Learning non-compensatory sorting models using efficient SAT/MaxSAT formulations

Ali Tlili, Khaled Belahcène, Oumaima Khaled, Vincent Mousseau and Wassila Ouerdane

European Journal of Operational Research, 2022, vol. 298, issue 3, 979-1006

Abstract: The Non-Compensatory Sorting model aims at assigning alternatives evaluated on multiple criteria to one of the predefined ordered categories. Computing parameters of the Non-Compensatory Sorting model compatible to a set of reference assignments is computationally demanding. To overcome this problem, two formulations based on Boolean satisfiability have recently been proposed to learn the parameters of the Non-Compensatory Sorting model from perfect preference information, i.e. when the set of reference assignments can be completely represented in the model. In this paper, two popular variants of the Non-Compensatory Sorting model are considered, the Non-Compensatory Sorting model with a unique profile and the Non-Compensatory Sorting model with a unique set of sufficient coalitions. For each variant, we start by extending the formulation based on a separation principle to the multiple category case. Moreover, we extend the two formulations to handle inconsistency in the preference information using the Maximum satisfiability problem language. A computational study is proposed to compare the efficiency of both formulations to learn the two Non-Compensatory Sorting models (with a unique profile and with a unique set of sufficient coalitions) from noiseless and noisy preference information.

Keywords: Multiple criteria analysis; Non-compensatory sorting; Preference learning; SAT/MaxSAT (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:298:y:2022:i:3:p:979-1006

DOI: 10.1016/j.ejor.2021.08.017

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