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

G. Olivier (1) , Corinne Lucet (1) , Laure Brisoux-Devendeville (1) , D. Sylvain

1 MIS - Modélisation, Information et Systèmes - UR UPJV 4290

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.

Domaines: Informatique [cs]

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⟩