Interval Type-2 Fuzzy Programming Method for Risky Multicriteria Decision Making with Heterogeneous Relationship

Date

2021-11-04

Advisors

Journal Title

Journal ISSN

ISSN

0020-0255

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

Abstract

We propose a new interval type-2 fuzzy (IT2F) programming method for risky multicriteria decision-making (MCDM) problems with IT2F truth degrees, where the criteria exhibit a heterogeneous relationship and decision-makers behave according to bounded rationality. First, we develop a technique to calculate the Banzhaf-based overall perceived utility values of alternatives based on 2-additive fuzzy measures and regret theory. Subsequently, considering pairwise comparisons of alternatives with IT2F truth degrees, we define the Banzhaf-based IT2F risky consistency index (BIT2FRCI) and the Banzhaf-based IT2F risky inconsistency index (BIT2FRII). Next, to identify the optimal weights, an IT2F programming model is established based on the concept that BIT2FRII must be minimized and must not exceed the BIT2FRCI using a fixed IT2F set. Furthermore, we design an effective algorithm using an external archive-based constrained state transition algorithm to solve the established model. Accordingly, the ranking order of alternatives is derived using the Banzhaf-based overall perceived utility values. Experimental studies pertaining to investment selection problems demonstrate the state-of-the-art performance of the proposed method, that is, its strong capability in addressing risky MCDM problems.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Risky multicriteria decision making, Heterogeneous relationship, Evolutionary computation, Interval type-2 fuzzy set, 2-additive fuzzy measure, Regret theory

Citation

Tang, G., Long, J., Teng, F., Chiclana, F., Gu, X., Liu, P. (2021) Interval Type-2 Fuzzy Programming Method for Risky Multicriteria Decision Making with Heterogeneous Relationship. Information Sciences, 584, pp.184-211.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)