American Journal of Information Systems
ISSN (Print): 2374-1953 ISSN (Online): 2374-1988 Website: http://www.sciepub.com/journal/ajis Editor-in-chief: Sergii Kavun
Open Access
Journal Browser
Go
American Journal of Information Systems. 2013, 1(1), 31-43
DOI: 10.12691/ajis-1-1-5
Open AccessReview Article

A Survey on Multi Criteria Decision Making Methods and Its Applications

Martin Aruldoss1, , T. Miranda Lakshmi2 and V. Prasanna Venkatesan1

1Department of Banking Technology, Pondicherry University, Puducherry, India

2Department of Computer Science, Research and Development Centre, Bharathiyar University, Coimbatore, India

Pub. Date: December 15, 2013

Cite this paper:
Martin Aruldoss, T. Miranda Lakshmi and V. Prasanna Venkatesan. A Survey on Multi Criteria Decision Making Methods and Its Applications. American Journal of Information Systems. 2013; 1(1):31-43. doi: 10.12691/ajis-1-1-5

Abstract

Multi Criteria Decision Making (MCDM) provides strong decision making in domains where selection of best alternative is highly complex. This survey paper reviews the main streams of consideration in multi criteria decision making theory and practice in detail. The main purpose is to identify various applications and the approaches, and to suggest approaches which are most robustly and effectively useable to identify best alternative. This survey work also addresses the problem in fuzzy multi criteria decision making techniques. Multi criteria decision making have been applied in many domains. MCDM method helps to choose the best alternatives where many criteria have come into existence, the best one can be obtained by analyzing the different scope for the criteria, weights for the criteria and the choose the optimum ones using any multi criteria decision making techniques. This survey provides the comprehensive developments of various methods of FMCDM and its applications.

Keywords:
multi criteria decision making fuzzy MCDM TOPSIS best choice decision making

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Figures

Figure of 4

References:

[1]  Albayrak, E., Erensal, Y. C. (2005). “A study bank selection decision in Turkey using the extended fuzzy AHP method”. Proceeding of 35th International conference on computers and industrial engineering, Istanbul, Turkey.
 
[2]  Albayrak, E., &Erensal, Y. C. (2005). A study bank selection decision in Turkey using the extended fuzzy AHP method. In 35th International conference on computers and industrial engineering, Istanbul, Turkey.
 
[3]  Aldlaigan, A., & Buttle, F. A. (2002). SYSTRA-SQ: A new measure of bank service quality. International Journal of Service Industry Management, 13, 38-362.
 
[4]  Aldlaigan, A.,Buttle, F.A.(2002). “A new measure of bank service quality”. International Journal of Service Industry Management, Vol. 13, pp. 38-362.
 
[5]  Business Credits (2006). “Non-financial data can predict future profitability”. BusinessCredits, Vol 108, Nbr. (4), pp.57.
 
[6]  Anderson, W., Jr., Cox, J. E. P., & Fulcher, D. (1976). Bank selection decisions and marketing segmentation. Journal of Marketing, 40(1), 40-45.
 
[7]  Anonymous (2006). Non-financial data can predict future profitability. Business Credits, 108(4), 57.
 
[8]  Nikoomaram.H, M.Mohammadi, M. JavadTaghipouria and Y. Taghipourian(2009). “Training Performance Evaluation of Administration Sciences Instructors by Fuzzy MCDM Approach”. Tehran, Iran.
 
[9]  Yusuf TanselIç, (2012) “Development of a credit limit allocation model for banks using an integrated Fuzzy TOPSIS and linear programming”. Expert System with Applications. Vol. 39(5), pp. 5309-5316.
 
[10]  Arshadi, N., & Lawrence, E. C. (1987). An empirical investigation of new bank performance. Journal of Banking and Finance, 11(1), 33-48.
 
[11]  Ashton, C. (1998). Balanced scorecard benefits Nat West Bank. International Journal of Retail and Distribution Management, 26(10), 400-407.
 
[12]  TuncayOzcan, NumanCelebi,(2011) “Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem”. Vol. 38, pp.9773-9779.
 
[13]  Mohammad SaeedZaeri, Amir Sadeghi, Amir Naderi,etal.,(2011). “Application of multi criteria decision making technique to evaluation suppliers in supply chain management”, African Journal of Mathematics and Computer Science Research Vol. 4 (3), pp.100-106.
 
[14]  Athanassopoulos, A., & Giokas, D. (2000). On-going use of data envelopment analysis in banking institutions: evidence from the Commercial Bank of Greece. Interfaces, 30(2), 81-95.
 
[15]  Anjali Awasthia,∗, S.S. Chauhanb, S.K. Goyalb A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty a CIISE, Concordia University, Montreal, Canada b Decision Sciences, JMSB, Concordia University, Montreal, Canada.
 
