Discrete flower pollination algorithm for patient admission scheduling problem

Abdalkareem ZA 1 , Al-Betar MA 2 , Amir A 3 , Ehkan P 4 , Hammouri AI 5 , Salman OH 6

Affiliations 

  • 1 Faculty of Electronic Engineering Technology, University Malaysia Perlis, 02600 Arau, Perlis, Malaysia; Alimam Aladham University College, Baghdad, Iraq. Electronic address: zahraaadnan@studentmail.unimap.edu.my
  • 2 Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates; Department of Information Technology, Al-Huson University College Al-Balqa Applied University, P.O. Box 50, Al-Huson, Irbid, Jordan. Electronic address: m.albetar@ajman.ac.ae
  • 3 Faculty of Electronic Engineering Technology, University Malaysia Perlis, 02600 Arau, Perlis, Malaysia. Electronic address: amiza@unimap.edu.my
  • 4 Faculty of Electronic Engineering Technology, University Malaysia Perlis, 02600 Arau, Perlis, Malaysia. Electronic address: phaklen@unimap.edu.my
  • 5 Department of Computer Science, Al-Balqa Applied University, Al-Salt, Jordan. Electronic address: Aziz@bau.edu.jo
  • 6 Networking Department, Engineering College, Al Iraqia University, Baghdad, Iraq
Comput Biol Med, 2022 Feb;141:105007.
PMID: 34785077 DOI: 10.1016/j.compbiomed.2021.105007

Abstract

This paper aims to tackle the Patient Admission Scheduling Problem (PASP) using the Discrete Flower Pollination Algorithm (DFPA), a new, meta-heuristic optimization method based on plant pollination. PASP is one of the most important problems in the field of health care. It is a highly constrained and combinatorial optimization problem of assigning patients to medical resources in a hospital, subject to predefined constraints, while maximizing patient comfort. While the flower pollination algorithm was designed for continuous optimization domains, a discretization of the algorithm has been carried out for application to the PASP. Various neighborhood structures have been employed to enhance the method, and to explore more solutions in the search space. The proposed method has been tested on six instances of benchmark datasets for comparison against another algorithm using the same dataset. The prospective method is shown to be very efficient in solving any scheduling problem.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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