Penyelesaian Permasalahan Penjadwalan Terintegrasi di Rumah Sakit Menggunakan Algoritma Quantum Learning - Great Deluge

Kurniawan, Bryan Michael Kurniawan (2026) Penyelesaian Permasalahan Penjadwalan Terintegrasi di Rumah Sakit Menggunakan Algoritma Quantum Learning - Great Deluge. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Penjadwalan (Timetabling) merupakan proses pengalokasian serangkaian acara dan sumber daya secara optimal dalam ruang dan waktu tertentu. Meskipun bertujuan memberikan solusi optimal, kegagalan dalam merancang sistem penjadwalan yang baik dapat menyebabkan inefisiensi, pemborosan sumber daya, serta ketidakpuasan pengguna layanan. Rumah sakit, sebagai institusi yang sangat bergantung pada efektivitas layanan kesehatan, menghadapi tantangan kompleks dalam mengelola jadwal operasionalnya. Penelitian ini mengangkat permasalahan integrasi tiga aspek utama penjadwalan rumah sakit yang sering kali diselesaikan secara terpisah, yaitu: penjadwalan operasi (surgical case planning), penempatan pasien ke ruang rawat inap (patient admission scheduling), dan penugasan perawat ke ruangan pasien (nurse-to-room assignment), yang berpotensi menghasilkan solusi suboptimal jika tidak ditangani secara holistik. Penelitian ini bertujuan untuk mengembangkan solusi berbasis algoritma metaheuristik hibrida, yaitu Quantum Learning - Great Deluge (QLGD), guna mengoptimalkan sistem penjadwalan terintegrasi rumah sakit dengan mengacu pada spesifikasi masalah Integrated Healthcare Timetabling Competition (IHTC) 2024. Data yang digunakan bersifat statis dan deterministik, dengan asumsi ketersediaan informasi di awal periode, tanpa mempertimbangkan dinamika stokastik seperti pembatalan mendadak. Metodologi penelitian menggabungkan teknik Quantum Learning untuk memandu pemilihan operator heuristik secara cerdas guna mempercepat eksplorasi ruang solusi, serta mekanisme Great Deluge yang memanfaatkan boundary adaptif untuk menghindari jebakan solusi lokal (local optima). Hasil eksperimen pada 30 instance dataset IHTC 2024 menunjukkan bahwa algoritma QLGD mampu menghasilkan solusi yang sepenuhnya feasible (layak) untuk seluruh kasus uji tanpa pelanggaran hard constraint. Analisis komparatif menunjukkan bahwa QLGD mengungguli algoritma dasar Hill Climbing dan Adaptive Threshold Iterated Local Search (ATILS) secara signifikan, terutama pada instance berskala besar, serta menunjukkan kinerja yang kompetitif dan stabil dibandingkan metode Quantum Learning Simulated Annealing (QLSA). Dalam validasi eksternal terhadap hasil kompetisi IHTC 2024, algoritma ini menempati peringkat ke-26 dengan keunggulan nol solusi unfeasible, membuktikan reliabilitasnya. Penelitian ini menyimpulkan bahwa penerapan algoritma QLGD terbukti efektif meningkatkan efisiensi penjadwalan rumah sakit, menawarkan keseimbangan antara kualitas solusi dan stabilitas komputasi, serta berkontribusi sebagai referensi penerapan metaheuristik adaptif pada permasalahan optimasi dunia nyata yang kompleks.
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Timetabling is the process of optimally allocating a series of events and resources within a specific space and time. Although aimed at providing optimal solutions, failure to design a good timetabling system can lead to inefficiencies, resource wastage, and service user dissatisfaction. Hospitals, as institutions heavily reliant on healthcare service effectiveness, face complex challenges in managing their operational schedules. This study addresses the complexity of integrating three main aspects of hospital timetabling often solved separately: surgical case planning, patient admission scheduling, and nurse-to-room assignment, which potentially yield suboptimal solutions if not handled holistically. This study aims to develop a solution based on a hybrid metaheuristic algorithm, Quantum Learning - Great Deluge (QLGD), to optimize the integrated hospital timetabling system referring to the Integrated Healthcare Timetabling Competition (IHTC) 2024 problem specifications. The data used is static and deterministic, assuming information availability at the beginning of the period, without considering stochastic dynamics like sudden cancellations. The research methodology combines Quantum Learning techniques to intelligently guide heuristic operator selection to accelerate solution space exploration, and the Great Deluge mechanism utilizing an adaptive boundary to avoid local optima traps. Experimental results on 30 instances of the IHTC 2024 dataset demonstrate that the QLGD algorithm is capable of producing fully feasible solutions for all test cases without hard constraint violations. Comparative analysis shows that QLGD significantly outperforms baseline algorithms Hill Climbing and Adaptive Threshold Iterated Local Search (ATILS), particularly on large-scale instances, and demonstrates competitive and stable performance compared to the Quantum Learning Simulated Annealing (QLSA) method. In external validation against IHTC 2024 competition results, this algorithm ranks 26th with the advantage of zero unfeasible solutions, proving its reliability. This study concludes that the application of the QLGD algorithm proves effective in improving hospital timetabling efficiency, offering a balance between solution quality and computational stability, and contributing as a reference for adaptive metaheuristic application in complex real-world optimization problems.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Great Deluge, Metaheuristik, Permasalahan Penjadwalan, Quantum Learning, Great Deluge, Metaheuristic, Timetabling Problem, Quantum Learning
Subjects:	T Technology > T Technology (General) > T57.84 Heuristic algorithms.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 57201-(S1) Undergraduate Thesis
Depositing User:	Bryan Michael Kurniawan
Date Deposited:	29 Jan 2026 07:20
Last Modified:	29 Jan 2026 07:20
URI:	http://repository.its.ac.id/id/eprint/131110

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