[16]  Schinas O.(2007) “Examining the use and application of Multi -Criteria DecisionMaking Techniques in Safety Assessment”, International Symposium on Maritime Safety, Security & Environmental Protection, Athens.
 
[17]  Anjali Awasthia, S.S. Chauhanb, S.K. Goyalb(2000). “A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty” CIISE, Montreal, Canada.
 
[18]  Bauer, P. W., Berger, A. N., Ferrier, G. D., & Humphrey, D. B. (1998). Consistencyconditions for regulatory analysis of financial institutions: A comparison offrontier efficiency methods. Journal of Economic and Business, 50(2), 85-114.
 
[19]  Beccalli, A. (2007). Does IT investment improve bank performance? Evidence from Europe. Journal of Banking & Finance, 31, 2205-2230.
 
[20]  T.C. Chu, (2002) “Facility location selection using fuzzy TOPSIS under group decisions”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems Vol. 10 (6), pp. 687-701.
 
[21]  MahdiZarghami, FerencSzidarovszky(2011) “Revising the OWA operator for multi criteria decision making problems under uncertainty a Faculty of Civil Engineering ”, Tabriz 51666-16471, Iran.
 
[22]  Caballero, R., Cerda, E., Munoz, M.M., Rey, L., 2004. Stochastic approach versus multiobjective approach for obtaining efficient solutions in stochastic multiobjective programming problems. European Journal of Operational Research 158, 633-648.
 
[23]  Changchit, C., Terrell, M. P., 1993. A multi-objective reservoir operation model with stochastic inflows. Computers and Industrial Engineering 24 (2), 303-313.
 
[24]  DoraidDalalah, Mohammed Hayajneh, FarhanBatieha,(2011) .“A fuzzy multi-criteria decision making model for supplier selection”, Expert Systems with Applications Vol. 38, pp. 8384-8391.
 
[25]  Yong-Sheng Ding, Zhi-HuaHu, Wen-Bin Zhang,(2011). “ Multi-criteria decision making approach based on immune co-evolutionary algorithm with application to garment matching problem”, Expert Systems with Applications Vol. 38, pp.10377-10383.
 
[26]  Chen, C. W. E., 2000. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems 114, 1-9.
 
[27]  Chen, S. J., Hwang, C. L., 1991. Fuzzy Multiple Attribute Decision making. Springer- Verlag, Berlin.
 
[28]  Yi Peng, Yong Zhang, Yu Tang, Shiming Li, (2011). “An incident information management framework based on data integration, datamining, and multi-criteria decision making ”, Decision Support Systems Vol. 51, pp.316-327.
 
[29]  MehtapDursun, E. ErtugrulKarsak,MelisAlmulaKaradayi,(2011). “Assessment of health-care waste treatment alternatives using fuzzymulti-criteria decision making approaches”, Expert Systems with Applications Vol. 38, pp.10377-10383.
 
[30]  H. Nikoomaram1*, M. Mohammadi 1, M. Javad Taghipourian1 and Y. Taghipourian 2 .Training Performance Evaluation of Administration. Sciences Instructors by Fuzzy MCDM Approach. Department of AccountingIslamic Azad University-Science and Research Branch, Tehran, Iran Department of Accounting, Islamic Azad University - Chalous, Tehran, Iran.
 
[31]  Hung-Yi Wua,*, Gwo-HshiungTzenga, b, Yi-Hsuan Chen c. A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard a Department of Business and Entrepreneurial Management, Kainan University, No. 1, Kainan Road, Luchu, Taoyuan 338, Taiwan.
 
[32]  SaharRezaiana, Seyed Ali Joziba,(2012) “Health- Safety and Environmental Risk Assessment of Refineries Using of Multi Criteria Decision Making Method”, APCBEE Procedia Vol.3 , pp. 235-238.
 
[33]  KeivanSadeghzadeh, Mohammad BagherSalehi (2011). “Mathematical analysis of fuel cell strategic technologies development solutions in the automotive industry by the TOPSIS multi-criteria decision making method” , International journal of hydrogen energy Vol. 3 6, pp.13272-13280.
 
[34]  MaherAburrous,*, M.A. Hossain a, KeshavDahal a, FadiThabtahb. Intelligent phishing detection system for e-banking using fuzzy data mining .Department of Computing University of Bradford, Bradford, UK. MIS Department Philadelphia University Amman, Jordan.
 
[35]  MahdiZarghami *, FerencSzidarovszky. Revising the OWA operator for multi criteria decision making problems under uncertainty a Faculty of Civil Engineering, University of Tabriz, Tabriz 51666-16471, Iran. b Systems and Industrial Engineering Department, University of Arizona, Tucson, AZ 85721-0020, USA.
 
