Paper #2

Real-World Application of a Stochastic Three-Objective Optimization Model for Greening, Cost Reduction, and Time Management in a Blood Supply Chain Network: A Case Study

Xu Guo

Abstract: Managing a green Blood Supply Chain (BSC) under stochastic demand and perishability constraints presents a critical optimization challenge. This study addresses this by proposing a novel four-echelon, multi-objective Mixed Integer Programming (MIP) model designed to minimize network costs, transportation time, and environmental impacts (waste and emissions) simultaneously. Unlike previous deterministic models, this research incorporates stochastic demand utilizing a chance-constrained programming approach and explicitly models the shelf-life constraints of three distinct blood products (red blood cells, platelets, plasma). To handle the conflicting objectives, the Lp-metric method converts the multi-objective model into a single-objective formulation, enabling efficient solution via the CPLEX solver in GAMS. The model's applicability is validated through a numerical case study, followed by a comprehensive sensitivity analysis on waste ratios and demand fluctuations. Results demonstrate that the proposed framework effectively balances cost-efficiency with sustainability, reducing waste by optimizing flow allocations. Furthermore, the study discusses the computational trade-offs between exact methods and heuristic approaches for large-scale implementations. This research bridges the gap between theoretical green logistics and practical healthcare supply chain management, offering a robust decision-support tool for blood center managers to navigate uncertainties in demand and product shelf-life.

Keywords: Blood Supply Chain; Green Logistics; Lp-metric; Shelf Life; Stochastic Optimization.