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A two-stage emergency supplies procurement model based on prospect multi-attribute three-way decision.

Authors :
Jia, Fan
Wang, Yujie
Liu, Yuanyuan
Source :
International Journal of Machine Learning & Cybernetics; Dec2024, Vol. 15 Issue 12, p5895-5919, 25p
Publication Year :
2024

Abstract

Emergency supply chain management has recently drawn growing attention of managers and researchers with frequent appearance of pandemics, disasters and safety accidents. Previous studies proposed methods for supplier selection and order allocation, while they cannot satisfy the demand for emergency supplies as emergency events bring many uncertainties and risks in supply chain disruption. To guarantee the efficiency in emergency supplies procurement, this work aims at putting forward a two-stage approach for emergency supplier selection and order allocation by use of three-way decision and fuzzy multi-objective optimization. Firstly, by considering the perceived utilities and perceived losses of purchasing process simultaneously, a prospect profit-based three-way decision model is established. Next, the prospect multi-attribute three-way decision model for emergency supplier selection is proposed, constructing the calculation approaches of thresholds, conditional probabilities as well as decision rules. Thirdly, inspired by perceived utilities and perceived losses of supplies purchasing, the utility-based objective function and loss-based objective function are introduced to multi-objective optimization model for order allocation. Finally, a real case of government emergency supplies procurement is discussed to show the applicability and effectiveness of the proposed approach. The final results of the proposed methodology show that it can effectively manage data with uncertainty, determine the qualified suppliers as well as alternative suppliers simultaneously to prevent emergency supply chain disruption, and provide satisfactory solutions for order allocation by introducing different combinations of objective functions according to decision makers' preference. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18688071
Volume :
15
Issue :
12
Database :
Complementary Index
Journal :
International Journal of Machine Learning & Cybernetics
Publication Type :
Academic Journal
Accession number :
180589101
Full Text :
https://doi.org/10.1007/s13042-024-02291-4