Revisiting Gini for equitable humanitarian logistics. (August 2022)

• Record Type: Journal Article
• Authors: Alem, Douglas; Caunhye, Aakil M.; Moreno, Alfredo
• Is Part Of: Socio-economic planning sciences. Number 82(2022)Part B
• Publication Date: 2022-08
• Subjects: Gini -- Equity/Fairness -- Humanitarian Logistics -- Location-Allocation -- Disaster Relief -- Lorenz Curve -- Clustering; Planning -- Periodicals; Economic policy -- Periodicals; Social policy -- Periodicals; Planification -- Périodiques; Politique économique -- Périodiques; Politique sociale -- Périodiques; ECONOMIC PLANNING; SOCIAL PLANNING; DECISION-MAKING; 361
• DOI: 10.1016/j.seps.2022.101312
• Languages: English
• ISSNs: 0038-0121

Abstract

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 they 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 a 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. Highlights: We present a new formulation to optimize effectiveness-equity. Gini is used to model equity based on the Lorenz curve. Valid inequalities are derived. A clustering-based approach is also presented. The approach is validated through a real case-study based on Brazil.