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Balancing common disease treatment and epidemic control is a key objective ofmedical supplies procurement in hospitals during a pandemic such as COVID-19.This problem can be formulated as a bi-objective optimization problem forsimultaneously optimizing the effects of common disease treatment and epidemiccontrol. However, due to the large number of supplies, difficulties inevaluating the effects, and the strict budget constraint, it is difficult forexisting evolutionary multiobjective algorithms to efficiently approximate thePareto front of the problem. In this paper, we present an approach that firsttransforms the original high-dimensional, constrained multiobjectiveoptimization problem to a low-dimensional, unconstrained multiobjectiveoptimization problem, and then evaluates each solution to the transformedproblem by solving a set of simple single-objective optimization subproblems,such that the problem can be efficiently solved by existing evolutionarymultiobjective algorithms. We applied the transform-and-divide evolutionaryoptimization approach to six hospitals in Zhejiang Province, China, during thepeak of COVID-19. Results showed that the proposed approach exhibitssignificantly better performance than that of directly solving the originalproblem. Our study has also shown that transform-and-divide evolutionaryoptimization based on problem-specific knowledge can be an efficient solutionapproach to many other complex problems and, therefore, enlarge the applicationfield of evolutionary algorithms.

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