Mathematical optimization models for reallocating and sharing health equipment in pandemic situations
Metadatos
Mostrar el registro completo del ítemEditorial
Springer
Materia
Reallocation Sharing policies Robust integer linear programming Math-Heuristic COVID-19
Fecha
2022-09-02Referencia bibliográfica
Blanco, V., Gázquez, R. & Leal, M. Mathematical optimization models for reallocating and sharing health equipment in pandemic situations. TOP (2022). [https://doi.org/10.1007/s11750-022-00643-3]
Patrocinador
Spanish Ministerio de Ciencia e Innovacion, Agencia Estatal de Investigacion/FEDER PID2020-114594GB-C21; Junta de Andalucia SEJ-584 FQM-331 P18-FR-1422 US-1256951 P18-FR-2369; Spanish Government PEJ2018-002962-A; Doctoral Program in Mathematics at the Universidad of Granada; Proyect NetMeetData (Fundacion BBVA - Big Data); IMAG-Maria de Maeztu grant CEX2020-001105-M/AEI; Center for Forestry Research & Experimentation (CIEF); European Commission CIGE/2021/161Resumen
In this paper we provide a mathematical programming based decision tool to optimally
reallocate and share equipment between different units to efficiently equip
hospitals in pandemic emergency situations under lack of resources. The approach is
motivated by the COVID-19 pandemic in which many Heath National Systems were
not able to satisfy the demand of ventilators, sanitary individual protection equipment
or different human resources. Our tool is based in two main principles: (1) Part
of the stock of equipment at a unit that is not needed (in near future) could be shared
to other units; and (2) extra stock to be shared among the units in a region can be
efficiently distributed taking into account the demand of the units. The decisions are
taken with the aim of minimizing certain measures of the non-covered demand in a
region where units are structured in a given network. The mathematical programming
models that we provide are stochastic and multiperiod with different robust
objective functions. Since the proposed models are computationally hard to solve,
we provide a divide-et-conquer math-heuristic approach. We report the results of
applying our approach to the COVID-19 case in different regions of Spain, highlighting
some interesting conclusions of our analysis, such as the great increase of
treated patients if the proposed redistribution tool is applied.