A possibilistic-robust-fuzzy programming model for designing a game theory based blood supply chain network - Ghasemi, Peiman; Goodarzian, Fariba; Abraham, Ajith; Khanchehzarrin, Saeed (Applied mathematical modelling, 2022)
•Considering a competitive game for the problem of plasma collection in the context of the outbreak of COVID-19.•Providing the types of donors containing: healed donors, healed donors along with side diseases and healthy donors.•Considering various types of advertisements including social networks, TV ads, and banners to attract more donors.•Considering reserved donors and the possibility of donation during working and nonworking hours.•Suggesting a new mixed possibilistic-robust-fuzzy programming. 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.