Document Type : Research Paper
Authors
1 Department of Statistics, Shahid Chamran University of Ahvaz, Ahvaz 83151-61355, Iran
2 Faculty of Mathematical Sciences and Computer, Department of Mathematics, Shahid Chamran University of Ahvaz, Ahvaz, Iran
Abstract
Determining weights of criteria is a pivotal challenge that arises in Multi-Attribute Decision Making (MADM) problems. Different methods have been suggested in the literature which can be classified into three main categories: subjective, objective and integrated. Especially when the decision maker does not have a specific judgment regarding the weights of the criteria of the decision problem or the number of criteria is large, the methods based on pairwise comparisons are not effective due to the large number of required judgments as well as the natural increase in inconsistency in the judgments. In this paper, we propose an integrated model to determine criteria weights in MADM problems while combining the Ordered Weighted Average (OWA) Yager, entropy, fuzzy/crisp initial decision maker's judgments about the preferences of alternatives, and the information of decision matrix. In this regard, by considering a decision matrix, we formulate the idea as an optimization problem including an extended TOPSIS, L-p metric and $\widetilde{L-p}$ metric, or goal programming model in order to provide an extension of OWA operator and entropy method, simultaneously. Then, we use the proposed method in a real-world dataset to evaluate the priorities of mining opportunities in 17 provinces of Iran (including 235 alternatives by considering 48 criteria). In this regard, a comprehensive list of economic, political, social, strategic and environmental criteria has been used. A full analysis is performed to illustrate the application of the technique that stems from our approach. Finally, we compare the results that we obtain with the results from existing approaches, including Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Simple Additive Weighting (SAW) method and Yager. In this way the accuracy and effectiveness of the presented work is conclusively validated.
Keywords
- OWA operators
- Fuzzy preferences
- Constrained OWA optimization
- L-p metirc and Goal Programing
- Multi-attribute decision making (MADM)
Main Subjects
Reasoning, 47 (2008), 166-178. https://doi.org/10.1016/j.ijar.2007.04.001
Systems, 25 (2017), 1231-1238. https://doi.org/10.1109/TFUZZ.2016.2604006
Journal of Fuzzy Systems, 18 (2021), 107-127. https://doi.org/10.22111/IJFS.2021.6258
and practical developments and future perspectives, Pesquisa Operacional, 43 (2023), e268356. https://doi.org/
10.1590/0101-7438.2023.043spe1.00268356
fuzzy environment, Iranian Journal of Fuzzy Systems, 17 (2020), 183-199. https://doi.org/10.22111/IJFS.
2020.5228
Wiley and Sons, 2021. https://doi.org/10.1002/9781119563501
https://doi.org/10.1007/978-0-387-70914-7
International Conference on Granular Computing (GRC 2007), pages 145-145, 2007. https://doi.org/10.1007/
978-3-540-76284-3
159 (2021), 107498. https://doi.org/10.1016/j.cie.2021.107498
of Fuzzy Systems, 18 (2021), 131-148. https://doi.org/10.22111/IJFS.2021.6181
and Industrial Engineering, 105 (2017), 260-264. https://doi.org/10.1016/j.cie.2016.12.041
problems by maximum entropy membership function, Computers and Industrial Engineering, 124 (2018), 370-378.
https://doi.org/10.1016/j.cie.2018.07.040
https://doi.org/10.1016/j.dajour.2021.100021
Multi-Criteria Decision-Making Methods in Manufacturing Environments, Apple Academic Press, 2024, pages
325-335. https://doi.org/10.1201/9781003377030-30
https://doi.org/10.1007/s40314-022-02061-4
questionnaires and their statistical analysis, IEEE Transactions on Fuzzy Systems, 23 (2015), 111-126.
https://doi.org/10.1109/TFUZZ.2014.2307895
n-dimensional fuzzy sets, admissible orders and OWA operators, Information Fusion, 37 (2017), 126-131. https:
//doi.org/10.1016/j.inffus.2017.01.007
The critic method, Computers and Operations Research, 22 (1995), 763-770. https://doi.org/10.1016/
0305-0548(94)00059-H
https://doi.org/10.1016/j.ijar.2021.12.009
methods, Annals of Operations Research, In press, 2024. https://doi.org/10.1007/s10479-024-05926-5
9781003168416
135-147. Springer Berlin Heidelberg, Berlin, Heidelberg, 2011. https://doi.org/10.1007/978-3-642-17910-5_7
Journal of Information Technology and Decision Making, 10 (2011), 1067-1095. https://doi.org/10.1142/
S0219622011004713
448 (2022), 145-156. https://doi.org/10.1016/j.fss.2022.01.003
attributes in multiple attribute decision making, Applied Soft Computing, 74 (2019), 643-651. https://doi.org/
10.1016/j.asoc.2018.10.050
(2021), 107274. https://doi.org/10.1016/j.cie.2021.107274
engine/vehicle selection problem, Informatica, 34 (2023), 249-270. https://doi.org/10.15388/23-INFOR507
Technology and Decision Making, 22 (2023), 1439-1488. https://doi.org/10.1142/S0219622022500985
and gas industry, Soft Computing, 28 (2024), 281-303. https://doi.org/10.1007/s00500-023-09282-7
decision making, an empirical application in petroleum industry, Group Decision and Negotiation, 32 (2023),
1351-1390. https://doi.org/10.1007/s10726-023-09846-w
making dealing with extended comparative linguistic expressions with symbolic translation, Mathematics, 9 (2021),
20. https://doi.org/10.3390/math9010020
criteria importance through inter-criteria correlation (CRITIC) in uncertainty environment, Springer International
Publishing, Cham, 2023, pages 309-324. https://doi.org/10.1007/978-3-031-44742-6_13
20 (2020), 50-58. https://doi.org/10.2478/cait-2020-0015
weighted averaging operators, Journal of Ambient Intelligence and Humanized Computing, 36 (2021), 997-1014.
