ENHANCING MEDICAL OFFICER SCHEDULING IN HEALTHCARE ORGANIZATIONS: A COMPREHENSIVE INVESTIGATION OF GENETIC AND GOOGLE OR TOOLS ALGORITHMS FOR MULTI-PROJECT RESOURCE-CONSTRAINED OPTIMIZATION

Yıl 2024, Cilt: 8 Sayı: 1, 92 - 103, 30.04.2024

Osama Burak Elhalid , Ali Hakan Isık

https://doi.org/10.46519/ij3dptdi.1415512

Öz

In healthcare organizations, medical staff scheduling is vital to achieving optimal patient care, ensuring the well-being of medical officers, and the efficiency of operations. This research aims to address the challenges of optimizing the scheduling of limited resources for multiple projects for medical staff, through a comparative analysis of Google OR tools and genetic algorithms. We evaluate the performance of these tools in various scenarios, taking into account factors such as overtime, work balance, and scheduling efficiency. This comparative analysis reveals the strengths and weaknesses of each approach, facilitating the development of improved medical staff scheduling solutions. Additionally, we offer algorithmic optimizations tailored to meet the requirements of specific healthcare settings, which contribute to enhancing the adaptability and effectiveness of scheduling tools. The research findings provide valuable insights to guide decision-making in healthcare institutions, ultimately aiming to enhance the quality of care provided by medical officers and improve the overall efficiency of the healthcare system. In conclusion, the results show that the modified Google OR algorithm significantly outperforms the Google OR tools and the regular genetic algorithm in performance.

Anahtar Kelimeler

Medical officer scheduling, Multi-project resource-constrained scheduling, Genetic algorithms, Google OR Tools, Algorithm comparison.

Kaynakça

• 1. Google Corporation, “Google Developers”, http://developers.google.com/, January 9, 2024.
• 2. GeeksforGeeks, “Genetic Algorithms”, https://www.geeksforgeeks.org/genetic-algorithms/. January 9, 2024.
• 3. Static, U., Jacko, P., & Kirkbride, C., “Performance evaluation of scheduling policies for the dynamic and stochastic resource-constrained multi-project scheduling problem “, International Journal of Production Research, Vol. 60, Issue 4, Pages 1411-1423, 2022.
• 4. Browning, T. R., Yassine, A. A., “Resource-constrained multi-project scheduling: Priority rule performance revisited“, International Journal of Production Economics, Vol. 126, Issue 2, Pages 212-228, 2010.
• 5. Fischer, F. M., Borges, F. N., Rotenberg, L., Latorre, M. R., Soares, N. S., Rosa, P. L., Teixeira, L. R., Nagai, R., Steluti, J., Landsbergis, P., “Workability of health care shift workers: What matters? “, Chronobiol Int., Vol. 23, Issue 6, Pages 1165-79, 2006.
• 6. Mahmud, F., “Evolutionary Algorithms for Resource Constrained Project Scheduling Problems “, Doctoral Thesis, UNSW University, Sydney, 2023.
• 7. El-Abbasy, M. S. K., “Multi-objective multi-project construction scheduling optimization“, Doctoral Thesis, Concordia University, Montreal, 2015.
• 8. Chen, R., Liang, C., Gu, D., Leung, J. Y., “A multi-objective model for multi-project scheduling and multi-skilled staff assignment for IT product development considering competency evolution “, International Journal of Production Research, Vol. 55, Issue 21, Pages 6207-6234, 2017.
• 9. De Boer, R., “Resource-constrained multi-project management”, Doctoral Thesis, University of Twente, Netherlands, 1998.
• 10. Kannimuthu, M., Raphael, B., Ekambaram, P., Kuppuswamy, A., “Comparing optimization modeling approaches for the multi-mode resource-constrained multi-project scheduling problem “, Engineering, Construction and Architectural Management, Vol. 27, Issue 4, Pages 893-916, 2020.
• 11. Browning, T. R., & Yassine, A. A., “A random generator of resource-constrained multi-project network problems “, Journal of Scheduling, Vol. 13, Issue 1, Pages 143-161, 2010.
• 12. Badawiyeh, B. H., “The effect of planning and resource leveling on UAE contractors”, Doctoral Thesis “, The British University, Dubai, 2010.
• 13. Cadorin, D., Darwish, R., “Decision-making biases in project portfolio selection and prioritization: An exploratory study of the rationale behind decision making leading to project portfolio problems “, Master Thesis, Umea University, Sweden, 2015.
• 14. Zhou, Q., Li, J., Dong, R., Zhou, Q., & Yang, B., “Optimization of multi-execution modes and multi-resource-constrained offshore equipment project scheduling based on a hybrid genetic algorithm “, Computer Modeling in Engineering & Sciences, Vol. 134, Issue 2, Pages 1263-1281, 2023.
• 15. Workable, Medical Administrative Assistant job description. https://www.workable.com, January 9, 2024
• 16. IEEE Xplore, Multi-Mode Project Scheduling with Limited Resource and Budget Constraints, https://ieeexplore.ieee.org, January 9, 2024
• 17. Typeset. Multiproject Scheduling with Limited Resources: A Zero-One Programming Approach, https://www.typeset.io, January 9, 2024
• 18. Springer, Multi-project scheduling with two-stage decomposition, https://www.springer.com, January 9, 2024
• 19. Wikipedia, Genetic algorithm, https://www.wikipedia.org, January 9, 2024
• 20. GeeksforGeeks, Genetic Algorithms, https://www.geeksforgeeks.org, January 9, 2024
• 21. OpenDSA, Comparing Algorithms, https://opendsa.io, January 9, 2024
• 22. Study Algorithms, How do you compare the two algorithms?, https://www.studyalgorithms.com, January 9, 2024
• 23. Baeldung, How to Compare Two Algorithms Empirically?, https://www.baeldung.com, January 9, 2024
• 24. Wikibooks, Problem Solving: Comparing algorithms, https://www.wikibooks.org, January 9, 2024