Open Access

ARTICLE

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

Xingli Wu^{1,{{sup}}#{{/sup}}}, Huchang Liao^{1,{{sup}}#{{/sup}}}, Shuxian Sun¹, Zhengjun Wan^2,*

1 Business School, Sichuan University, Chengdu, 610064, China
2 National Institute of Measurement and Testing Technology, Chengdu, 610021, China

* Corresponding Author: Zhengjun Wan. Email:

(This article belongs to the Special Issue: Linguistic Approaches for Multiple Criteria Decision Making and Applications)

Computer Modeling in Engineering & Sciences 2024, 139(3), 3125-3146. https://doi.org/10.32604/cmes.2023.047031

Received 22 October 2023; Accepted 16 November 2023; Issue published 11 March 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 decision-makers 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-makers merely 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 0–1 mixed 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.

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