The bed assignment problem here addressed consists in assigning elective patients to beds by considering several requirements, such as patient clinical conditions and personal preferences, medical needs, bed availability in departments, length of stay and competing requests for beds. The rather complex combinatorial structure of the problem compels finding a solution for effective and efficient decision-making tools to support bed managers in making fast and accurate decisions. In this paper, we design and develop combinatorial optimization models for supporting the bed assignment decision-making process. Since the problem is NP-hard, in order to solve the models efficiently, we propose and motivate a matheuristic solution framework based on a re-optimization approach. The matheuristic is implemented and tested on literature-based benchmark instances. It shows impressive computational performance for all the benchmark instances and the results improve all the best-known bounds of the state-of-the-art.

An efficient matheuristic for offline patient-to-bed assignment problems

Guido, Rosita;GROCCIA, MARIA CARMELA;Conforti, Domenico
2018-01-01

Abstract

The bed assignment problem here addressed consists in assigning elective patients to beds by considering several requirements, such as patient clinical conditions and personal preferences, medical needs, bed availability in departments, length of stay and competing requests for beds. The rather complex combinatorial structure of the problem compels finding a solution for effective and efficient decision-making tools to support bed managers in making fast and accurate decisions. In this paper, we design and develop combinatorial optimization models for supporting the bed assignment decision-making process. Since the problem is NP-hard, in order to solve the models efficiently, we propose and motivate a matheuristic solution framework based on a re-optimization approach. The matheuristic is implemented and tested on literature-based benchmark instances. It shows impressive computational performance for all the benchmark instances and the results improve all the best-known bounds of the state-of-the-art.
Combinatorial optimization; Matheuristic; Patient bed assignment; Scheduling; Modeling and Simulation; Management Science and Operations Research; Information Systems and Management
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/277076
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