A Data Pre-Processing Model for the Topsis Method

Open access

Abstract

TOPSIS is one of the most popular methods of multi-criteria decision making (MCDM). Its fundamental role is the establishment of chosen alternatives ranking based on their distance from the ideal and negative-ideal solution. There are three primary versions of the TOPSIS method distinguished: classical, interval and fuzzy, where calculation algorithms are adjusted to the character of input rating decision-making alternatives (real numbers, interval data or fuzzy numbers). Various, specialist publications present descriptions on the use of particular versions of the TOPSIS method in the decision-making process, particularly popular is the fuzzy version. However, it should be noticed, that depending on the character of accepted criteria – rating of alternatives can have a heterogeneous character. The present paper suggests the means of proceeding in the situation when the set of criteria covers characteristic criteria for each of the mentioned versions of TOPSIS, as a result of which the rating of the alternatives is vague. The calculation procedure has been illustrated by an adequate numerical example.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Afshar A. Marino M.A. Saadatpour M. Afshar A. (2011). Fuzzy TOPSIS multicriteria decision analysis applied to Karun reservoirs system. Water Resource Management25 545–563.

  • Amiri M.P. (2010). Project selection for oil-fields development by using the AHP and fuzzy TOPSIS methods. Expert Systems with Applications37 6218–6224.

  • Amiri M. Zandieh M. Soltani R. Vahdani B. (2009). A hybrid multi-criteria decision-making model for firms competence evaluation. Expert Systems with Applications36 12314–12322.

  • Awasthi A. Chauhan S.S. Omrani H. (2011). Application of fuzzy TOPSIS in evaluating sustainable transportation systems. Expert Systems with Applications38 12270–12280.

  • Aydogan E.K. (2011). Performance measurement model for Turkish aviation firms using the rough-AHP and TOPSIS methods under fuzzy environment. Expert Systems with Applications38 3992–3998.

  • Behzadian M. Khanmohammadi O.S. Yazdani M. Ignatius J. (2012). A state of the art survey of TOPSIS applications. Expert Systems with Applications39 13051–13069.

  • Bottani E. Rizzi A. (2006). A fuzzy TOPSIS methodology to support outsourcing of logistics services. Supply Chain Management: An International Journal11 294–308.

  • Byun H.S. Lee K.H. (2005). A decision support system for the selection of a rapid prototyping process using the modified TOPSIS method. International Journal of Advanced Manufacturing Technology26 1338–1347.

  • Cheng S. Chan C.W. Huang G.H. (2002): Using multiple criteria decision analysis for supporting decision of solid waste management. Journal of Environmental Science and Health Part A37 975–990.

  • Chen C.T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems114 1–9.

  • Chen M.H. Tzeng G.H. (2004). Combining gray relation ad TOPSIS concepts for selecting an expatriate host country. Mathematical and Computer Modelling40 1473–1490.

  • Chu T.C. (2002). Facility location selection using fuzzy TOPSIS under group decision. International Journal of UncertaintyFuzziness and Knowledge-Based Systems10 687–701.

  • Chu T.C. Lin Y.C. (2003). A fuzzy TOPSIS method for robot selection. International Journal of Advanced Manufacturing Technology21 284–290.

  • Deng H. Yeh C.H. Willis R.J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers & Operations Research27 963–973.

  • Dubois D. Prade H. (1978). Operations on fuzzy numbers. International Journal of Systems Science9 613–626.

  • Erkayman B. Gundogar E. Akkaya G. Ipek M. (2011). A fuzzy TOPSIS approach for logistics center location problem. Journal of Business Case Studies7 49–54.

  • Ertugrul I. (2010). Fuzzy group decision making for the selection of facility location. Group Decision and Negotiation20 725–740.

  • Hwang C.L. Yoon K. (1981). Multiple attribute decision making: Methods and applications. Berlin: Springer Verlag.

  • Ishizaka A. Nemery P. (2013). Multi-criteria decision analysis: Methods and software. John Wiley & Sons Ltd.

  • Jahanshahloo G.R. Lotfi F.H. Izadikhah M. (2006a). An algorithmic method to extend TOPSIS for decision-making problems with interval data. Applied Mathematics and Computation175 1375–1384.

  • Jahanshahloo G.R. Lotfi F.H. Izadikhah M. (2006b). Extension of the TOPSIS method for decision-making problems with fuzzy data. Applied Mathematics and Computation181 1544–1551.

  • Janic M. (2003). Multicriteria evaluation of high-speed rail transrapid maglev and air passenger transport in Europe. Transportation Planning and Technology26 491–512.

  • Kahraman C. Büyüköykan G. Ates N.Y. (2007). A two phase multi-attribute decision-making approach for new product introduction. Information Sciences177 1567–1582.

  • Kannan G. Pokharel S. Kumar P.S. (2009). A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources Conservation and Recycling54 28–36.

  • Krohling R.A. Campanharo V.C. (2011). Fuzzy TOPSIS for group decision making: A case study for accidents with oil spill in the sea. Expert Systems with Applications38 4190–4197.

  • Lin C.T. Tsai M.C. (2010). Location choice for direct foreign investment in new hospitals in China by using ANP and TOPSIS. Quality Quantity44 375–390.

  • Lin M.C. Wang C.C. Chen M.S. Chang C.A. (2008). Using AHP and TOPSIS approaches in customer-driven product design process. Computers in Industry59 17–31.

  • Milani A.S. Shanian A. Madoliat R. (2005). The effect of normalization norms in multiple attribute decision making models: A case study in gear material selection. Structural Multidisciplinary Optimization29 312–318.

  • Moore R.E. (1979). Methods and applications of interval analysis. Studies in Applied and Numerical Mathematics. Madison: University of Wisconsin.

  • Park J.H. Park I. Kwun Y.C. Tan X. (2011). Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment. Applied Mathematical Modeling35 2544–2556.

  • Parkan C. Wu M.L. (1999). Decision making and performance measurement models with applications to robot selection. Computers and Industrial Engineering36 503–523.

  • Sengupta A. Pal T.K. (2000). On comparing interval numbers. European Journal of Operational Research127 28–43.

  • Srdjevic B. Medeiros Y.D.P. Faria A.S. (2004). An objective multi criteria evaluation of water management scenarios. Water Resources Management18 35–54.

  • Sun C.C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications37 7745–7754.

  • Sun C.C. Lin G.T.R. (2009). Using fuzzy TOPSIS method for evaluating the competitive advantages of shopping websites. Expert Systems with Applications36 11764–11771.

  • Wang J.W. Cheng C.H. Huang K.C. (2009). Fuzzy hierarchical TOPSIS for supplier selection. Applied Soft Computing9 377–386.

  • Wang T.C. Chang T.H. (2007). Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Expert Systems with Applications33 870–880.

  • Wang T.C. Lee H.D. (2009). Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Systems with Applications36 8980–8985.

  • Wang W.P. (2009). Toward developing agility evaluation of mass customization systems using 2-tuple linguistic computing. Expert Systems with Applications36 3439–3447.

  • Yager R.R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information Sciences24 143–161.

  • Yang T. Chou P. (2005). Solving a multiresponse simulation-optimization problem with discrete variables using a multi-attribute decision making method. Mathematics and Computers in Simulation68 9–21.

  • Yoon K. Hwang C.L. (1985). Manufacturing plant location analysis by multiple attribute decision making: Part I – single plant strategy. International Journal of Production Research23 345–359.

  • Zadeh L.A. (1965). Fuzzy sets. Information and Control8 333–353.

Search
Journal information
Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 273 160 1
PDF Downloads 127 88 0