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A multi-criteria approach for hospital capacity analysis

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  • Burdett, Robert
  • Kozan, Erhan

Abstract

Hospitals are critical elements of health care systems and analyzing their capacity and productivity is a very important topic. To perform a system wide analysis of public hospital resources and capacity, a multi-objective optimization (MOO) approach has been proposed. This approach identifies the theoretical capacity of the entire hospital and facilitates a sensitivity analysis, for example of the patient case mix (PCM). It is necessary because the competition for hospital resources, for example between different patient types and hospital units, is highly influential on the hospitals productivity. The MOO approach has been extensively tested on a real life case study and significant worth is shown. In this MOO approach, the epsilon constraint method (ECM) has been utilized. However, for solving real life applications, with a large number of competing objectives, it was necessary to devise new and improved algorithms. In addition, to identify the best solution, a separable programming approach was developed. Multiple optimal solutions are also obtained via the iterative refinement and re-solution of the model.

Suggested Citation

  • Burdett, Robert & Kozan, Erhan, 2016. "A multi-criteria approach for hospital capacity analysis," European Journal of Operational Research, Elsevier, vol. 255(2), pages 505-521.
    • Handle: RePEc:eee:ejores:v:255:y:2016:i:2:p:505-521
    DOI: 10.1016/j.ejor.2016.05.041
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    References listed on IDEAS

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    Cited by:

    1. Bevrani, Bayan & Burdett, Robert L. & Bhaskar, Ashish & Yarlagadda, Prasad K.D.V., 2017. "A capacity assessment approach for multi-modal transportation systems," European Journal of Operational Research, Elsevier, vol. 263(3), pages 864-878.
    2. Song, Malin & Zhou, Wenzhuo & Upadhyay, Arvind & Shen, Zhiyang, 2023. "Evaluating hospital performance with plant capacity utilization and machine learning," Journal of Business Research, Elsevier, vol. 159(C).
    3. Yu, Shiwei & Zheng, Shuhong & Gao, Shiwei & Yang, Juan, 2017. "A multi-objective decision model for investment in energy savings and emission reductions in coal mining," European Journal of Operational Research, Elsevier, vol. 260(1), pages 335-347.
    4. Marynissen, Joren & Demeulemeester, Erik, 2019. "Literature review on multi-appointment scheduling problems in hospitals," European Journal of Operational Research, Elsevier, vol. 272(2), pages 407-419.
    5. Burdett, Robert L. & Kozan, Erhan, 2018. "An integrated approach for scheduling health care activities in a hospital," European Journal of Operational Research, Elsevier, vol. 264(2), pages 756-773.
    6. Wu, Xiaodan & Li, Juan & Chu, Chao-Hsien, 2019. "Modeling multi-stage healthcare systems with service interactions under blocking for bed allocation," European Journal of Operational Research, Elsevier, vol. 278(3), pages 927-941.
    7. Burdett, Robert L & Corry, Paul & Yarlagadda, Prasad & Cook, David & Birgan, Sean & McPhail, Steven M, 2023. "A mathematical framework for regional hospital case mix planning and capacity appraisal," Operations Research Perspectives, Elsevier, vol. 10(C).
    8. Bevrani, Bayan & Burdett, Robert & Bhaskar, Ashish & Yarlagadda, Prasad K.D.V., 2020. "A multi-criteria multi-commodity flow model for analysing transportation networks," Operations Research Perspectives, Elsevier, vol. 7(C).

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