Informatica logo


Login Register

  1. Home
  2. Issues
  3. Volume 34, Issue 2 (2023)
  4. An Improved Rank Order Centroid Method ( ...

Informatica

Information Submit your article For Referees Help ATTENTION!
  • Article info
  • Full article
  • Related articles
  • Cited by
  • More
    Article info Full article Related articles Cited by

An Improved Rank Order Centroid Method (IROC) for Criteria Weight Estimation: An Application in the Engine/Vehicle Selection Problem
Volume 34, Issue 2 (2023), pp. 249–270
Mohammad Ali Hatefi ORCID icon link to view author Mohammad Ali Hatefi details  

Authors

 
Placeholder
https://doi.org/10.15388/23-INFOR507
Pub. online: 23 January 2023      Type: Research Article      Open accessOpen Access

Received
1 July 2022
Accepted
1 January 2023
Published
23 January 2023

Abstract

The focus of this paper is on the criteria weight approximation in Multiple Criteria Decision Making (MCDM). An approximate weighting method produces the weights that are surrogates for the exact values that cannot be elicited directly from the DM. In this field, a very famous model is Rank Order Centroid (ROC). The paper shows that there is a drawback to the ROC method that could be resolved. The paper gives an idea to develop a revised version of the ROC method called Improved ROC (IROC). The behaviour of the IROC method is investigated using a set of simulation experiments. The IROC method could be employed in situations of time pressure, imprecise information, etc. The paper also proposes a methodology including the application of the IROC method in a group decision making mode, to estimate the weights of the criteria in a tree-shaped structure. The proposed methodology is useful for academics/managers/decision makers who want to deal with MCDM problem. A study case is examined to show applicability of the proposed methodology in a real-world situation. This case is engine/vehicle selection problem, that is one of the fundamental challenges of road transport sector of any country.

References

 
Abbasi, M., Hadji-Hosseinlou, M. (2022). Assessing feasibility of overnight-charging electric bus in a real-world BRT system in the context of a developing country. Scientia Iranica, 29(6), 2968–2978.
 
Ahn, B.S. (2011). Compatible weighting method with rank order centroid: maximum entropy ordered weighted averaging approach. European Journal of Operational Research, 212(3), 552–559.
 
Ahn, B.S. (2017). Approximate weighting method for multi-attribute decision problems with imprecise parameters. Omega, 72, 87–95.
 
Ahn, B.S., Park, K.S. (2008a). Comparing methods for multi attribute decision making with ordinal weights. Computers & Operations Research, 35(5), 1660–1670.
 
Ahn, B.S., Park, K.S. (2008b). Least-squared ordered weighted averaging operator weights. International Journal of Intelligent Systems, 23, 33–49.
 
Alfares H.K., Duffuaa, S.O. (2008). Assigning cardinal weights in multi-criteria decision making based on ordinal ranking. Journal of Multi-Criteria Decision Analysis, 15(5–6), 125–133.
 
Alfares H.K., Duffuaa, S.O. (2016). Simulation-based evaluation of criteria rank weighting methods in multi-criteria decision making. International Journal of Information Technology and Decision Making, 15(1), 43–61.
 
Andersson, L., Ek, K., Kastensson, A., Warell, L. (2020). Transition towards sustainable transportation – what determines fuel choice? Transport Policy, 90, 31–38.
 
Asilata, M.D., Keswani, I.P. (2015). Selection of fuel by using analytical hierarchy process. Journal of Engineering Research and Applications, 5(4), 91–94.
 
Barron, F.H. (1992). Selecting a best multi-attribute alternative with partial information about attribute weights. Acta Psychologica, 80, 91–103.
 
Barron, F., Barrett, B.E. (1996). Decision quality using ranked attribute weights. Management Science, 42(11), 1515–1523.
 
Bhan, S., Gautam, R., Singh, P. (2022). An experimental assessment of combustion, emission, and performance behavior of a diesel engine fueled with newly developed biofuel blend of two distinct waste cooking oils and metallic nano-particle (Al2O3). Scientia Iranica, 29(4), 1853–1867.
 
