Miłosz Kadziński

TL;DR: The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preferences considered within ML.

TL;DR: This paper proposes a hybrid approach that combines the revised Simos procedure, PROMETHEE methods, algorithms for constructing a group compromise ranking, and robustness analysis, and introduces and applies some original procedures based on Binary Linear Programming.

TL;DR: By showing how decision making can be split into manageable and justifiable steps, it is shown how the DSSs for MCDA method recommendation can be grouped into three main clusters, which can enhance a traceable and categorizable development of such systems.

TL;DR: The proposed ELECTRE-based method enriches the spectrum of multiple criteria decision analysis approaches that can be used to effectively approach the problem of the 3PRLP selection, which was so far dominated by Analytic Hierarchy Process and TOPSIS.

TL;DR: In this paper, a new method, called ELECTREGKMS, which employs robust ordinal regression to construct a set of outranking models compatible with preference information, is presented, where preference information supplied by the decision maker is composed of pairwise comparisons stating the truth or falsity of the outranking relation for some real or fictitious reference alternatives.