M-OPARA: A Modified Approach to the OPARA Method

Do Duc Trung; Nazlı Ersoy; Tran Van Dua; Duong Van Duc; Manh Thi Diep

doi:10.31181/dmame8120251334

Authors

Do Duc Trung Hanoi University of Industry, Cau Dien, Bac Tu Liem, Hanoi, Vietnam https://orcid.org/0000-0002-0059-2603
Nazlı Ersoy Osmaniye Korkut Ata University, Osmaniye, 80000, Türkiye https://orcid.org/0000-0002-0059-2603
Tran Van Dua Hanoi University of Industry, Cau Dien, Bac Tu Liem, Hanoi, Vietnam https://orcid.org/0000-0002-0059-2603
Duong Van Duc Hanoi University of Industry, Cau Dien, Bac Tu Liem, Hanoi, Vietnam https://orcid.org/0000-0002-0059-2603
Manh Thi Diep Hanoi University of Industry, Cau Dien, Bac Tu Liem, Hanoi, Vietnam https://orcid.org/0000-0002-0059-2603

DOI:

https://doi.org/10.31181/dmame8120251334

Keywords:

Multi-criteria decision-making; MCDM; OPARA method; M-OPARA method

Abstract

The process of selecting the optimal option among multiple conflicting criteria is a fundamental task across various disciplines and is known as multi-criteria decision-making (MCDM). This study seeks to enhance the Objective Pairwise Adjusted Ratio Analysis (OPARA) method, which is limited by its inability to rank alternatives when any criterion within the decision matrix assumes a zero value. To address this limitation, an additional transformation step is introduced, resulting in the development of the M-OPARA method. The effectiveness of the M-OPARA method has been rigorously assessed through diverse case studies, incorporating different sets of criterion weights derived from 20 scenarios, and by comparing its performance against several MCDM techniques, including OPARA, PIV, ROV, SAW, WASPAS, MARCOS, and FUCA. The findings indicate that the M-OPARA method achieves high accuracy in ranking alternatives and successfully mitigates the constraints of the OPARA method. The methodological advancements introduced by M-OPARA constitute a substantial improvement in the rank ability of alternatives within decision-making frameworks. This novel approach facilitates more precise and dependable decision-making in practice, equipping decision-makers with a more flexible and robust analytical tool

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References

[1] Hussain, S. A. I., Chandra, H., & Mandal, U. K. (2022). Comparison of Cross-Entropy Based MCDM Approach for Selection of Material in Sugar Industry. Chapters. http://doi.org/10.5772/intechopen.98242

[2] Sahoo, S. K., Goswami, S. S., & Halder, R. (2024). Supplier selection in the age of industry 4.0: a review on MCDM applications and trends. Decision Making Advances, 2(1), 32-47. https://doi.org/10.31181/dma21202420

[3] Mzougui, I., Carpitella, S., Certa, A., El Felsoufi, Z., & Izquierdo, J. (2020). Assessing supply chain risks in the automotive industry through a modified MCDM-based FMECA. Processes, 8(5), 579. https://doi.org/10.3390/pr8050579

[4] Baydaş, M., Eren, T., Stević, Ž., Starčević, V., & Parlakkaya, R. (2023). Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics. PeerJ Computer Science, 9, e1350. https://doi.org/10.7717/peerj-cs.1350

[5] Jalhoom, R. J. K., & Mahjoob, A. M. R. (2024). An MCDM approach for evaluating construction-related risks using a combined fuzzy grey DEMATEL method. Engineering, Technology & Applied Science Research, 14(2), 13572-13577. https://doi.org/10.48084/etasr.6959

[6] Rezakhani, P. (2012). Fuzzy MCDM model for risk factor selection in construction projects. Engineering Journal, 16(5), 79-94. https://doi.org/10.4186/ej.2012.16.5.79

[7] Vasant, P., & Bhattacharya, A. (2007). Sensing degree of fuzziness in MCDM model using modified flexible S-curve MF. International journal of systems science, 38(4), 279-291. https://doi.org/10.1080/00207720601117108

[8] Saghafian, S., & Hejazi, S. R. (2005). Multi-criteria group decision making using a modified fuzzy TOPSIS procedure. International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), 215-221. https://doi.org/10.1109/CIMCA.2005.1631471

[9] Adıgüzel Mercangöz, B., Yıldırım, B. F., & Kuzu Yıldırım, S. (2020). Time period based COPRAS-G method: application on the Logistics Performance Index. LogForum, 16(2), 239-250. http://doi.org/10.17270/J.LOG.2020.432

[10] Matin, A. O., & Misagh, F. (2019). A modified MCDM algorithm with cumulative entropy weights for selecting the winner of the tender. Стратегические решения и риск-менеджмент, 10(1), 47-51. http://doi.org/10.17747/2618-947X-2019-1-46-51