Bottomley, P.A., Doyle, J.R. (2001). A comparison of three weight elicitation methods: good, better, and best. Omega, 29, 553–560.
 
Cui, Y., Liu, J., Cong, B., Han, X., Yin, S. (2022). Characterization and assessment of fire evolution process of electric vehicles placed in parallel. Process Safety and Environmental Protection, 166, 524–534.
 
Danielson, M., Ekenberg, L. (2014). Rank ordering methods for multi-criteria decisions. In: Proceedings of the 14th Group Decision and Negotiation (GDN 2014), Springer.
 
Danielson, M., Ekenberg, L. (2016). A robustness study of state-of-the-art surrogate weights for MCDM. Group Decision and Negotiation, 26, 677–691.
 
Dawes, R.M., Corrigan, B. (1974). Linear models in decision making. Psychological Bulletin, 81, 91–106.
 
Diao, F., Cai, Q., Wei, G. (2022). Taxonomy method for multiple attribute group decision making under the spherical fuzzy environment. Informatica, 33(4), 713–729.
 
Doyle, J.R., Green, R.H., Bottomley, P.A. (1997). Judging relative importance: direct rating and point allocation are not equivalent. Organizational Behavior and Human Decision Processes, 70(1), 65–72.
 
Duleba, S., Kutlu Gundogdu, F., Moslem, S. (2021). Interval-valued spherical fuzzy analytic hierarchy process method to evaluate public transportation development. Informatica, 32(4), 661–686.
 
Erdogan, S., Sayin, C. (2018). Selection of the most suitable alternative fuel depending on the fuel characteristics and price by the hybrid MCDM method. Sustainability, 10, 1583.
 
Erdogan, S., Balki, M.K., Aydin, S., Sayin, C. (2019). The best fuel selection with hybrid multiple-criteria decision making approaches in a CI engine fueled with their blends and pure biodiesels produced from different sources. Renewable Energy, 134, 653–668.
 
Farkas, A. (2014). An interaction-based scenario and evaluation of alternative fuel modes of buses. Acta Polytechnica Hungarica, 11(1), 205–225.
 
Ginevičius, R. (2011). A new determining method for the criteria weights in multi-criteria evaluation. International Journal of Information Technology & Decision Making, 10(6), 1067–1095.
 
Hatefi, M.A. (2018). A multi-criteria decision analysis model on the fuels for public transport with the use of hybrid ROC-ARAS method. Petroleum Business Review, 1(2), 45–55.
 
Hatefi, M.A. (2019). Indifference threshold-based attribute ratio analysis: a method for assigning the weights to the attributes in multiple attribute decision making. Applied Soft Computing, 74, 643–651.
 
Hatefi, M.A. (2021). BRAW: block-wise rating the attribute weights in MADM. Computers & Industrial Engineering, 156, 107274, 14 pages.
 
Hatefi, M.A. (2022). A typology scheme for the criteria weighting methods in MADM. International Journal of Information Technology & Decision Making. https://doi.org/10.1142/S0219622022500985. 50 pages.
 
Hatefi, M.A., Balilehvand, H.R. (2023). Risk assessment of oil and gas drilling operation: an empirical case using a hybrid GROC-VIMUN-modified FMEA method. Process Safety and Environmental Protection, 170, 392–402.
 
Hwang, C.L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Springer, Berlin.
 
Karasan, A., Kahraman, C. (2020). Selection of the most appropriate renewable energy alternatives by using a novel interval-valued neutrosophic ELECTRE I method. Informatica, 31(2), 225–248.
 
Katsikopoulos, K.V., Fasolo, B. (2006). New tools for decision analysts. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 36(5), 960–967.
 
Keeney, R.L., Raiffa, H.D. (1993). Decision with Multiple-Objectives: Preferences and Value Tradeoffs. Cambridge University Press, New York.
 
