20.500.12556/DKUM-96782 Optimizing emergency home healthcare scheduling with improved Quantum-behaved Particle Swarm Optimization With the intensification of China’s aging society, improving the health management and emergency response capabilities of the elderly at home has become an urgent issue that needs to be addressed. To meet this challenge, an Emergency Home Monitoring System (EHMS) that utilizes real-time data and wearable device monitoring is developed to optimize the Emergency Medical Transport Vehicle and Hospital Scheduling Problem (EMTVHSP) for elderly people at home. The patient's condition classification and waiting time are effectively combined to establish an Emergency Medical Transport Vehicle and Hospital Scheduling Model (EMTVHSM). Specifically, the optimization objective of the model is to minimize the maximum rescue time, thereby improving the allocation efficiency of medical resources and the efficiency of patient transfer. To solve this model, an Improved Quantum-behaved Particle Swarm Optimization (IQPSO) is proposed. The algorithm significantly improves the ability to solve complex scheduling problems by introducing neighborhood structure, improving constraint processing, introducing mutation operations and designing innovative resource reallocation strategies. Simulation results show that the dynamic resource scheduling method based on the IQPSO has significant advantages over traditional algorithms in reducing the maximum patient transfer time and improving scheduling efficiency and the optimization effect is improved by an average of 6.1 %. The emergency home monitoring system, scheduling model, and optimization algorithm designed effectively provide a more efficient emergency medical resource scheduling solution for elderly people at home and offer strong technical support and a practical basis for addressing health management challenges in an aging society. emergency home monitoring system emergency medical resource scheduling treatment priority quantum-behaved particle swarm optimization cellular neighbor network roulette wheel selection machine learning nadzorni sistemi sistemi za nujno zdravstveno pomoč razvrščanje zmogljivosti optimizacija zdravstvena nega true false true Fakulteta za strojništvo Angleški jezik Slovenski jezik https://apem-journal.org/ Članek v reviji 2026-01-28 14:49:27 2026-01-28 14:52:03 2026-01-29 03:11:48 0000-00-00 00:00:00 2025 0 0 str. 254–276 no. 2 Vol. 20 June 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-01-26 2025-06-07 2025-07-29 519.8:616.083 266515971 10.14743/apem2025.2.539 1854-6250 APEM20-2_254-276.pdf APEM20-2_254-276.pdf 1 BA610D8BBCBDF4618374F758AB6F83E3 16c5f4ade3b6020355e09f104f2f829d6849315f343d02f956184d11ed81b40a 60a88d95-fc50-11f0-9a72-00155d00018d https://dk.um.si/Dokument.php?lang=slv&id=202700 https://apem-journal.org/Archives/2025/VOL20-ISSUE02.html 0 725da95d-fc50-11f0-9a72-00155d00018d https://dk.um.si/Dokument.php?lang=slv&id=202701 Fakulteta za strojništvo Advances in production engineering & management 0 0 0