The uncertain surgery time in integrated sequencing, planning and scheduling problem of the surgical ward

TL;DR

This paper develops mathematical models for surgical ward scheduling that incorporate uncertainties in surgery times, aiming to optimize patient flow, reduce waiting times, and improve hospital management and patient satisfaction.

Contribution

It introduces a robust optimization model for surgical scheduling that effectively handles uncertain surgery durations, enhancing decision-making in hospital operations.

Findings

Robust model reduces solution fluctuations under uncertainty.

Mathematical models improve scheduling efficiency and patient flow.

Computational results provide managerial insights for hospital administrators.

Abstract

The surgical department and adequate access to health care are critical problems. The operating room plays a fundamental role in the performance of a hospital. The real problem faces several issues to collect data and optimize scheduling and planning. In this paper, the planning and integral scheduling problems were taken into consideration including surgeon work times, patients' surgery priority, and preparation time of the operation room after each surgery. Based on this, two mathematical models have been developed. The first model is a mixed-integer programming model that minimizes the total patient waiting time. This model was designed for three sections: Public Health Unit (PHU), Operating Rooms, and Post Anesthesia Care Unit (PACU). This model includes scheduling, allocating surgery personnel, and sequencing the groups of patients in each section. The decision-makers, for patients, demonstrate the experts would determine some priorities. The service times for the patients at every stage are affected by the proficiency of surgery personnel. In the second model, a robust optimization approach has been used due to overcoming the uncertain surgery times. The proposed robust model indicated that the fluctuations of solutions in the future are less than the deterministic model at an acceptable level. The computational results reveal some good managerial insights for hospital administrators. The managers could use this model to improve system management, quality of services to patients, and patient satisfaction.