[11] Tsuei, H.-J., Tsai, W.-H., Pan, F.-T., & Tzeng, G.-H. (2020). Improving search engine optimization (SEO) by using hybrid modified MCDM models. Artificial Intelligence Review, 53, 1-16. https://doi.org/10.1007/s10462-018-9644-0

[12] Abdulaal, R. M., & Bafail, O. A. (2022). Two New Approaches (RAMS‐RATMI) in Multi‐Criteria Decision‐Making Tactics. Journal of mathematics, 2022(1), 6725318. https://doi.org/10.1155/2022/6725318

[13] Mehdi, K.-G., Abdolghani, R., Maghsoud, A., Zavadskas, E. K., & Antuchevičienė, J. (2024). Multi-Criteria personnel evaluation and selection using an objective pairwise adjusted ratio analysis (OPARA). Economic computation and economic cybernetics studies and research., 58(2), 23-45. http://doi.org/10.24818/18423264/58.2.24.02

[14] Roy, J., Kumar Sharma, H., Kar, S., Kazimieras Zavadskas, E., & Saparauskas, J. (2019). An extended COPRAS model for multi-criteria decision-making problems and its application in web-based hotel evaluation and selection. Economic research-Ekonomska istraživanja, 32(1), 219-253. https://doi.org/10.1080/1331677X.2018.1543054

[15] Chatterjee, P., & Chakraborty, S. (2016). A comparative analysis of VIKOR method and its variants. Decision Science Letters, 5(4), 469-486. http://dx.doi.org/10.5267/j.dsl.2016.5.004

[16] Boral, S., Howard, I., Chaturvedi, S. K., McKee, K., & Naikan, V. N. A. (2020). An integrated approach for fuzzy failure modes and effects analysis using fuzzy AHP and fuzzy MAIRCA. Engineering Failure Analysis, 108, 104195. https://doi.org/10.1016/j.engfailanal.2019.104195

[17] Simić, V., Lazarević, D., & Dobrodolac, M. (2021). Picture fuzzy WASPAS method for selecting last-mile delivery mode: a case study of Belgrade. European Transport Research Review, 13, 1-22. https://doi.org/10.1186/s12544-021-00501-6

[18] Biswas, S., & Pamucar, D. (2023). A modified EDAS model for comparison of mobile wallet service providers in India. Financial Innovation, 9(1), 41. https://doi.org/10.1186/s40854-022-00443-5

[19] Ionașcu, A. E., Goswami, S. S., Dănilă, A., Horga, M.-G., Barbu, C. A., & Şerban-Comǎnescu, A. (2024). Analyzing Primary Sector Selection for Economic Activity in Romania: An Interval-Valued Fuzzy Multi-Criteria Approach. Mathematics, 12(8), 1157. https://doi.org/10.3390/math12081157

[20] Ulutaş, A., Balo, F., Sua, L., Demir, E., Topal, A., & Jakovljević, V. (2021). A new integrated grey MCDM model: Case of warehouse location selection. Facta Universitatis, Series: Mechanical Engineering, 19(3), 515-535. http://doi.org/10.22190/FUME210424060U

[21] Adali, E. A., Öztaş, G. Z., Öztaş, T., & Tuş, A. (2022). Assessment of European cities from a smartness perspective: An integrated grey MCDM approach. Sustainable cities and society, 84, 104021. https://doi.org/10.1016/j.scs.2022.104021

[22] Ulutaş, A., & Karaköy, Ç. (2021). Evaluation of LPI values of transition economies countries with a grey MCDM model. In Handbook of research on applied AI for international business and marketing applications (pp. 499-511). IGI Global. http://doi.org/10.4018/978-1-7998-5077-9.ch024

[23] Ulutaş, A., Krstić, M., Topal, A., Agnusdei, L., Tadić, S., & Miglietta, P. P. (2024). A Novel Hybrid Gray MCDM Model for Resilient Supplier Selection Problem. Mathematics, 12(10), 1444. https://doi.org/10.3390/math12101444

[24] Biswas, T., & Chaki, S. (2022). Applications of modified simple additive weighting method in manufacturing environment. International Journal of Engineering, 35(04), 830-836. http://doi.org/10.5829/ije.2022.35.04a.23

[25] Ha, L. (2023). Selection of suitable data normalization method to combine with the CRADIS method for making multi-criteria decision. Applied Engineering Letters, 8(1), 24-35. https://doi.org/10.18485/aeletters.2023.8.1.4

[26] Wardany, R. N. (2024). A study comparative of PSI, PSI-TOPSIS, and PSI-MABAC methods in analyzing the financial performance of state-owned enterprises companies listed on the Indonesia stock exchange. Yugoslav Journal of Operations Research(00), 17-17. https://doi.org/10.2298/YJOR240115017W

