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A room-oriented artificial bee colony algorithm for optimizing the patient admission scheduling problem.

Asaju La'aro Bolaji1, Akeem Femi Bamigbola2, Lawrence Bunmi Adewole3

  • 1Department of Computer Science, Faculty of Pure and Applied Sciences, Federal University Wukari, P. M. B. 1020, Wukari, Taraba State, Nigeria.

Computers in Biology and Medicine
|July 28, 2022
PubMed
Summary

This study introduces an Artificial Bee Colony Algorithm (ABC) to solve the complex patient admission scheduling (PAS) problem. The proposed ABC algorithm demonstrates superior performance, achieving optimal solutions across all tested datasets compared to existing methods.

Keywords:
Artificial Bee ColonyMetaheuristicsPatient admission schedulingPopulation-based methodTimetabling

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Healthcare Management

Background:

  • Patient Admission Scheduling (PAS) is a complex combinatorial optimization problem with significant real-world implications in healthcare.
  • Existing population-based algorithms often struggle to find high-quality solutions for the PAS problem, highlighting the need for improved methods.
  • The discrete nature of PAS requires adaptation of continuous optimization algorithms.

Purpose of the Study:

  • To propose and evaluate an Artificial Bee Colony Algorithm (ABC) for solving the patient admission scheduling problem.
  • To adapt the continuous ABC algorithm to handle the discrete and rugged solution space characteristic of PAS.
  • To compare the performance of the proposed ABC algorithm against a wide range of existing methods.

Main Methods:

  • An Artificial Bee Colony Algorithm (ABC), a swarm intelligence metaheuristic, is adapted for the PAS problem.
  • A room-oriented approach is used to generate initial feasible solutions.
  • Three neighborhood structures are integrated into the ABC algorithm's employed and onlooker bee operators to optimize solutions.

Main Results:

  • The proposed ABC algorithm achieved the best results across all tested benchmark datasets when compared to five population-based methods.
  • It outperformed eleven heuristic and hyperheuristic-based methods in five instances.
  • The ABC algorithm demonstrated competitive performance against three integer programming methods.

Conclusions:

  • The adapted Artificial Bee Colony Algorithm (ABC) is a highly effective method for solving the patient admission scheduling (PAS) problem.
  • The algorithm's ability to handle discrete solution spaces and its superior performance make it a valuable tool for healthcare scheduling.
  • The proposed ABC algorithm offers a promising new template for addressing PAS challenges within the healthcare community.