Blackout-Resilient Operating Room Scheduling: A Multi-Objective Hybrid Microgrid Solution Leveraging Solar, Diesel, and EV Batteries

نوع: Type: Thesis

مقطع: Segment: masters

عنوان: Title: Blackout-Resilient Operating Room Scheduling: A Multi-Objective Hybrid Microgrid Solution Leveraging Solar, Diesel, and EV Batteries

ارائه دهنده: Provider: pegah ghafarpour

اساتید راهنما: Supervisors: Dr javad behnamian

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Dr amir saman kheirkhah ghah-Dr hamidreza dezfoolian

زمان و تاریخ ارائه: Time and date of presentation: 2025

مکان ارائه: Place of presentation: 62

چکیده: Abstract: This research addresses the development of an operating room scheduling problem under blackout conditions in hospitals requiring emergency power supply during blackouts. In this framework, a hybrid microgrid integrating solar panels, diesel generators, and repurposed electric vehicle (EV) batteries has been considered for supplying electricity to hospital systems. Such a model can ensure continuous electricity during blackouts without reliance on the main grid. The proposed model allocates surgical capacity based on the available energy from solar panels, diesel generators, and EV batteries, thereby enabling flexible responses during crises and reducing reliance on fossil fuels. The integration of EV batteries is particularly valuable in emergency conditions, as they can be discharged to support critical loads. To date, no comprehensive multi-objective mixed-integer programming framework has been developed for operating room scheduling under hybrid microgrid conditions that considers economic, environmental, and social dimensions simultaneously. In response, this study presents a multi-objective model with three objectives: (1) minimizing total surgery and energy costs, (2) minimizing carbon emissions from energy use, and (3) maximizing patient satisfaction. A solution approach using the augmented ε-constraint method and the GAMS software for exact solving, along with a multi-objective particle swarm optimization (MOPSO) algorithm has been employed. Due to the large-scale nature of the problem, the MOPSO algorithm is particularly suited for efficiently obtaining high-quality solutions while reducing computational time. Additionally, a multi-objective variable neighborhood search (MOVNS) algorithm is used as a benchmark for evaluating the proposed method. The results indicate that the proposed algorithm demonstrates strong performance based on multiple efficiency criteria.