Optimization of Organ Transplantation Logistics from an Operations Research Linear Programming Perspective

Abstract

Logistics is crucial in organ transplantation surgeries, focusing on maximizing donor-recipient matches within organ survival timeframes. This study explores the impact of clustering structures on organ matches, utilizing dynamic analysis and novel mathematical models to evaluate transportation modes and prioritize intra-regional organ transplantation while meeting time constraints. Initially concentrating on maximizing flow within a single timeframe, the study addresses disparate outcomes across organ types by developing an additional model for maximizing flow across multiple organ types. Computational analyses and diverse performance metrics underscore the potential for enhancing organ transplantation networks through re-clustering strategies.