Revisiting Gini for equitable humanitarian logistics

TL;DR

This paper revisits the Gini coefficient in humanitarian logistics, deriving its original form for better equity modeling, and demonstrates that alternative definitions can lead to less equitable decisions.

Contribution

It derives the original Gini coefficient from the Lorenz curve for optimization in humanitarian logistics and introduces new inequalities and clustering methods to improve model efficiency.

Findings

Original Gini-based models improve equity in resource allocation.

Alternative Gini definitions can lead to less equitable decisions.

Computational results show the importance of using the original Gini measure.

Abstract

Modeling equity in the allocation of scarce resources is a fast-growing concern in the humanitarian logistics field. The Gini coefficient is one of the most widely recognized measures of inequity and it was originally characterized by means of the Lorenz curve, which is a mathematical function that links the cumulative share of income to rank-ordered groups in a population. So far, humanitarian logistics models that have approached equity using the Gini coefficient do not actually optimize its original formulation, but use alternative definitions that do not necessarily replicate that original Gini measure. In this paper, we derive the original Gini coefficient via the Lorenz curve to optimize the effectiveness-equity trade-off in a humanitarian location-allocation problem. We also propose new valid inequalities based on an upper-bounding Lorenz curve to tighten the linear relaxation of our model and develop a clustering-based construction of the Lorenz curve that requires fewer additional constraints and variables than the original one. The computational study, based on the floods and landslides in Rio de Janeiro state, Brazil, reveals that while alternative Gini definitions have interesting properties, they can generate vastly different decisions compared to the original Gini coefficient. In addition, viewed from the perspective of the original Gini coefficient, these decisions can be significantly less equitable.