A Bi-Objective Robust Optimization Model for Emergency Blood Supply Network Design under Uncertainty

Natural and anthropogenic disasters annually give rise to huge financial and human losses. Disasters make a large amount of demand for relief items (e.g. medicine and hygiene products) in the affected area. There is a great deal of importance in appropriate response to demand of these items, as they have a vital role in preserving injured people's life. This paper presents a bi-objective location-allocation model for blood supply under uncertainty. The objectives are minimizing the blood shortage in blood centers and also minimizing the operational costs including cost of transporting temporary blood facilities and the cost of blood collection and transportation in a multi-period context. Due to the lack of information about the real values of some input parameters such as demand, amount of donated blood and also unit operational cost in disaster, these are considered as uncertain parameters in order to make more precise decisions. Robust optimization approach based on box uncertainty set is used to tackle the model uncertainty. Moreover, Lexicographic weighted Tchebycheff method is used to obtain the pareto-optimal solutions. Finally, a numerical example is presented to compare the performance of the robust model and its deterministic counterpart