Abstract
ABSTRACTIn this paper, a generalized network optimization model is developed for a complex blood supply chain including a regionalized blood bank system. This system consist of collection sites, testing and processing facilities, storage facilities, distribution centers, as well as points of demand (hospitals). To keep the network in contradiction of the uncertainty, a consolidated approach based on a recently developed stochastic robust approach is extended. An accelerated stochastic Benders decomposition algorithm is proposed to solve the problem modeled in this paper. To speed up the convergence of the solution algorithm, valid inequalities are introduced to get better quality lower bounds. Numerical illustrations are given to verify the mathematical formulation and also to show the benefits of using the stochastic robust approach. At the end, the performance improvements attained by the valid inequalities and the Pareto-optimal cuts are demonstrated in a real-world application.
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