An Optimal Algorithm to Maximize Vaccine Production in a Distributed Environment

Abstract

Pandemics have a devastating effect on lives across the globe as evinced by the ongoing COVID-19. Production of vaccine, medicines and other critical supplies such as oxygen are indispensable and are to be expedited. This work examines the problem of maximizing the manufacturing of critical supplies within a target stipulated time by various manufacturing units. Two scenarios are considered in vaccine production: centralized and distributed. In the former case, the manufacturing units are closely located and transport the raw materials with no delay. In the latter case, the units are located geographically apart and transport of raw materials takes a finite time causing delays in vaccine production. The main contribution of the paper lies in the elucidation of optimal algorithms for both scenarios that take the dependencies between the ingredients into consideration. The dependencies have a constraining effect on the order of manufacturing of both the raw materials and the end products referred to as targets and hence careful consideration is necessary to maximize production. The optimality is measured by an incentive-based scheme that seeks to reward the faster development of targets.