Kendall, M., Gibbons, J. (1990). Rank Correlation Method. Edward Arnold, London.
 
Kersuliene, V., Zavadskas, E.K., Turskis, Z. (2010). Selection of rational dispute method by applying new Step-wise Weight Assessment Ratio Analysis (SWARA). Journal of Business Economics and Management, 11(2), 243–258.
 
Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E.K., Turskis, Z., Antucheviciene, J. (2018). Simultaneous Evaluation of Criteria and Alternatives (SECA) for multi-criteria decision making. Informatica, 29(2), 265–280.
 
Liang, F., Brunelli, M., Septian, K., Rezaei, J. (2021). Belief-based best worst method. International Journal of Information Technology & Decision Making, 20(1), 287–320.
 
Liu, D., Li, T., Liang, D. (2020). An integrated approach towards modelling ranked weights. Computers & Industrial Engineering, 147, 106629. 16 pages.
 
Lootsma, F.A. (1999). Multi-Criteria Decision Analysis via Ratio and Difference Judgment. Kluwer Academic Publishers, Dordrecht, Netherlands.
 
Morais, D.C., Almeida, A.T., Alencar, L.H., Clemente, T.R.N., Cavalcanti, C.Z.B. (2015). PROMETHEE-ROC model for assessing the readiness of technology for generating energy. Mathematical Problems in Engineering, 2015, 530615, 11 pages.
 
Morita, K. (2003). Automotive power source in 21st century. Journal of Society of Automotive Engineers of Japan, 24, 3–7.
 
Mousaei A., Hatefi, M.A. (2015). A Decision Support System (DSS) to select the premier fuel to develop in the value chain of Natural Gas (NG). International Journal of Oil & Gas Science and Technology, 4(3), 60–76.
 
Oztaysi, B., Onar, S.C., Kahraman, C., Yavuz, M. (2017). Multi-criteria alternative-fuel technology selection using interval-valued intuitionistic fuzzy sets. Transportation Research Part D, 53, 128–148.
 
Patil, A., Herder, P., Brown, K. (2010). Investment decision making for alternative fuel public transport buses: The case of Brisbane transport. Journal of Public Transportation, 13(2), 115–133.
 
Poh, K.L., Ang, B.W. (1999). Transportation fuels and policy for Singapore: An AHP planning approach. Computers and Industrial Engineering, 37(4), 507–525.
 
Rani, P., Mishra, A.R. (2020). Multi-criteria weighted aggregated sum product assessment framework for fuel technology selection using q-rung ortho-pair fuzzy sets. Sustainable Production and Consumption, 24, 90–104.
 
Roberts, R., Goodwin, P. (2002). Weight approximations in multi-attribute decision models. Journal of Multi-Criteria Decision Analysis, 11, 291–303.
 
Sarabando, P., Dias, L.C. (2009). Multi-attribute choice with ordinal information: a comparison of different decision rules. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 39(3), 545–554.
 
Scott, J.A., Ho, W., Dey, P.K. (2012). A review of multi-criteria decision-making methods for bioenergy systems. Energy, 42(1), 146–156.
 
Shah, M.S., Halder, P.K., Shamsuzzaman, A.S.M., Hossain, M.S., Pal, S.K., Sarker, E. (2017). Perspectives of biogas conversion into bio-CNG for automobile fuel in Bangladesh. Journal of Renewable Energy, 4385295, 1–14.
 
Shapiro, S.S., Wilk, M.B. (1965). An analysis of variance test for normality. Biometrika, 52(3/4), 591–611.
 
Singh, A.P., Agarwal, R.A., Agarwal, A.K., Dhar, A., Shukla, M.K. (2018). Prospects of Alternative Transportation Fuels. Springer, Berlin.
 
Sperling, D. (1995). Future-Drive Electric Vehicles and Sustainable Transportation. Island Press, Washington DC.
 