https://doi.org/10.1002/int.22328
[37] A. Kazemifard, J. Chachi, MADM approach to analyse the performance of fuzzy regression models, Journal
of Ambient Intelligence and Humanized Computing, 13 (2020), 4019-4031. https://doi.org/10.1007/
s12652-021-03394-4
weights in solving mcdm problem, International Journal of Information Technology and Decision Making, 13 (2014),
1119-1133. https://doi.org/10.1142/S0219622014500825
applications, Expert Systems with Applications, 65 (2016), 398-422. https://doi.org/10.1016/j.eswa.2016.
08.064
google.com/books?id=9rotAAAAYAAJ
//doi.org/10.1007/978-3-540-73165-8
selected methods, Computers and Industrial Engineering, 155 (2021), 107180. https://doi.org/10.1016/j.cie.
2021.107180
2024, pages 17-73. https://doi.org/10.1007/978-3-031-35378-9_2
cosmetic brands in a group decision-making environment, Granular Computing, 9 (2024), 1-37. https://doi.
org/10.1007/s41066-024-00481-7
integrated lot-sizing and cutting stock problem, European Journal of Operational Research, 295 (2021), 996-1007.
https://doi.org/10.1016/j.ejor.2021.03.049
Full consistency method (fucom), Symmetry, 10 (2018), 393. https://doi.org/10.3390/sym10090393
development, Soft Computing, 27 (2023), 18011-18039. https://doi.org/10.1007/s00500-023-09011-0
and idocriw to fuzzy mcdm, Economic Computation and Economic Cybernetics Studies and Research, 54 (2022),
59-75. https://doi.org/10.24818/18423264/54.2.20.04
The state of the art, Processes, 11 (2023), 497. https://doi.org/10.3390/pr11020497
Vienna, Austria, 2022. https://www.R-project.org
method, and pythagorean fuzzy sets, Information Sciences, 644 (2023), 119274. https://doi.org/10.1016/j.ins.
2023.119274
1: Multi-attribute decision-making applications in the industrial environment, International Journal of Industrial
Engineering Computations, 15 (2024), 13-40. https://doi.org/10.5267/j.ijiec.2023.12.003
Manufacturing Environment: Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods, 2007,
pages 27-41. https://doi.org/10.1007/978-1-84628-819-7_3
process in fitradeoff, International Transactions in Operational Research, 30 (2023), 1299-1319. https://doi.org/
10.1111/itor.12958
Series, 2017. ISBN13:9780131437470
623-656. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Research, 40 (2003), 2250024. https://doi.org/10.1142/S0217595922500245
criteria priority and weights in solving multi-attribute decision-making problems, Soft Computing, 27 (2023), 6613-
6628. https://doi.org/10.1007/s00500-022-07807-0
10.1002/int.20418
weight information, Iranian Journal of Fuzzy Systems, 16 (2019), 145-157. https://doi.org/10.22111/IJFS.
2019.4788
//doi.org/10.1007/0-387-30623-4
https://doi.org/10.1198/tas.2009.0033
2015. https://doi.org/10.1007/978-3-662-45640-8
Publishing, Cham, 2022, pages 621-658. https://doi.org/10.1007/978-3-030-96935-6_18
on Fuzzy Systems, Man and Cybernetics, 18 (1988), 183-190. https://doi.org/10.1109/21.87068
aggregations, Knowledge-Based Systems, 191 (2020), 105216. https://doi.org/10.1016/j.knosys.2019.105216
Omega, 71 (2017), 93-105. https://doi.org/10.1016/j.omega.2016.09.011
weights, Computers and Industrial Engineering, 63 (2012), 338-342. https://doi.org/10.1016/j.cie.2012.03.
010
//doi.org/10.4135/9781412985161
90241-X
uncertainty, European Journal of Operational Research, 198 (2009), 259-265. https://doi.org/10.1016/j.ejor.
2008.09.014
Journal of Information Technology and Decision Making, 15 (2016), 267-283. https://doi.org/10.1142/
S0219622016500036
multidimensional analysis of preference based on novel distance measures, Computers and Industrial Engineering,
148 (2020), 106670. https://doi.org/10.1016/j.cie.2020.106670
10.1007/978-94-010-0646-0
Decision Making: Applications in Management and Engineering, 2 (2019), 126-137. https://doi.org/10.31181/
dmame1902102z