Fair allocation of personal protective equipment to health centers during early phases of a pandemic

We consider the problem of allocating personal protective equipment, namely surgical and respiratory masks, to health centers under extremely limited supply. We formulate a multi-objective multi-period non-linear resource allocation model for this problem with the objectives of minimizing the number...

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Veröffentlicht in:Computers & operations research Jg. 141; S. 105690
Hauptverfasser: Dönmez, Zehranaz, Turhan, Serkan, Karsu, Özlem, Kara, Bahar Y., Karaşan, Oya
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 01.05.2022
Pergamon Press Inc
Schlagworte:
Algorithms
Breathing masks
Comparative analysis
Cost function
COVID-19
Deprivation
Epsilon-constraint algorithm
Equity
Fairness
Genetic algorithm
Genetic algorithms
Health facilities
Healthcare resource allocation
Heuristic methods
Operations research
Pandemic
Personal protective equipment
Personal protective equipment allocation
Policies
Resource allocation
Surgical equipment
COVID-19
Epsilon-constraint algorithm
Healthcare resource allocation
Genetic algorithm
Equity
Pandemic
Personal protective equipment allocation
Fairness
ISSN:0305-0548, 1873-765X, 0305-0548
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Zusammenfassung:We consider the problem of allocating personal protective equipment, namely surgical and respiratory masks, to health centers under extremely limited supply. We formulate a multi-objective multi-period non-linear resource allocation model for this problem with the objectives of minimizing the number of infected health workers, the number of infected patients and minimizing a deprivation cost function defined over shortages. We solve the resulting problem using the ε-constraint algorithm so as to obtain the exact Pareto set. We also develop a customized genetic algorithm to obtain an approximate Pareto frontier in reasonable time for larger instances. We provide a comparative analysis of the exact and heuristic methods under various scenarios and give insights on how the suggested allocations outperform the ones obtained through a set of rule-of-thumb policies, policies that are implemented owing to their simplicity and ease-of-implementation. Our comparative analysis shows that as the circumstances get worse, the trade-off between the deprivation cost and the ratio of infections deepens and that the proposed heuristic algorithm gives very close solutions to the exact Pareto frontier, especially under pessimistic scenarios. We also observed that while some rule-of-thumb policies such as a last-in-first-receives type policy work well in terms of deprivation costs in optimistic scenarios, others like split policies perform well in terms of number of infections under neutral or pessimistic settings. While favoring one of the objectives, these policies typically fail to provide good solutions in terms of the other objective; hence if such policies are to be implemented the choice would depend on the problem characteristics and the priorities of the policy makers. Overall, the solutions obtained by the proposed methods imply that more complicated distribution schemes that are not induced by these policies would be needed for best results. •Allocation of personal protective equipment to hospitals during a pandemic is studied.•A bi-objective non-linear multi-period mathematical model is proposed.•A set of allocation policies and a customized genetic algorithm are developed.•Solutions for exact and approximate Pareto fronts are compared.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2021.105690