Balancing Common Treatment and Epidemic Control in Medical Procurement during COVID-19: Transform-and-Divide Evolutionary Optimization

TL;DR

This paper introduces a transform-and-divide evolutionary optimization method to efficiently balance medical supplies procurement for disease treatment and epidemic control during COVID-19, outperforming traditional algorithms.

Contribution

The paper proposes a novel transform-and-divide approach that simplifies high-dimensional constrained multiobjective problems for better evolutionary optimization performance.

Findings

The method significantly outperforms existing algorithms in hospital procurement scenarios.

Transforming the problem reduces computational complexity and improves solution quality.

The approach is adaptable to other complex multiobjective optimization problems.

Abstract

Balancing common disease treatment and epidemic control is a key objective of medical supplies procurement in hospitals during a pandemic such as COVID-19. This problem can be formulated as a bi-objective optimization problem for simultaneously optimizing the effects of common disease treatment and epidemic control. However, due to the large number of supplies, difficulties in evaluating the effects, and the strict budget constraint, it is difficult for existing evolutionary multiobjective algorithms to efficiently approximate the Pareto front of the problem. In this paper, we present an approach that first transforms the original high-dimensional, constrained multiobjective optimization problem to a low-dimensional, unconstrained multiobjective optimization problem, and then evaluates each solution to the transformed problem by solving a set of simple single-objective optimization subproblems, such that the problem can be efficiently solved by existing evolutionary multiobjective algorithms. We applied the transform-and-divide evolutionary optimization approach to six hospitals in Zhejiang Province, China, during the peak of COVID-19. Results showed that the proposed approach exhibits significantly better performance than that of directly solving the original problem. Our study has also shown that transform-and-divide evolutionary optimization based on problem-specific knowledge can be an efficient solution approach to many other complex problems and, therefore, enlarge the application field of evolutionary algorithms.