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A possibilistic-robust-fuzzy programming model for designing a game theory based blood supply chain network.
Ghasemi, Peiman; Goodarzian, Fariba; Abraham, Ajith; Khanchehzarrin, Saeed.
  • Ghasemi P; Department of Logistics, Tourism & Service Management, German University of Technology in Oman (GUtech), Muscat, Oman.
  • Goodarzian F; Engineering Group, School of Engineering, University of Seville, Camino de los Descubrimientos s/n, Seville 41092, Spain.
  • Abraham A; Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, 11, 3rd Street NW, P.O. Box 2259. Auburn, WA 98071, USA.
  • Khanchehzarrin S; Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran.
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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Full text: Available Collection: 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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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Appl Math Model Year: 2022 Document Type: Article Affiliation country: J.apm.2022.08.003