Combinative distance based assessment (CODAS) framework using logarithmic normalization for multi-criteria decision making

  • Sanjib Biswas Operations Management & Information Systems, Calcutta Business School, South 24 Parganas, West Bengal – 743503, India
  • Dragan S. Pamučar University of defense, Military academy
Keywords: CODAS method, logarithmic normalization, smartphone ranking, sensitivity analysis

Abstract



The purpose of this paper is to present an extended Combinative Distance based Assessment (CODAS) framework using logarithmic normalization (LN) scheme. LN is useful in the situations where criteria values differ significantly. This framework is used to carry out a comparative performance based ranking of the popular smartphones in India. The result obtained from this extended version of CODAS method (CODAS-LN) shows consistency with that generated by using some other established multi-criteria decision making (MCDM) approaches. The sensitivity analysis shows considerable stability in the result. Further, it is observed that CODAS-LN is free from rank reversal phenomenon and follows the transitivity property. Findings of the case study suggest that the smartphones with higher computational capability and features rank in top brackets.

Author Biography

Dragan S. Pamučar, University of defense, Military academy
Department of Logistic, Military academa

References

Aggarwal, A., Choudhary, C., & Mehrotra, D. (2018). Evaluation of smartphones in Indian market using EDAS. Procedia computer science, 132, 236-243.

Badi, I., Abdulshahed, A.M., & Shetwan, A. (2018a). A case study of supplier selection for a steelmaking company in Libya by using the Combinative Distance-based ASsessment (CODAS) model. Decision Making: Applications in Management and Engineering, 1 (1), 1-12.

Badi, I., Ballem, M., & Shetwan, A. (2018b). Site selection of desalination plant in Libya by using combinative distance-based assessment (CODAS) method. International Journal for Quality Research, 12 (3), 609-624.

Biswas, S., Bandyopadhyay, G., Guha, B., & Bhattacharjee, M. (2019). An ensemble approach for portfolio selection in a multi-criteria decision making framework. Decision Making: Applications in Management and Engineering, 2 (2), 138-158.

Biswas, S., & Pamucar, D. (2020). Facility Location Selection for B-Schools in Indian Context: A Multi-Criteria Group Decision Based Analysis. Axioms, 9(3), 77.

Bolturk, E., & Kahraman, C. (2018). Interval-valued intuitionistic fuzzy CODAS method and its application to wave energy facility location selection problem. Journal of Intelligent & Fuzzy Systems, 35 (4), 4865-4877.

Boltürk, E., & Karaşan, A. (2018). Interval valued neutrosophic CODAS method for renewable energy selection. In Liu, J., Lu, J., Xu, Y., Martinez, L., Kerre, E.E. (Eds), Data Science and Knowledge Engineering for Sensing Decision Support, 1026-1033.

Büyüközkan, G., & Güleryüz, S. (2016). Multi criteria group decision making approach for smart phone selection using intuitionistic fuzzy TOPSIS. International Journal of Computational Intelligence Systems, 9(4), 709-725.

Çelen, A. (2014). Comparative analysis of normalization procedures in TOPSIS method: with an application to Turkish deposit banking market. Informatica, 25 (2), 185-208.

Changyong, F.E.N.G., Hongyue, W.A.N.G., Naiji, L.U., Tian, C.H.E.N., Hua, H.E., & Ying, L.U. (2014). Log-transformation and its implications for data analysis. Shanghai archives of psychiatry, 26 (2), 105-109.

Chatterjee, P., & Chakraborty, S. (2014). Investigating the Effect of Normalization Norms in Flexible Manufacturing Sytem Selection Using Multi-Criteria Decision-Making Methods. Journal of Engineering Science & Technology Review, 7 (3), 141-150.

Chatterjee, P., & Stevic, Z. (2019). A two-phase fuzzy AHP - fuzzy TOPSIS model for supplier evaluation in manufacturing environment. Operational Research in Engineering Sciences: Theory and Applications, 2 (1), 72-90.

de Farias Aires, R.F., & Ferreira, L. (2019). A new approach to avoid rank reversal cases in the TOPSIS method. Computers & Industrial Engineering, 132, 84-97.

