The outbreak of SARS-CoV-2 and the corresponding surge in patients with severe symptoms of COVID-19 put a strain on health systems, requiring specialized material and human resources, of- ten exceeding the locally available ones. Motivated by a real emergency response system employed in Northern Italy, we propose a mathematical programming approach for rebalancing the health resources among a network of hospitals in a large geographical area. It is meant for tactical planning in facing foreseen peaks of patients requiring specialized treatment. Our model has a clean combi- natorial structure. At the same time, it allows to include as options the handling of patients by a dedicated home healthcare service, and the efficient exploiting of resource sharing. We introduce mathematical programming heuristic based on decomposition methods and column generation to drive very large scale neighborhood search. We evaluate its embedding in a multi-objective opti- mization framework. We experiment on real world data of the COVID-19 in Northern Italy during 2020, whose aggregation and post processing is made openly available to the community. Our ap- proach proves to be effective in tackling realistic instances, thus making it a reliable basis for actual decision support tools.

On the impact of resource relocation in facing health emergencies / M. Barbato, A. Ceselli, M.L. Premoli. - In: EUROPEAN JOURNAL OF OPERATIONAL RESEARCH. - ISSN 0377-2217. - (2022). [Epub ahead of print] [10.1016/j.ejor.2022.11.024]

On the impact of resource relocation in facing health emergencies

M. Barbato
Primo
;
A. Ceselli
Secondo
;
M.L. Premoli
Ultimo
2022

Abstract

The outbreak of SARS-CoV-2 and the corresponding surge in patients with severe symptoms of COVID-19 put a strain on health systems, requiring specialized material and human resources, of- ten exceeding the locally available ones. Motivated by a real emergency response system employed in Northern Italy, we propose a mathematical programming approach for rebalancing the health resources among a network of hospitals in a large geographical area. It is meant for tactical planning in facing foreseen peaks of patients requiring specialized treatment. Our model has a clean combi- natorial structure. At the same time, it allows to include as options the handling of patients by a dedicated home healthcare service, and the efficient exploiting of resource sharing. We introduce mathematical programming heuristic based on decomposition methods and column generation to drive very large scale neighborhood search. We evaluate its embedding in a multi-objective opti- mization framework. We experiment on real world data of the COVID-19 in Northern Italy during 2020, whose aggregation and post processing is made openly available to the community. Our ap- proach proves to be effective in tackling realistic instances, thus making it a reliable basis for actual decision support tools.
OR in health services; COVID-19; facility location-allocation; mathematical programming
Settore INF/01 - Informatica
Settore MAT/09 - Ricerca Operativa
RL_DG-UNI20GZUCC_01 - Centro operativo dimessi COVID-19 (COD-19) - ZUCCOTTI, GIAN VINCENZO - RL_DG-UNI - Bandi DG Università, ricerca e open innovation - 2020
17-nov-2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/945849
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