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 0–1 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.
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