Dehghan-Manshadi, B., Mahmudi, H., Abedian, A., & Mahmudi, R. (2007). A novel method for materials selection in mechanical design: combination of non-linear normalization and a modified digital logic method. Materials & design, 28 (1), 8-15.

Despic, D., Bojović, N., Kilibarda, M. & Kapetanović, M. (2019). Assessment of efficiency of military transport units using the DEA and SFA methods. Military Technical Courier, 67 (1), 68–92.

Eftekhary, M., Gholami, P., Safari, S., & Shojaee, M. (2012). Ranking normalization methods for improving the accuracy of SVM algorithm by DEA method. Modern applied science, 6 (10), 26-36.

García-Cascales, M.S., & Lamata, M.T. (2012). On rank reversal and TOPSIS method. Mathematical and Computer Modelling, 56(5-6), 123-132.

Gharib, M.R. (2020). Comparison of robust optimal QFT controller with TFC and MFC controller in a multi-input multi-output system. Reports in Mechanical Engineering, 1(1), 151-161.

Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., Hooshmand, R., & Antuchevičienė, J. (2017). Fuzzy extension of the CODAS method for multi-criteria market segment evaluation. Journal of Business Economics and Management, 18(1), 1-19.

Ghorabaee, M.K., Zavadskas, E.K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26 (3), 435-451.

Ghorabaee, M. K., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation & Economic Cybernetics Studies & Research, 50 (3), 25-41.

Ghosh, I., & Biswas, S. (2016). A comparative analysis of multi-criteria decision models for ERP package selection for improving supply chain performance. Asia-Pacific Journal of Management Research and Innovation, 12(3-4), 250-270.

Gupta, S., Bandyopadhyay, G., Bhattacharjee, M., & Biswas, S. (2019). Portfolio Selection using DEA-COPRAS at Risk–Return Interface Based on NSE (India). International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8 (10), 4078-4086.

Hassanpour, M., & Pamucar, D. (2019). Evaluation of Iranian household appliance industries using MCDM models. Operational Research in Engineering Sciences: Theory and Applications, 2 (3), 1-25.

Hu, S.K., Lu, M.T., & Tzeng, G.H. (2014). Exploring smart phone improvements based on a hybrid MCDM model. Expert Systems with Applications, 41 (9), 4401-4413.

Huszak, A., & Imre, S. (2010, May). Eliminating rank reversal phenomenon in GRA-based network selection method. Proceedings of IEEE International Conference on Communications, ICC 2010, Cape Town, South Africa, 1-6.

Hwang, C.L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making. Springer, Berlin, Heidelberg. 58-191.

Ijadi Maghsoodi, A., Ijadi Maghsoodi, A., Poursoltan, P., Antucheviciene, J., & Turskis, Z. (2019). Dam construction material selection by implementing the integrated SWARA—CODAS approach with target-based attributes. Archives of Civil and Mechanical Engineering, 19, 1194-1210.

Irvanizam, I., Marzuki, M., Patria, I., & Abubakar, R. (2018). An Application for Smartphone Preference Using TODIM Decision Making Method2018 International Conference on Electrical Engineering and Informatics (ICELTICs), 122-126.

Işıklar, G., & Büyüközkan, G. (2007). Using a multi-criteria decision making approach to evaluate mobile phone alternatives. Computer Standards & Interfaces, 29 (2), 265-274.

Jahan, A., & Edwards, K. L. (2015). A state-of-the-art survey on the influence of normalization techniques in ranking: Improving the materials selection process in engineering design. Materials & Design (1980-2015), 65, 335-342.

Jassbi, J.J., Ribeiro, R.A., & Varela, L.R. (2014). Dynamic MCDM with future knowledge for supplier selection. Journal of Decision Systems, 23 (3), 232-248.

Karmakar, P., Dutta, P., & Biswas, S. (2018). Assessment of mutual fund performance using distance based multi-criteria decision making techniques-An Indian perspective. Research Bulletin, 44 (1), 17-38.

