A Hybrid Simulated Annealing Approach for the Patient Bed Assignment Problem

We address, in this paper, a very recurring problem within hospitals that consists in assigning elective patients to a limited number of beds. Especially when dealing with patients requiring urgent intervention, this problem becomes more complex and the time factor becomes the most critical. In such situations, a set of patients are to be examined and their clinical states are to be well specified in order to decide whether they need admission and hospitalization or not. In case of hospitalization, the hospital staff should assign patients to beds while taking into account beds availability in terms of specialization and patient needs. All these actions should be well planned in order to maximize the quality of service in the hospitals. This challenging problem can be modeled as an assignment problem that handles a set of patients to be assigned to a set of beds over a given time horizon, while taking into account availability constraints expressed in terms of beds, medical necessity and patient demands. Due to its NP-hardness, the problem is mainly solved using approximate approaches, especially for large-scaled instances. We propose a hybrid simulated annealing approach, combining both advantages of simulated annealing (SA), that provides a local search, and genetic algorithm, that provides a global search, to enhance the performance of SA. The experimental results show that the proposed metaheuristic generates high-quality solutions for several benchmark instances from the literature with regards to the basic simulated annealing approach. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of KES International.