Srivastava, J., Connolly, T., Beach, L.R. (1995). Do ranks suffice? A comparison of alternative weighting approaches in value elicitation. Organizational Behavior Human Decision Process, 63(1), 112–116.
 
Stillwell, W.G., Seaver, D.A., Edwards, W. (1981). A comparison of weight approximation techniques in multi-attribute utility decision making. Organizational Behavior and Human Performance, 28(1), 62–77.
 
Sureeyatanapas, P., Sriwattananusart, K., Niyamosoth, T., Sessomboon, W., Arunyanart, S. (2018). Supplier selection towards uncertain and unavailable information: an extension of TOPSIS method. Operations Research Perspectives, 5, 69–79.
 
Tzeng, G.H., Lin, C.W., Opricovic, S. (2005). Multi-criteria analysis of alternative fuel buses for public transportation. Energy Policy, 33, 1373–1383.
 
Vahdani, B., Zandieh, M., Tavakkoli-Moghaddam, R. (2011). Two novel FMCDM methods for alternative fuel buses selection. Applied Mathematical Modelling, 35, 1396–1412.
 
Wang, J., Zionts, S. (2015). Using ordinal data to estimate cardinal values. Journal of Multi-Criteria Decision Analysis, 22, 185–196.
 
Wang, Y.M., Lou, Y. (2010). Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Mathematical and Computer Modeling, 51(1–2), 1–12.
 
Winebrake, J.J., Creswick, B.P. (2003). The future of hydrogen fueling systems for transportation: An application of perspective-based scenario analysis using the Analytic Hierarchy Process. Technology Forecasting Social Change, 70(2), 359–384.
 
Winkler, R.L., Hays, W.L. (1985). Statistics: Probability, Inference, and Decision. Holt, Rinehart and Winston New York.
 
Zizovic, M., Pamucar, D., Cirovic, G., Zizovic, M.M., Miljkovic, B.D. (2020). Model for determining weight confidents by forming a Non-decreasing Series at Criteria Significance Levels (NDSL). Mathematics, 8, 745.

Biographies

Hatefi Mohammad Ali
https://orcid.org/0000-0001-8740-2392
Hatefi@put.ac.ir

M.A. Hatefi is an associate professor of Energy & Economics Management Department at Petroleum University of Technology (PUT). He received his BS, MSc, and PhD degrees in industrial engineering from Iran University of Science and Technology (IUST), with honor. His area of interest and researches are decision analysis, multiple criteria decision making, operations research, risk analysis, project risk management, and management information systems. He has published several journal papers and books in the mentioned areas. He was the head of Tehran Faculty of Petroleum between 2017 and 2021. He is currently serving as the manager of his department at the PUT. He is also an editorial member of some journals, such as Petroleum Business Review (PBR), and Scientific Journal of Mechanical and Industrial Engineering (SJMIE).


Full article Related articles Cited by PDF XML
Full article Related articles Cited by PDF XML

Copyright
© 2023 Vilnius University
by logo by logo
Open access article under the CC BY license.

Keywords
MCDM criteria weighting approximate weighting methods ROC IROC simulation engine/vehicle selection problem public transport

Metrics
since January 2020
651

Article info
views

410

Full article
views

431

PDF
downloads

80

XML
downloads

Export citation

Copy and paste formatted citation
Placeholder

Download citation in file


Share


RSS

INFORMATICA

  • Online ISSN: 1822-8844
  • Print ISSN: 0868-4952
  • Copyright © 2023 Vilnius University

About

  • About journal

For contributors

  • OA Policy
  • Submit your article
  • Instructions for Referees
    •  

    •  

Contact us

  • Institute of Data Science and Digital Technologies
  • Vilnius University

    Akademijos St. 4

    08412 Vilnius, Lithuania

    Phone: (+370 5) 2109 338

    E-mail: informatica@mii.vu.lt

    https://informatica.vu.lt/journal/INFORMATICA
Powered by PubliMill  •  Privacy policy