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An imperialist competition algorithm using a global search strategy for physical examination scheduling.

Hui Yu1, Jun-Qing Li1,2, Lijing Zhang3

  • 1School of Information Science and Engineering, Shandong Normal University, Jinan, China.

Applied Intelligence (Dordrecht, Netherlands)
|November 12, 2021
PubMed
Summary

Effective physical examination scheduling is crucial, especially during health crises. A new flexible job shop scheduling model and the imperialist competition algorithm with global search strategy (ICA_GS) optimize patient flow and reduce NP-hard scheduling challenges.

Keywords:
Flexible job shop schedulingGlobal searchImperialist competition algorithmLocal searchPhysical examination schedulingSetup timeTransportation time

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Area of Science:

  • Operations Research
  • Healthcare Management
  • Computer Science

Background:

  • The COVID-19 pandemic underscored the need for efficient physical examination scheduling.
  • Patient treatment times are uncertain, making scheduling a complex NP-hard problem.

Purpose of the Study:

  • To develop an effective scheduling model and algorithm for physical examinations.
  • To address the NP-hard nature of optimizing patient examination sequences.

Main Methods:

  • Introduced a complex flexible job shop scheduling model.
  • Developed a novel imperialist competition algorithm with global search strategy (ICA_GS).
  • Incorporated patient processing, equipment transportation, and setup times into the model.

Main Results:

  • The ICA_GS algorithm demonstrated superior performance in solving the physical examination scheduling problem.
  • Embedded local and global search strategies enhanced algorithm efficiency and prevented local optima.
  • Simulations verified the algorithm's effectiveness in optimizing examination processes.

Conclusions:

  • The proposed ICA_GS algorithm offers a robust solution for complex physical examination scheduling.
  • This approach can improve healthcare operational efficiency and patient throughput.
  • Optimized scheduling is vital for managing patient flow during public health emergencies.