A bi-objective mathematical model for the patient appointment scheduling problem in outpatient chemotherapy clinics using Fuzzy C-means clustering: A case study

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Masoud  Rabbani ¹ (Department of Industrial Engineering, University of Tehran, Tehran, Iran)
Alireza  Khani ² (Department of Industrial Engineering, University of Tehran, Tehran, Iran)
amirreza  Zare ³ (Department of Industrial Engineering, University of Tehran, Tehran, Iran)
niloofar  Akbarian-Saravi ⁴ (Department of Industrial Engineering, University of Tehran, Tehran, Iran)

Received: 2021-04-16 Accepted : 2022-08-14 Published : 2022-09-01

Keywords: fuzzy clustering, Mathematical programming model, Patient appointment scheduling, Outpatient chemotherapy,

Abstract :

In healthcare, the Patient Appointment Scheduling (PAS) problem is one of the critical issues in Outpatient Chemotherapy Clinics (OCC). In the wake of this, this paper proposes a novel bi-objective mathematical programming model for solving the PAS problem in OCC. The developed mathematical model is inspired by cellular manufacturing. The first objective function minimizes the completion time of all treatments, and the second objective function maximizes the use of nurses' skills while considering clustered patients about their characteristics. To solve the bi-objective mathematical model, for the first time a hybrid approach based on Torabi-Hassini (TH) and Lagrange method is utilized. The results indicate that an increase in the number of nurses will enhance the treatment completion speed and allocation of nurses’ work skill. On the other hand, an increase in the number of chairs in clinics will decrease the assignments of nurses’ skills priority. The study supports decision makers in considering nurses' skills for the PAS problem. The results also denote the desirability of the proposed model. To validate the proposed model, OCC in Tehran is considered as a case study. Computational results reveal that considering nurses' skills in OCC and using the fuzzy clustering model, as a new method in patient clustering, lead to achieving a desirable and more realistic outcome.

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