Decision making could be an extremely complicated process. Especially, in situations in which the diversity of backgrounds and experience of decision makers (DMs) may require various forms to model their opinions. DMs' rational choice even may be distorted by their judgements due to limitations of human cognitive competence. Generally, the interactions between criteria, the bipolar scale with negative values, and linguistic-valued aspirations on criteria are also needed to be addressed. In addition, several types of input information may be stochastic or uncertain, such as criteria evaluations, capacities, and preference coefficients. Our aim is to address all previous issues simultaneously by proposing a novel model that incorporates Choquet integral, bicapacity, regret theory, and stochastic multiobjective acceptability analysis (SMAA), i.e., SMAA-bicapacity-Choquet-regret for heterogeneous linguistic multiple criteria decision-making (MCDM) problems with interactive criteria with bipolar scale and 2-tuple aspirations. The heterogeneous linguistic information can offer a highly flexible way to express DMs' preference; a novel 2-tuple aspirations-based utility within regret can better reflect the subjective perceptions of DMs in MCDM problems with 2-tuple aspirations on criteria; bicapacity can weigh the criteria and their coalitions, and Choquet integral concerning bicapacity can aggregate interactive criteria with bipolar scale; and finally, an application to evaluate medical waste disposal technologies is given, and a comparative analysis is performed to verify the applicability and effectiveness of the proposed model.

SMAA-Bicapacity-Choquet-Regret Model for Heterogeneous Linguistic MCDM With Interactive Criteria With Bipolar Scale and 2-Tuple Aspirations / Zhao, Q.; Ju, Y.; Martinez, L.; Pedrycz, W.; Dong, P.; Wang, A.. - In: IEEE TRANSACTIONS ON FUZZY SYSTEMS. - ISSN 1063-6706. - 30:10(2022), pp. 4384-4398. [10.1109/TFUZZ.2022.3149401]

SMAA-Bicapacity-Choquet-Regret Model for Heterogeneous Linguistic MCDM With Interactive Criteria With Bipolar Scale and 2-Tuple Aspirations

Zhao Q.;
2022

Abstract

Decision making could be an extremely complicated process. Especially, in situations in which the diversity of backgrounds and experience of decision makers (DMs) may require various forms to model their opinions. DMs' rational choice even may be distorted by their judgements due to limitations of human cognitive competence. Generally, the interactions between criteria, the bipolar scale with negative values, and linguistic-valued aspirations on criteria are also needed to be addressed. In addition, several types of input information may be stochastic or uncertain, such as criteria evaluations, capacities, and preference coefficients. Our aim is to address all previous issues simultaneously by proposing a novel model that incorporates Choquet integral, bicapacity, regret theory, and stochastic multiobjective acceptability analysis (SMAA), i.e., SMAA-bicapacity-Choquet-regret for heterogeneous linguistic multiple criteria decision-making (MCDM) problems with interactive criteria with bipolar scale and 2-tuple aspirations. The heterogeneous linguistic information can offer a highly flexible way to express DMs' preference; a novel 2-tuple aspirations-based utility within regret can better reflect the subjective perceptions of DMs in MCDM problems with 2-tuple aspirations on criteria; bicapacity can weigh the criteria and their coalitions, and Choquet integral concerning bicapacity can aggregate interactive criteria with bipolar scale; and finally, an application to evaluate medical waste disposal technologies is given, and a comparative analysis is performed to verify the applicability and effectiveness of the proposed model.
2022
30
10
4384
4398
SMAA-Bicapacity-Choquet-Regret Model for Heterogeneous Linguistic MCDM With Interactive Criteria With Bipolar Scale and 2-Tuple Aspirations / Zhao, Q.; Ju, Y.; Martinez, L.; Pedrycz, W.; Dong, P.; Wang, A.. - In: IEEE TRANSACTIONS ON FUZZY SYSTEMS. - ISSN 1063-6706. - 30:10(2022), pp. 4384-4398. [10.1109/TFUZZ.2022.3149401]
Zhao, Q.; Ju, Y.; Martinez, L.; Pedrycz, W.; Dong, P.; Wang, A.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1309388
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