Harmony Search Algorithm for Patient Admission Scheduling Problem

• Main Authors: Abu Doush Iyad, Al-Betar Mohammed Azmi, Awadallah Mohammed A., Hammouri Abdelaziz I., Al-Khatib Ra’ed M., ElMustafa Saba, ALkhraisat Habes
• Format: Article
• Language: English
• Published: De Gruyter 2018-04-01
• Series: Journal of Intelligent Systems
• Subjects: harmony search algorithm; optimization; patient admission problem; metaheuristic; patient scheduling problem
• Online Access: https://doi.org/10.1515/jisys-2018-0094

The patient admission scheduling (PAS) problem is an optimization problem in which we assign patients automatically to beds for a specific period of time while preserving their medical requirements and their preferences. In this paper, we present a novel solution to the PAS problem using the harmony search (HS) algorithm. We tailor the HS to solve the PAS problem by distributing patients to beds randomly in the harmony memory (HM) while respecting all hard constraints. The proposed algorithm uses five neighborhood strategies in the pitch adjustment stage. This technique helps in increasing the variations of the generated solutions by exploring more solutions in the search space. The PAS standard benchmark datasets are used in the evaluation. Initially, a sensitivity analysis of the HS algorithm is studied to show the effect of its control parameters on the HS performance. The proposed method is also compared with nine methods: non-linear great deluge (NLGD), simulated annealing with hyper-heuristic (HH-SA), improved with equal hyper-heuristic (HH-IE), simulated annealing (SA), tabu search (TS), simple random simulated annealing with dynamic heuristic (DHS-SA), simple random improvement with dynamic heuristic (DHS-OI), simple random great deluge with dynamic heuristic (DHS-GD), and biogeography-based optimization (BBO). The proposed HS algorithm is able to produce comparably competitive results when compared with these methods. This proves that the proposed HS is a very efficient alternative to the PAS problem, which can be efficiently used to solve many scheduling problems of a large-scale data.