In this research, a novel application of operation research in healthcare is developed. In recent years, operation research has played a significant role in planning healthcare and medical services. In other words, here, a hybrid healthcare-disaster problem is studied which somewhat distinguishes the characteristics of proposed problem from other issues in supply chain . In details, blood collection and distribution in disaster is considered. For this purpose, a mixed integer mathematical model is developed for location allocation routing problem for blood collection and distribution is disasters. The proposed model make decisions such as optimal number and location of collection and distribution facilities, optimal allocation of facilities to the regions and optimal routes for blood collection and distribution by vehicles. The proposed mathematical model has two objective function: the first aims in minimizing blood units shortage (minimizing losses) and the second one minimizes total costs of supply chain. Also, to deal with data uncertainty in disasters, robust optimization approach is utilized and robust counterpart of the problem is developed. To solve the problem in small size with exact method, constraint method is used. The method is implemented for both deterministic and robust counterpart model and obtained results is compared. The results have shown that developed robust counterpart has appropriate efficiency. Since the problem has complexity of NP-Complete at least, non-dominated sorting genetic algorithm (NSGA II) and multi-objective variable neighborhood search algorithm (MOVNS) is used to solve large size problems. In NSGA II, a heuristic algorithm is used to generate initial solutions. Also, to improve the solution quality in NSGA II, Taguci method in design of experiments is utilized for adjusting the parameters of algorithm. Then, a number of sample instances are generated randomly in small and large size to evaluate and compare the presented algorithms. Computational results are indicated that MOVNS performs highly effective in comparison with NSGA II. Finally, sensitivity analysis in done on some critical parameters and the results are presented.