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

Abstract : 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 (\lnot A)LNS. We also compare our results with the ones obtained with a 0\textendash 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 (\lnot A)LNS with a gain of up to 18.34% for some scenarios. \textcopyright 2021, IFIP International Federation for Information Processing.
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Contributeur : Louise DESSAIVRE Connectez-vous pour contacter le contributeur
Soumis le : mercredi 1 juin 2022 - 19:36:55
Dernière modification le : dimanche 21 août 2022 - 13:38:21




G. Olivier, Corinne Lucet, Laure Brisoux-Devendeville, D. Sylvain. An Adaptive Large Neighborhood Search Method to Plan Patient's Journey in Healthcare. IFIP Advances in Information and Communication Technology, 2021, 631 IFIP, pp.289--297. ⟨10.1007/978-3-030-85902-2_31⟩. ⟨hal-03685098⟩



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