In this paper, a problem arising from a real-world healthcare application is addressed, dealing with the transportation of biological samples from blood draw centers to a main laboratory. The problem can be modeled as a Vehicle Routing Problem with time-windows and samples lifetime constraints, in which transfers and multiple visits at specific nodes (i.e., the spokes) are allowed to get extra lifetimes of the samples. Mixed Integer Linear Programming formulations and Hybrid Adaptive Large Neighborhood Search (H-ALNS) algorithms have been developed for the problem. Computational experiments on different sets of instances based on real-life data provided by the Local Healthcare Authority of Bologna, Italy, show the effectiveness of the proposed algorithms. More precisely, the H-ALNS algorithms are able to find solutions in which all the samples are delivered on time, unlike the real case where lifetime requirements are usually not fully respected.

Benini, M., Detti, P., & Zabalo Manrique de Lara, G. (2022). Mathematical programming formulations and metaheuristics for biological sample transportation problems in healthcare. COMPUTERS & OPERATIONS RESEARCH, 146 [10.1016/j.cor.2022.105921].

Mathematical programming formulations and metaheuristics for biological sample transportation problems in healthcare

Benini M.;Detti P.;Zabalo Manrique de Lara G.
2022

Abstract

In this paper, a problem arising from a real-world healthcare application is addressed, dealing with the transportation of biological samples from blood draw centers to a main laboratory. The problem can be modeled as a Vehicle Routing Problem with time-windows and samples lifetime constraints, in which transfers and multiple visits at specific nodes (i.e., the spokes) are allowed to get extra lifetimes of the samples. Mixed Integer Linear Programming formulations and Hybrid Adaptive Large Neighborhood Search (H-ALNS) algorithms have been developed for the problem. Computational experiments on different sets of instances based on real-life data provided by the Local Healthcare Authority of Bologna, Italy, show the effectiveness of the proposed algorithms. More precisely, the H-ALNS algorithms are able to find solutions in which all the samples are delivered on time, unlike the real case where lifetime requirements are usually not fully respected.
Benini, M., Detti, P., & Zabalo Manrique de Lara, G. (2022). Mathematical programming formulations and metaheuristics for biological sample transportation problems in healthcare. COMPUTERS & OPERATIONS RESEARCH, 146 [10.1016/j.cor.2022.105921].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/1212976