Robust stochastic multi-choice goal programming for blood collection and distribution problem with real application

摘要

Blood transfusion services are a vital section component of the healthcare system all over the world. Literature on studying and modeling of these systems is surprisingly sparse. In this paper, we expand a generalized network optimization model for the complex supply chain of blood, which is a regionalized blood bank system. In this paper; the purpose of blood is red blood cells (RBC). This system consists of collection sites, testing and processing facilities, storage facilities, distribution centers, as well as points of demand, which are classically, include hospitals. Our major contribution is to develop a novel Hybrid stochastic programming, multi-choice goal programming and robust optimization (SMCGR) approaches to simultaneously model two different types of uncertainties by including stochastic scenarios for total blood donations and polyhedral uncertainty sets for demands. Real numerical studies are implemented to verify our mathematical formulation and also show the benefits of the SMCGR approach. The performance improvements achieved by the valid inequalities and Pareto-optimal cuts are demonstrated in real world application.