024.45118.7968

[21] B. Roy, ELECTRE III: Un algorithme de classements fond´e sur une repr´esentation floue des pr´ef´erences en
pr´esence de crit`eres multiples, Cahiers du Centre D’´etudes de Recherche Operationnelle, 20(1) (1978), 3-24.

[22] B. Roy, The outranking approach and the foundation of ELECTRE methods, Theory Decision, 31 (1991), 49-73.
https://doi.org/10.1007/BF00134132

[23] B. Roy, J. R. Figueira, J. Almeida-Dias, Discriminating thresholds as a tool to cope with imperfect knowledge in
multiple criteria decision aiding: Theoretical results and practical issues, Omega, 43 (2014), 9-20. https://doi.
org/10.1016/j.omega.2013.05.003

[24] M. Sarwar, Decision making model for design concept evaluation based on interval rough integrated cloud VIKOR,
Journal of Ambient Intelligence and Humanized Computing, 14 (2023), 3875-3897. https://doi.org/10.1007/
s12652-022-04459-8

[25] M. Sarwar, G. Ali, S. Shahzadi, L. Xiao, Dual interval rough integrated cloud COPRAS method: A novel hybrid
assessment model for remanufacturing system selection, Soft Computing, (2023). https://doi.org/10.1007/
s00500-023-09327-x

[26] ˇZ. Stevi´c, E. Durmi´c, M. Gaji´c, D. Pamucar, A. Pu´ska, A novel multi-criteria decision-making model: Interval
rough SAW method for sustainable supplier selection, Information, 14(7) (2023), 383. https://doi.org/10.3390/
info10100292

[27] M. Sultan, M. Akram, An extended multi-criteria decision-making technique for hydrogen and fuel cell supplier
selection by using spherical fuzzy rough numbers, Journal of Applied Mathematics and Computing, (2024). https:
//doi.org/10.1007/s12190-024-02298-8

[28] E. Takeda, A method for multiple pseudo-criteria decision problems, Computers and Operations Research, 28(14)
(2001), 1427-1439. https://doi.org/10.1016/S0305-0548(00)00050-2

[29] M. Yazdani, P. Chatterjee, D. Pamucar, S. Chakraborty, Development of an integrated decision making model for
location selection of logistics centers in the Spanish autonomous communities, Expert System with Applications,
148 (2020), 113208. https://doi.org/10.1016/j.eswa.2020.113208

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

[31] M. ˇZiˇzovi´c, D. Pamucar, New model for determining criteria weights: Level based weight assessment (LBWA)
model, Decision Making: Applications in Management and Engineering, 2(2) (2019), 126-137. https://doi.org/
10.31181/dmame1902102z

An Enhanced Interval Rough Numbers-Based Outranking Technique for Evaluation of Technology for Sustainable Mining

References

References

Volume 22, Issue 1January and February 2025Pages 111-130

Volume 22, Issue 1
January and February 2025
Pages 111-130