A bi-objective MILP model for pandemic vaccine allocation and distribution with behavioral adaptation

Purpose

The purpose of this study is to develop a comprehensive optimization framework for pandemic vaccine allocation and distribution that minimizes both distribution costs and disease-induced mortality. The work addresses the need for timely, equitable, and logistically feasible vaccine delivery, particularly in settings with constrained resources and infrastructure.

Design/methodology/approach

A bi-objective, multiproduct, multiperiod mixed-integer linear programming (MILP) model is formulated to integrate inter- and intra-provincial routing, cold chain requirements, vehicle availability and maintenance and vaccine spoilage. The model incorporates a piecewise linear vaccination rate that dynamically adjusts based on prior public acceptance trends. A context-informed, scenario-based application inspired by the COVID-19 vaccination campaign in Iran is presented to illustrate the model’s structure and policy-relevant insights, along with sensitivity analyses on vehicle capacity and disease spread rate.

Findings

The model generates distribution and allocation plans that satisfy the modeled cold-chain and fleet constraints and illustrate the tradeoffs between distribution cost, mortality and equity under the assumed epidemiological and behavioral settings. Sensitivity analyses further examine how outcomes vary with transportation capacity and disease spread conditions.

Practical implications

The framework can be adapted to different geographic and epidemiological contexts, guiding ministries of health and humanitarian logistics planners in designing responsive and equitable vaccination strategies.

Originality/value

This study integrates detailed routing, cold chain compatibility, vehicle management and behavioral adaptation into a single optimization framework – features rarely combined in existing literature. It provides policymakers a decision-support modeling framework that has the potential to support pandemic preparedness and response under the modeled assumptions.