Kim, J., Lee, H., & Lee, J. (2020). Smartphone preferences and brand loyalty: A discrete choice model reflecting the reference point and peer effect. Journal of Retailing and Consumer Services, 52, 101907.

Kong, F., Wei, W., & Gong, J.H. (2016). Rank reversal and rank preservation in ANP method. Journal of Discrete Mathematical Sciences and Cryptography, 19 (3), 821-836.

Kosareva, N., Krylovas, A., & Zavadskas, E.K. (2018). Statistical analysis of MCDM data normalization methods using Monte Carlo approach. The case of ternary estimates matrix. Economic Computation and Economic Cybernetics Studies and Research, 52 (4), 159-175.

Laha, S., & Biswas, S. (2019). A hybrid unsupervised learning and multi-criteria decision making approach for performance evaluation of Indian banks. Accounting, 5 (4), 169-184.

Li, X., Wang, K., Liu, L., Xin, J., Yang, H., & Gao, C. (2011). Application of the entropy weight and TOPSIS method in safety evaluation of coal mines. Procedia Engineering, 26, 2085-2091.

Macharis, C., Springael, J., De Brucker, K., & Verbeke, A. (2004). PROMETHEE and AHP: The design of operational synergies in multicriteria analysis.: Strengthening PROMETHEE with ideas of AHP. European Journal of Operational Research, 153 (2), 307-317.

Mathew, M., Sahu, S., & Upadhyay, A.K. (2017). Effect of normalization techniques in robot selection using weighted aggregated sum product assessment. International Journal of Innovative Research and Advanced Studies, 4 (2), 59-63.

Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2018). A new multi-criteria decision making approach for sustainable material selection problem: A critical study on rank reversal problem. Journal of Cleaner Production, 182, 466-484.

Mukhametzyanov, I., & Pamucar, D. (2018). A sensitivity analysis in MCDM problems: A statistical approach. Decision making: applications in management and engineering, 1 (2), 51-80.

Önüt, S., Kara, S.S., & Işik, E. (2009). Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company. Expert systems with applications, 36 (2), 3887-3895.

Pamučar, D.S., Božanić, D., & Ranđelović, A. (2017). Multi-criteria decision making: An example of sensitivity analysis. Serbian journal of management, 12 (1), 1-27.

Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert systems with applications, 42 (6), 3016-3028.

Pamucar, D., Ecer, F. (2020). Prioritizing the weights of the evaluation criteria under fuzziness: The fuzzy full consistency method – FUCOM-F. Facta Universitatis, series: Mechanical Engineering, 18 (3), 419 - 437.

Pamucar, D.S. & Savin, L.M. (2020). Multiple-criteria model for optimal off-road vehicle selection for passenger transportation: BWM-COPRAS model. Military Technical Courier, 68 (1), 28-64.

Panchal, D., Chatterjee, P., Shukla, R. K., Choudhury, T., & Tamosaitiene, J. (2017). Integrated Fuzzy AHP-Codas Framework for Maintenance Decision in Urea Fertilizer Industry. Economic Computation & Economic Cybernetics Studies & Research, 51(3), 179-196.

Pavličić, D. (2001). Normalization affects the results of MADM methods. Yugoslav journal of operations research, 11 (2), 251-265.

Precup, R.-E., Preitl , S., Petriu, E., Bojan-Dragos, C.-A., Szedlak-Stinean, A.-I., Roman, R.-C., & Hedrea, E.-L. (2020). Model-Based Fuzzy Control Results for Networked Control Systems. Reports in Mechanical Engineering, 1 (1), 10-25.

Rani, P., Mishra, A.R., & Ansari, M.D. (2019). Analysis of Smartphone Selection Problem under Interval-valued Intuitionistic Fuzzy ARAS and TOPSIS Methods. 2019 Fifth International Conference on Image Information Processing (ICIIP), 509-514.

Ren, J. (2018). Sustainability prioritization of energy storage technologies for promoting the development of renewable energy: A novel intuitionistic fuzzy combinative distance-based assessment approach. Renewable energy, 121, 666-676.

Roy, J., Chatterjee, K., Bandyopadhyay, A., & Kar, S. (2018). Evaluation and selection of medical tourism sites: A rough analytic hierarchy process based multi‐attributive border approximation area comparison approach. Expert Systems, 35(1), e12232.

