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All Journal G-Tech : Jurnal Teknologi Terapan
Muhammad Andika Fadilla
Universitas Bina Darma, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Penggunaan Algoritma Greedy dan Deep Reinforcement Learning untuk Optimasi Jadwal Operasi dalam Adaptive Scheduling Muhammad Andika Fadilla; Tata Sutabri
G-Tech: Jurnal Teknologi Terapan Vol 9 No 2 (2025): G-Tech, Vol. 9 No. 2 April 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i2.6844

Abstract

Operating room scheduling faces persistent challenges in healthcare facilities worldwide, with inefficiencies leading to resource wastage, extended patient waiting times, and staff burnout. This study addresses these challenges through three methodologies: greedy algorithm, deep reinforcement learning (DRL), and a novel hybrid model. Analysis of 35,000 surgical procedures revealed significant inefficiencies in current practices, including OR overutilization (463.87%), substantial waiting times (170.07 minutes), and frequent delays (58.39% of procedures). The hybrid model demonstrated superior performance, achieving a 34.2% reduction in OR utilization, 55.9% reduction in waiting times, and 87.5% improvement in on-time procedures compared to baseline. These improvements translated into significant clinical benefits, including reduced staff overtime (57.1%) and enhanced emergency case accommodation (17.6%). The hybrid model's resilience to operational disruptions and balanced performance across multiple dimensions provides compelling evidence for implementing adaptive scheduling methodologies in clinical practice, offering a comprehensive solution that balances efficiency, adaptability, and patient-centered care.
Co-Authors Tata Sutabri