Supplier Selection Using Fuzzy Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP)

Author(s):  
Ali İhsan Boyacı ◽  
Tuğçen Hatipoğlu ◽  
Hatice Esen
2014 ◽  
Vol 13 (06) ◽  
pp. 1325-1360 ◽  
Author(s):  
Ting-Yu Chen

The purpose of this paper is to develop an inclusion-based LINMAP (i.e., Linear Programming Technique for Multidimensional Analysis of Preference) method for multiple criteria decision analysis that is based on interval-valued Atanassov's intuitionistic fuzzy sets. Using the inclusion comparison possibility in the interval-valued Atanassov's intuitionistic fuzzy context, an inclusion-based index of interval-valued Atanassov's intuitionistic fuzzy numbers is proposed that considers positive and negative ideals. An inclusion-based consistency index and an inclusion-based inconsistency index to measure the concordance and discordance, respectively, between paired comparison judgments are suggested. An inclusion-based LINMAP model is constructed using a linear programming technique to determine the optimal criterion weights and obtain the corresponding comprehensive inclusion-based index for each alternative. Then, the priority order of the alternatives can be acquired according to the comprehensive inclusion-based indices. The feasibility of the proposed method is illustrated using a practical problem that relates to the selection of bridge construction methods. A comparative analysis of other relevant decision-making methods is conducted to validate the effectiveness of the developed methodology.


Author(s):  
DENG-FENG LI ◽  
TAO SUN

The aim of this paper is to develop a fuzzy linear programming technique for multidimensional analysis of preference (FLINMAP) in multiattribute group decision making problems with linguistic variables and incomplete preference information. In this paper, linguistic variables are used to assess an alternative on qualitative attributes using fuzzy ratings corresponding to some triangular fuzzy numbers. Each alternative is assessed on the basis of its distance to a fuzzy positive ideal solution (FPIS) which is unknown a priori. The FPIS and the weights of attributes are calculated by constructing a new linear programming model based on the group consistency and inconsistency indices defined on the basis of preferences between alternatives given by the decision makers. The distance of each alternative to the FPIS can be calculated to determine the ranking order of all alternatives. The implementation process of this methodology is demonstrated with an example.


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