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An evolutionary approach to rehabilitation patient scheduling: A case study

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  • Chien, Chen-Fu
  • Tseng, Fang-Pin
  • Chen, Chien-Hung

Abstract

Focusing on real settings, this study aimed to develop an evolutionary approach based on genetic algorithm for solving the problem of rehabilitation patient scheduling to increase service quality by reducing patient waiting time and improve operation efficiency by increasing the therapy equipment utilization. Indeed, due to partial precedence constraints of rehabilitation therapies, the problem can be structured as a hybrid shop scheduling problem that has received little attention to date. In addition, a mixed integer programming model was also constructed as a benchmark to validate the solution quality with small problems. Based on empirical data from a Medical Center in Taiwan, several experiments were conducted to estimate the validity of the proposed algorithm. The results showed that the proposed algorithm can reduce patient waiting time and enhance resource utilization and thus demonstrated the practicality of the proposed algorithm. Indeed, a decision support system embedded with the developed algorithm has been implemented in this medical center.

Suggested Citation

  • Chien, Chen-Fu & Tseng, Fang-Pin & Chen, Chien-Hung, 2008. "An evolutionary approach to rehabilitation patient scheduling: A case study," European Journal of Operational Research, Elsevier, vol. 189(3), pages 1234-1253, September.
    • Handle: RePEc:eee:ejores:v:189:y:2008:i:3:p:1234-1253
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    Cited by:

    1. 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.
    2. Nossack, Jenny, 2022. "Therapy scheduling and therapy planning at hospitals," Omega, Elsevier, vol. 109(C).
    3. Lizhong Zhao & Chen-Fu Chien & Mitsuo Gen, 2018. "A bi-objective genetic algorithm for intelligent rehabilitation scheduling considering therapy precedence constraints," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 973-988, June.
    4. Xiang Ma & Antoine Sauré & Martin L. Puterman & Marianne Taylor & Scott Tyldesley, 2016. "Capacity planning and appointment scheduling for new patient oncology consults," Health Care Management Science, Springer, vol. 19(4), pages 347-361, December.
    5. H. Romero & N. Dellaert & S. Geer & M. Frunt & M. Jansen-Vullers & G. Krekels, 2013. "Admission and capacity planning for the implementation of one-stop-shop in skin cancer treatment using simulation-based optimization," Health Care Management Science, Springer, vol. 16(1), pages 75-86, March.
    6. Yang, Xiaopeng & Zheng, Danheng & Sieminowski, Tammy & Paradi, Joseph C., 2015. "A dynamic benchmarking system for assessing the recovery of inpatients: Evidence from the neurorehabilitation process," European Journal of Operational Research, Elsevier, vol. 240(2), pages 582-591.
    7. Tsai, Pei-Fang Jennifer & Teng, Guei-Yu, 2014. "A stochastic appointment scheduling system on multiple resources with dynamic call-in sequence and patient no-shows for an outpatient clinic," European Journal of Operational Research, Elsevier, vol. 239(2), pages 427-436.

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