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Agricultural Investment Project Decisions Based on an Interactive Preference Disaggregation Model Considering Inconsistency.

Authors :
Xingli Wu
Huchang Liao
Shuxian Sun
Zhengjun Wan
Source :
Computer Modeling in Engineering & Sciences (CMES); 2024, Vol. 139 Issue 3, p3125-3146, 22p
Publication Year :
2024

Abstract

Agricultural investment project selection is a complex multi-criteria decision-making problem, as agricultural projects are easily influenced by various risk factors, and the evaluation information provided by decisionmakers usually involves uncertainty and inconsistency. Existing literature primarily employed direct preference elicitation methods to address such issues, necessitating a great cognitive effort on the part of decision-makers during evaluation, specifically, determining the weights of criteria. In this study, we propose an indirect preference elicitation method, known as a preference disaggregation method, to learn decision-maker preference models from decision examples. To enhance evaluation ease, decision-makersmerely need to compare pairs of alternatives with which they are familiar, also known as reference alternatives. Probabilistic linguistic preference relations are employed to account for the presence of incomplete and uncertain information in such pairwise comparisons. To address the inconsistency among a group of decision-makers, we develop a pair of 0-1mixed integer programming models that consider both the semantics of linguistic terms and the belief degrees of decision-makers. Finally, we conduct a case study and comparative analysis. Results reveal the effectiveness of the proposed model in solving agricultural investment project selection problems with uncertain and inconsistent decision information. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15261492
Volume :
139
Issue :
3
Database :
Complementary Index
Journal :
Computer Modeling in Engineering & Sciences (CMES)
Publication Type :
Academic Journal
Accession number :
176091316
Full Text :
https://doi.org/10.32604/cmes.2023.047031