A probabilistic multiple criteria sorting approach based on distance functions

In this paper, a new probabilistic distance based sorting (PDIS) method is developed for multiple criteria sorting problems. The distance to the ideal point is used as a criteria disaggregation function to determine the values of alternatives. These values are used to sort alternatives into the predefined classes. The method also calculates probabilities that each alternative belong to the predefined classes in order to handle alternative optimal solutions. It is applied to five data sets and its performance is compared with two well-known methods from literature. Computational experiments show that the PDIS method performs better than the other methods.


A Mathematical programming evaluation approach for multiple criteria sorting problems
Civelek, Merve; Karasakal, Esra; Department of Operational Research (2019)
Multiple criteria sorting problem is to assign alternatives, evaluated according to multiple criteria, into predefined preference ordered classes. In this study, a new distance metric based sorting method is developed to solve multiple criteria sorting problems without specifying class thresholds between preference-ordered classes. The aim of the proposed method is to assign each alternative to one class or a set of possible adjacent classes considering the distance to class centroids. In the proposed metho...
An interactive probabilistic approach to multi-criteria sorting
BUGDACI, Asli Gul; KOKSALAN, Murat; Ozpeynirci, Selin; Serin, Yaşar Yasemin (2013-10-01)
This article addresses the problem of sorting alternatives evaluated by multiple criteria among preference-ordered classes. An interactive probabilistic sorting approach is developed in which the probability of an alternative being in each class is calculated and alternatives are assigned to classes keeping the probability of incorrect assignments below a specified small threshold value. The decision maker is occasionally required to place alternatives to classes. The probabilities for unassigned alternativ...
A Probabilistic and interactive approach to multiple criteria sorting
Mutlu, Sinem; Köksalan, Murat; Serin, Yaşar Yasemin; Department of Industrial Engineering (2015)
In this thesis, we develop a method in order to assign alternatives that are evaluated by multiple criteria into preference ordered classes probabilistically. Our motivation is that; when there are large sets of alternatives, placement could be realized in a fast and effective way based on a reasonable misclassification ratio. We assume that the underlying utility function of Decision Maker (DM) is additive. We first ask DM to place reference alternatives into the classes. We develop an interactive probabil...
A Hybrid Nonlinear Method for Array Thinning
Epcacan, Erdal; Çiloğlu, Tolga (2018-05-01)
A nonlinear method for array thinning is proposed. The method is based on hybrid usage of genetic algorithm (GA) and nonlinear apodization. The method proposes a special layout, which consists of two subarrays. Layout and weights for the subarray having larger aperture are designed by GA. Layout and weights for the second subarray are determined according to results of the aforementioned design. Dual apodization is applied to the outputs of the subarrays to obtain the output. Results show that there is an i...
An interactive approach for placing alternatives in preference classes
Köksalan, Mustafa Murat; Ulu, C (2003-01-16)
In this paper, we consider the multiple criteria decision making problem of partitioning alternatives into preference classes. We develop an interactive procedure based on the assumption that the decision maker (DM) has a linear utility function. The approach requires the DM to place an alternative in a preference class from time to time. Based on the preference information derived from the DM's placement as well as from dominance and linearity, we try to place other alternatives. We present results on the ...
Citation Formats
B. ÇELİK, E. Karasakal, and C. İyigün, “A probabilistic multiple criteria sorting approach based on distance functions,” EXPERT SYSTEMS WITH APPLICATIONS, pp. 3610–3618, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40041.