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A Data-Driven Pharmacists Scheduling Problem in a Pharmacy with Fairness Concerns

In: Liss 2022

Author

Listed:
  • Yuyao Feng

    (National University of Singapore)

  • Xiang Jie

    (Sichuan University)

Abstract

In a field investigation of the outpatient pharmacy of one hospital in Chengdu, we found it bears problems including the poor working experience of pharmacists, long patient waiting time, and low service efficiency. One major reason is traced to the lack of a scientific scheduling method, therefore, in this study, we proposed an optimization scheduling method based on the data on prescriptions and pharmacist preferences. We built a data-driven pharmacist scheduling model to minimize the difference in work experience and intensity among pharmacists. Specifically, the objective is to minimize the variance of the weighted working time among pharmacists and to lower the work intensity and negative emotions of pharmacists. The case study demonstrates that this model has considerable advantages in strengthening the fairness concerns of pharmacists as well as their work experience. The results also show that the model has good properties regarding stability and robustness in varied circumstances. Through this optimization, we could improve the efficiency of pharmacists and the service quality of the pharmacy.

Suggested Citation

  • Yuyao Feng & Xiang Jie, 2023. "A Data-Driven Pharmacists Scheduling Problem in a Pharmacy with Fairness Concerns," Lecture Notes in Operations Research, in: Xiaopu Shang & Xiaowen Fu & Yixuan Ma & Daqing Gong & Juliang Zhang (ed.), Liss 2022, pages 363-378, Springer.
    • Handle: RePEc:spr:lnopch:978-981-99-2625-1_29
    DOI: 10.1007/978-981-99-2625-1_29
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