A novel two-stage network data envelopment analysis model for kidney allocation problem under medical and logistical...

Area of Science:

• Healthcare Systems
• Operations Research
• Transplantation Medicine

Background:

• Organ transplantation faces challenges due to supply-demand imbalance, leading to patient mortality.
• Kidney transplantation is the most common procedure, with allocation being a critical decision point.
• Uncertainty in medical and logistical data, exacerbated by the COVID-19 pandemic, complicates organ allocation.

Purpose of the Study:

• To develop a novel method for assessing and ranking organ-patient pairs for kidney allocation.
• To address the complexities of kidney allocation under uncertainty and the COVID-19 pandemic.
• To improve the efficiency of the organ transplantation supply chain.

Main Methods:

• Utilized two-stage network data envelopment analysis (DEA).
• Integrated credibility-based chance constraint programming (CCP) for uncertainty handling.
• Developed a novel two-stage fuzzy network data envelopment analysis (TSFNDEA) method.

Main Results:

• The TSFNDEA method effectively assesses efficiency and ranks organ-patient pairs.
• The approach considers both medical and logistical factors within the kidney allocation system.
• A validation algorithm using a real case study confirmed the method's applicability and validity.

Conclusions:

• The developed TSFNDEA method outperforms existing kidney allocation systems.
• The approach offers unique efficiency decomposition capabilities under uncertainty.
• The method provides a robust framework for kidney allocation, adaptable to other network structures.