[36]  Hung-Yi Wua, Gwo-HshiungTzenga,b, Yi-Hsuan Chen c(2011). “A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard”, Taiwan.
 
[37]  Nese YalcınSecme, Ali Bayrakdaroglu, CengizKahramanb, (2007). “Fuzzy performance evaluation in Turkish Banking Sector using AnalyticHierarchy Process and TOPSIS”. Expert Systems with Applications, Vol. 36, pp.11699-11709.
 
[38]  Mohammad Saeed Zaeri*, Amir Sadeghi, Amir Naderi. Application of multicriteriadecisionmakingtechniqueto evaluation suppliersinsupplychain management. Department of Industrial Engineering, Islamic Azad University (KarajBranch), Tehran, Iran. Department of Management Information Technology, FarabiInstitute of Higher Education, Iran.
 
[39]  Nes_eYalcınSecme a,*, Ali Bayrakdarog˘lu a, CengizKahramanb Fuzzy performance evaluation in Turkish Banking Sector using Analytic Hierarchy Process and TOPSIS. a Department of Business Administration, Nevs_ehir University, 50300 Nevs_ehir, Turkey . Institute of Management of Technology, National Chiao-Tung University, 1001 Ta-Hsueh Road, Hsinchu 300, Taiwan Division of Global Purchasing, JI-EE Industry Co., Ltd., No. 498, Sec. 2, Bentian St., An Nan Dist., Tainan 709, Taiwan.
 
[40]  Ashton, C. (1998). “Balanced scorecard benefits Nat West Bank”. International Journal of Retail and Distribution Management, Vol. 26(10), pp.400-407.
 
[41]  Tsai, W. H., Yang, C. C., Leu, J. D., Lee, Y. F., & Yang, C. H. (2001). “An Integrated Group Decision Making Support Model for Corporate Financing Decisions”. Group Decision and Negotiation, 1-25.C.T. Chen, A fuzzy approach to select the location of the distribution center, Fuzzy Sets and Systems ,Vol. 118 , pp. 65-73.
 
[42]  Oresti Schinas ,Assistant Professor of Maritime Studies, Frederick University of Cyprus, Limassol, Cyprus .Examining the use and application of Multi-Criteria Decision Making Techniques in Safety Assessment and CEO, Transmart Consulting, Athens, Greece ,schinaso@transmartconsulting.gr.
 
[43]  C.T. Chen, A fuzzy approach to select the location of the distribution center, Fuzzy Sets and Systems 118 (2001) 65-73.
 
[44]  S.Y. Chou, Y.H. Chang, C.Y. Shen, (2008). “A fuzzy simple additive weighting system under group decision making for facility location selection with objective/subjective attributes”, European Journal of Operational Research Vol. 189 (1) 132-145.
 
[45]  Avijit Mazumdar (2010) “Application of multi-criteria decision Making (MCDM) approaches on teachers Performance evaluation and appraisal”.
 
[46]  S.Y. Chou, Y. H. Chang, C. Y. Shen, A fuzzy simple additive weighting system under group decision making for facility location selection with objective/subjective attributes, European Journal of Operational Research 189 (1) (2008) 132-145.
 
[47]  T.C. Chu, Facility location selection using fuzzy TOPSIS under group decisions, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10 (6) (2002) 687-701.
 
[48]  Chen, C.W.E.,( 2000). “Extensions of the TOPSIS for group decision-making under fuzzy environment”. Fuzzy Sets and Systems vol. 114, pp.1-9.
 
[49]  RabahMedjoudj, DjamilAissan, Klaus Dieter Haim, (2013).” Power customer satisfaction and profitability analysis using multi-criteria decision making methods”,Electrical Power and Energy Systems Vol. 45 , pp.331-339.
 
[50]  Kuo-Liang Lee, Shu-Chen Lin, A fuzzy quantified SWOT procedure for environmental evaluation of an international distribution center, Information Sciences 178 (2) (2008) 531-549.
 
[51]  J.J. Buckley, Ranking alternatives using fuzzy numbers, Fuzzy Sets Systems 15 (1) (1985) 21-31.
 
[52]  V. Lakshmana Gomathi Nayagam, S. Murali krishnan , Geetha Sivaraman,(2011). “Multi-criteria decision-making method based on interval-valued intuitionistic fuzzy sets”, pp.1464-1467.
 
[53]  H.C.W. Lau, Christina W.Y. Wong,, P.K.H. Lau, K.F. Pun, B. Jiang, K.S. Chin (2003) “A fuzzy multi-criteria decision support procedure for enhancing information delivery in extended enterprise networks.”,Vol.16,pp.1-9.
 
