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Journal of Combinatorial Optimization

, Volume 37, Issue 1, pp 337–362 | Cite as

Patient scheduling in hemodialysis service

  • Zhenyuan LiuEmail author
  • Jiongbing Lu
  • Zaisheng Liu
  • Guangrui Liao
  • Hao Howard Zhang
  • Junwu Dong
Article
  • 138 Downloads

Abstract

The patient scheduling presents a number of operations management challenges in hemodialysis service center. The homogeneity of the break time between treatments, satisfying the patients preferences on time, space and equipment and the multi-function dialysis devices make for an interesting and complex scheduling problem that could benefit from computerized decision support. In this paper, patient scheduling problem in hemodialysis service is formulated as a synthetic-objective optimization model combined with several criteria on minimizing the gross utilization cost of devices, the number of night treatment, satisfying the patients preferences and the equilibrium of the devices. A basic heuristics and a rollout algorithm based on the heuristics are developed for solving the problem where three levels of treatment schedule sets are constructed one by one. The performances of the rollout algorithm and the basic heuristics are compared on the real cases. Computational results show that significant improvement of patients degree of satisfaction can be achieved with the rollout algorithm while simultaneously considering to reduce the number of night shifts.

Keywords

Patient scheduling Hemodialysis service Synthetic-objective optimization Heuristics Rollout algorithm 

Notes

Acknowledgements

This work is supported partially by the Fundamental Research Funds for the Central Universities, HUST: 2017KFYXJJ178 and the Yellow Crane Talents Foundation of Wuhan, China.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of AutomationHuazhong University of Science and TechnologyWuhanChina
  2. 2.Key Laboratory of Education Ministry for Image Processing and Intelligent ControlWuhanChina
  3. 3.Department of Radiation OncologyUniversity of Maryland School of MedicineBaltimoreUSA
  4. 4.Pu ai HospitalWuhanChina

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