A hybrid algorithm of Simulated Annealing (SA) and flower pollination algorithm was presented to overcome the problems of low-accuracy computation, slow-speed convergence and being easily relapsed into local extremum. The sudden jump strategy in SA was utilized to avoid falling into local optimum, and the global searching performance of SA was exploited to enhance the global searching ability of the hybrid algorithm. The hybrid algorithm was tested through six standard functions and compared to basic Flower Pollination Algorithm (FPA), Bat Algorithm (BA), Particle Swarm Optimization (PSO) algorithm and improved PSO algorithm. The simulation results show that the optimal value of 4 functions were found by the hybrid algorithm with better convergence precision, convergence rate and robustness. At the same time, the experimental results of solving nonlinear equation group verify the validity of the hybrid algorithm.

Concerning the perspective of supply chain integration, a blood supply model was developed, which aimed to minimize the blood acquisition risk, system operation cost, the punishment for both excessive and insufficient acquisition by the multi-objective programming method. Taking into account the feature that the amount of expired blood is proportional to time, as well as the cost for expired blood processing, a regional supply and demand equilibrium model characterized by stochastic demand of the four types of blood was built. The model was proved to be convex, and the variational inequality of the blood supply and demand network equilibrium was derived. By modified quasi-Newton method, the solutions of the blood supply chain supply and demand equilibrium under stochastic demand condition were obtained. Finally, a case study in Chengdu verified the model's applicability.