Fuzzy optimization model for a blood supply network in the post-disaster phase, considering a branch-and-cut approach

Overview

This study develops a fuzzy optimization model for blood supply chain management in the post-disaster context, specifically addressing supply and demand uncertainties exacerbated by the COVID-19 pandemic. The research integrates fuzzy logic with mixed-integer linear programming to enhance decision-making in blood distribution systems across the supply chain continuum from donor collection to medical center distribution points.

Methods and approach

A mixed-integer linear programming model was formulated to represent the blood supply chain network with explicit consideration of uncertainty across supply and demand components. Supply-side uncertainty was modeled using Markov chain methodology to capture the stochastic nature of blood donation availability. Demand uncertainty was represented through fuzzy estimation frameworks reflective of medical center requirements. The objective function was designed to minimize blood product shortages, expiration rates, and operational costs simultaneously. The proposed model was solved using the branch-and-cut algorithm, an exact solution method for integer programming problems.

Results

The model was validated through a case study demonstrating quantifiable improvements across multiple performance metrics. Results indicate cost reduction in the blood supply chain while simultaneously achieving decreased shortage rates and reduced fulfillment timelines for blood-related commodities. The fuzzy optimization framework successfully accommodated the dual sources of uncertainty inherent to post-pandemic blood supply operations.

Implications

The findings suggest that integrated fuzzy-stochastic optimization approaches are viable for managing complex supply chain operations under conditions of structural uncertainty. The demonstrated reduction in shortages and expiration rates indicates potential for improved blood inventory management in healthcare systems facing demand volatility and supply disruption.