[27] Ulutaş, A., Popovic, G., Radanov, P., Stanujkic, D., & Karabasevic, D. (2021). A new hybrid fuzzy PSI-PIPRECIA-CoCoSo MCDM based approach to solving the transportation company selection problem. Technological and Economic Development of Economy, 27(5), 1227-1249. https://doi.org/10.3846/tede.2021.15058

[28] Wang, J., Cai, Q., Wei, G., & Liao, N. (2022). Modified EDAS Method Based on Cumulative Prospect Theory for Multiple Attributes Group Decision Making with Interval-valued Intuitionistic Fuzzy Information. arXiv preprint arXiv:2211.02806. https://doi.org/10.48550/arXiv.2211.02806

[29] Singh, S. P., Kundu, T., Adhikari, A., & Basu, S. (2020). An integrated weighting-based modified WASPAS methodology for assessing patient satisfaction. 2020 International Conference on Decision Aid Sciences and Application (DASA), 592-596. https://doi.org/10.1109/DASA51403.2020.9317282

[30] Watanasungsuit, A., Sangkhiew, N., Inthawongse, C., & Jomtong, P. (2023). A Modified AHP for Large-scale MCDM: a Case of Power Station Construction Project Selection. Science and Technology, Asia, 28, 220-230. https://www.researchgate.net/publication/374418746

[31] Wang, H., Mahmood, T., & Ullah, K. (2023). Improved CoCoSo method based on Frank softmax aggregation operators for T-spherical fuzzy multiple attribute group decision-making. International Journal of Fuzzy Systems, 25(3), 1275-1310. https://doi.org/10.1007/s40815-022-01442-5

[32] Deveci, K., Cin, R., & Kağızman, A. (2020). A modified interval valued intuitionistic fuzzy CODAS method and its application to multi-criteria selection among renewable energy alternatives in Turkey. Applied Soft Computing, 96, 106660. https://doi.org/10.1016/j.asoc.2020.106660

[33] Anvari, A., Zulkifli, N., & Arghish, O. (2014). Application of a modified VIKOR method for decision-making problems in lean tool selection. The international journal of advanced manufacturing technology, 71, 829-841. https://doi.org/10.1007/s00170-013-5520-x

[34] Qi, J., Hu, J., & Peng, Y. (2021). Modified rough VIKOR based design concept evaluation method compatible with objective design and subjective preference factors. Applied Soft Computing, 107, 107414. https://doi.org/10.1016/j.asoc.2021.107414

[35] Shahriari, M. (2017). Soft computing based on a modified MCDM approach under intuitionistic fuzzy sets. Iranian Journal of Fuzzy Systems, 14(1), 23-41. https://doi.org/10.22111/ijfs.2017.3035

[36] Ecer, F. (2022). An extended MAIRCA method using intuitionistic fuzzy sets for coronavirus vaccine selection in the age of COVID-19. Neural Computing and Applications, 34(7), 5603-5623. https://doi.org/10.1007/s00521-021-06728-7

[37] Keshavarz-Ghorabaee, M. (2021). A simple modification to the EDAS method for two exceptional cases. BOHR International Journal of Advances in Management Research, 1(1), 36-39. https://www.preprints.org/manuscript/202104.0111

[38] Zardari, N. H., Ahmed, K., Shirazi, S. M., & Yusop, Z. B. (2015). Weighting Methods and their Effects on Multi-Criteria Decision Making Model Outcomes in Water Resources Management. https://doi.org/10.1007/978-3-319-12586-2

[39] Mufazzal, S., & Muzakkir, S. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering, 119, 427-438. https://doi.org/10.1016/j.cie.2018.03.045

[40] Madić, M., Radovanović, M., & Manić, M. (2016). Application of the ROV method for the selection of cutting fluids. Decision Science Letters, 5(2), 245-254. https://dx.doi.org/10.5267/j.dsl.2015.12.001

[41] Van Thien, N., & Son, N. H. (2024). Material Selection for PMEDM Process. International Journal of Mechanical Engineering and Robotics Research, 13(3). https://doi.org/10.18178/ijmerr.13.3.315-324

[42] Kizielewicz, B., & Bączkiewicz, A. (2021). Comparison of Fuzzy TOPSIS, Fuzzy VIKOR, Fuzzy WASPAS and Fuzzy MMOORA methods in the housing selection problem. Procedia Computer Science, 192, 4578-4591. https://doi.org/10.1016/j.procs.2021.09.236

[43] Aazagreyir, P., Appiahene, P., Appiah, O., & Boateng, S.(2024). Comparative analysis of fuzzy multi-criteria decision-making methods for quality of service-based web service selection. IAES International Journal of Artificial Intelligence, 13(2), 1408-1419. https://doi.org/ 10.11591/ijai.v13.i2.pp1408-1419