Saqlain, M., Jafar, M. N., & Riaz, M. (2020). A New Approach of Neutrosophic Soft Set with Generalized Fuzzy TOPSIS in Application of Smart Phone Selection. Neutrosophic Sets and Systems, 32 (1), 307-316.

Sarraf, A.Z., Mohaghar, A., & Bazargani, H. (2013). Developing TOPSIS method using statistical normalization for selecting Knowledge management strategies. Journal of Industrial Engineering and Management, 6 (4), 860-875.

Seker, S. (2020). A novel interval-valued intuitionistic trapezoidal fuzzy combinative distance-based assessment (CODAS) method. Soft Computing, 24 (3), 2287-2300.

Senouci, M.A., Mushtaq, M.S., Hoceini, S., & Mellouk, A. (2016). TOPSIS-based dynamic approach for mobile network interface selection. Computer Networks, 107, 304-314.

Shannon, C.E. (1948). A mathematical theory of communication. Bell Systems Technical Journal, 27 (3), 379-423.

Sharma, H.K., Roy, J., Kar, S., & Prentkovskis, O. (2018). Multi criteria evaluation framework for prioritizing indian railway stations using modified rough ahp-mabac method. Transport and telecommunication journal, 19 (2), 113-127.

Soltanifar, M., & Shahghobadi, S. (2014). Survey on rank preservation and rank reversal in data envelopment analysis. Knowledge-Based Systems, 60, 10-19.

Triantaphyllou, E., & Shu, B. (2001). On the maximum number of feasible ranking sequences in multi-criteria decision making problems. European Journal of Operational Research, 130 (3), 665-678.

Tuş, A., & Adalı, E.A. (2018). Personnel assessment with CODAS and PSI methods. Alphanumeric Journal, 6 (2), 243-256.

Vafaei, N., Ribeiro, R.A., & Camarinha-Matos, L.M. (2018). Data normalisation techniques in decision making: case study with TOPSIS method. International journal of information and decision sciences, 10 (1), 19-38.

Wang, X., & Triantaphyllou, E. (2008). Ranking irregularities when evaluating alternatives by using some ELECTRE methods. Omega, 36 (1), 45-63.

Wang, Y.M., & Luo, Y. (2009). On rank reversal in decision analysis. Mathematical and Computer Modelling, 49 (5-6), 1221-1229.

Yeni, F.B., & Özçelik, G. (2019). Interval-valued Atanassov intuitionistic Fuzzy CODAS method for multi criteria group decision making problems. Group Decision and Negotiation, 28 (2), 433-452.

Yildiz, A., & Ergul, E.U. (2015). A two-phased multi-criteria decision-making approach for selecting the best smartphone. South African Journal of Industrial Engineering, 26 (3), 194-215.

Zavadskas, E.K., & Turskis, Z. (2008). A new logarithmic normalization method in games theory. Informatica, 19 (2), 303-314.

Zavadskas, E.K., & Turskis, Z. (2011). Multiple criteria decision making (MCDM) methods in economics: an overview. Technological and economic development of economy, 17 (2), 397-427.

Zavadskas, E.K., Zakarevicius, A., & Antucheviciene, J. (2006). Evaluation of ranking accuracy in multi-criteria decisions. Informatica, 17 (4), 601-618.

Zhou, J., Li, K.W., Baležentis, T., & Streimikiene, D. (2020). Pythagorean fuzzy combinative distance-based assessment with pure linguistic information and its application to financial strategies of multi-national companies. Economic Research-Ekonomska Istraživanja, 33 (1), 974-998.

Zolfani, S.H., Yazdani, M., Pamucar, D., & Zarate, P. (2020). A VIKOR and TOPSIS focused reanalysis of the MADM methods based on logarithmic normalization. Facta universitatis series: Mechanical Engineering, 18 (3), 341-355.

Zou, Z.H., Yi, Y., & Sun, J.N. (2006). Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Journal of Environmental sciences, 18 (5), 1020-1023.

Published
2021/12/02
Section
Original Scientific Paper