[54]  Teghem, J., Dufrane, D., Thauvoye, M., Kunsch, P., 1986. STRANGE: An interactive method for multi-objective linear programming under uncertainty. European Journal of Operational Research 26, 65-82.
 
[55]  Torra, V., Godo, L., 1997. Averaging continuous distributions with the WOWA operator. In: Proceedings of EFDAN’ 97, Dortmund, Germany.
 
[56]  Shu-Hsien Liao, and Kuo-Chung Lu (2002), “Evaluating Anti-Armor Weapon Using Ranking Fuzzy Numbers”, Vol. 11(1), pp.33-48.
 
[57]  Mahammad Haghighi, Ali Divandari, Masoud Keimasi(2010) “The impact of 3D e-readiness on e-banking development in Iran: A fuzzy AHP analysis.” Vol. (37), Issue 6, pp.4084-4093.
 
[58]  Torra, V., Narukawa, Y., 2007. Modeling Decisions: Information Fusion and Aggregation Operators. Springer, Berlin.
 
[59]  YusufTansel _Iç. Development of a credit limit allocation model for banks using an integrated Fuzzy. TOPSIS and linear programming.Department of Industrial Engineering, Faculty of Engineering, Baskent University, 06810 Baglica, Etimesgut, Ankara, Turkey.
 
[60]  Anderson, W., Jr., Cox, J. E. P., &Fulcher, D. (1976). “Bank selection decisions and marketing segmentation”. Journal of Marketing, Vol. 40(1), pp. 40-45.
 
[61]  Arshadi, N., Lawrence, E. C. (1987). “An empirical investigation of new bank performance”. Journal of Banking and Finance, Vol. 11(1), pp.33-48.
 
[62]  Ashton, C. (1998). “Balanced scorecard benefits Nat West Bank”. International Journal of Retail and Distribution Management, Vol. 26(10), pp.400-407.
 
[63]  Athanassopoulos, Giokas, D. (2000). “On-going use of data envelopment analysis in banking institutions”. Evidence from the Commercial Bank of Greece. Interfaces, Vol. 30(2), pp.81-95.
 
[64]  Bauer, P. W., Berger, A. N., Ferrier, G. D., & Humphrey, D. B. (1998). “Consistency conditions for regulatory analysis of financial institutions”: A comparison of frontier efficiency methods. Journal of Economic and Business, Vol. 50(2), pp.85-114.
 
[65]  Beccalli, A. (2007). “Does IT investment improve bank performance? Evidence from Europe”. Journal of Banking & Finance, Vol. 31, pp.2205-2230.
 
[66]  Caballero, R., Cerda, E., Munoz, M.M., Rey, L., (2004). “Stochastic approach versus multi objective approach for obtaining efficient solutions in stochastic multi objective programming problems”. European Journal of Operational Research 158, pp.633-648.
 
[67]  Changchit, C., Terrell, M.P., (1993).“A multi-objective reservoir operation model with stochastic inflows”. Computers and Industrial Engineering Vol. 24 (2), pp.303-313.
 
[68]  Chen, S.J., Hwang, C.L., (1991). “Fuzzy Multiple Attribute Decision making”. Springer- Verlag, Berlin.
 
[69]  MaherAburrous, M.A. Hossain, Keshav Dahal, FadiThabtah(2008). “Intelligent phishing detection system for e-banking using fuzzy data mining “. Jordan.
 
[70]  Kuo-Liang Lee, Shu-Chen Lin, (2008) “A fuzzy quantified SWOT procedure for environmental evaluation of an international distribution centre”, Information Sciences Vol. 178 (2), pp.531-549.
 
[71]  J.J. Buckley, (1985). “Ranking alternatives using fuzzy numbers”, Fuzzy Sets Systems Vol. 15 (1), pp. 21-31.
 
[72]  Torra, V., Godo, L., (1997). “Averaging continuous distributions with the WOWA operator”. EFDAN’ 97, Germany.
 
[73]  Torra, V., Narukawa, Y., (2007). “Modelling Decisions: Information Fusion and Aggregation Operators”. Springer, Berlin.
 
[74]  Yusuf TanselIç, (2012) “Development of a credit limit allocation model for banks using an integrated Fuzzy TOPSIS and linear programming”. Expert System with Applications. Vol. 39(5), pp. 5309-5316.
 
[75]  ErgünEraslan, Yusuf TanselIç, (2011). “A Multi-Criteria Approach for Determination of Investment Regions: Turkish Case”. Industrial Management and Data Systems Vol. 111(6).
 
[76]  Yusuf TanselIç, Mustafa Yurdakul,(2010). “Development of a quick credibility scoring decision support system using fuzzy TOPSIS”. Expert Systems with Applications Vol. 37(1), pp. 567-574.