A possibilistic-robust-fuzzy programming model for designing a game theory based blood supply chain network.
Appl Math Model
; 112: 282-303, 2022 Dec.
Article
in English
| MEDLINE | ID: covidwho-2060400
ABSTRACT
This paper presents a bi-level blood supply chain network under uncertainty during the COVID-19 pandemic outbreak using a Stackelberg game theory technique. A new two-phase bi-level mixed-integer linear programming model is developed in which the total costs are minimized and the utility of donors is maximized. To cope with the uncertain nature of some of the input parameters, a novel mixed possibilistic-robust-fuzzy programming approach is developed. The data from a real case study is utilized to show the applicability and efficiency of the proposed model. Finally, some sensitivity analyses are performed on the important parameters and some managerial insights are suggested.
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International databases
Database:
MEDLINE
Language:
English
Journal:
Appl Math Model
Year:
2022
Document Type:
Article
Affiliation country:
J.apm.2022.08.003
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