An Adaptive Large Neighborhood Search Method to Plan Patient's Journey in Healthcare - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

An Adaptive Large Neighborhood Search Method to Plan Patient's Journey in Healthcare

Résumé

In this paper an adaptation of the Adaptive Large Neighborhood Search (ALNS) to a patient’s care planning problem is proposed. We formalize it as an RCPSP problem that consists of assigning a start date and medical resources to a set of medical appointments. Different intensification and diversification movements for the ALNS are presented. We test this approach on real-life problems and compare the results of ALNS to a version without the adaptive layer, called (¬A)LNS. We also compare our results with the ones obtained with a 0–1 linear programming model. On small instances, ALNS obtains results close to optimality, with an average difference of 1.39 of solution quality. ALNS outperforms (¬A)LNS with a gain of up to 18.34% for some scenarios.
Fichier principal
Vignette du fichier
520755_1_En_31_Chapter.pdf (380.18 Ko) Télécharger le fichier
Origine : Accord explicite pour ce dépôt
Licence : CC BY - Paternité

Dates et versions

hal-03611359 , version 1 (13-09-2023)

Licence

Paternité

Identifiants

Citer

Olivier Gerard, Corinne Lucet, Laure Brisoux Devendeville, Sylvain Darras. An Adaptive Large Neighborhood Search Method to Plan Patient's Journey in Healthcare. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.289-297, ⟨10.1007/978-3-030-85902-2_31⟩. ⟨hal-03611359⟩
143 Consultations
7 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More