A5064277891- Healthcare Operations and Scheduling Optimization
- Facility Location and Emergency Management
- Supply Chain and Inventory Management
- Advanced Manufacturing and Logistics Optimization
- Vehicle Routing Optimization Methods
- Chronic Disease Management Strategies
- Transportation and Mobility Innovations
- Sustainable Supply Chain Management
- Scheduling and Optimization Algorithms
- Simulation Techniques and Applications
- Artificial Intelligence in Healthcare
- Recycling and Waste Management Techniques
- Air Traffic Management and Optimization
- Food Waste Reduction and Sustainability
- Global Trade and Competitiveness
- Insurance and Financial Risk Management
- Emergency and Acute Care Studies
- Autonomous Vehicle Technology and Safety
- Urban and Freight Transport Logistics
- Business, Innovation, and Economy
- Traffic control and management
- Quality and Supply Management
- Trauma and Emergency Care Studies
- Disaster Response and Management
- Medical Coding and Health Information
Université Jean Monnet
2017-2024
Université Claude Bernard Lyon 1
2017-2023
Universidad de La Sabana
2009-2015
Abstract The latest advancements in electric vertical take-off and landing (eVTOL) vehicles indicate that soon this technology will be available multiple fields. One potential application of new is emergency medical services. These able to reach sites faster than ground ambulances at lower costs traditional helicopters. So the following years, eVTOL could used for aeromedical transportation. crucial decision implementing such a services location their areas (vertiports). In work, we propose...
The objective of this study is twofold. First, we seek to understand the characteristics multimorbid population that needs hospital care by using all diagnoses information (ICD-10 codes) and two aggregated multimorbidity frailty scores. Second, use machine learning prediction models on these patients predict rehospitalization within 30 365 days their length stay. This was conducted 8 882 anonymized hospitalized at University Hospital Saint-Étienne. A descriptive statistical analysis...
This paper considers the problem of scheduling a set jobs on both single machine and identical parallel machines with objective minimizing makespan or maximum completion time all jobs. Jobs are subject to release dates there sequence-dependent setup times. Since this is known be strongly NP-hard even for case, proposes heuristic algorithm solve it. The uses strategy random generation various execution sequences, then selects best such schedules. Experiments performed using random-generated...
Bed occupancy ratio reflects the state of hospital at a given time. It is important for management to keep track this figure proactively avoid overcrowding and maintain high level quality care. The objective work consists in proposing decision-aid tool managers allowing decide on bed requirements or network hospitals short-medium term horizon. To that extent we propose new data-driven discrete-event simulation model based data from French university predict staff requirements. We case study...
The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units respond crisis. This process greatly impacted daily activities hospitals. In this paper, we propose a two-step approach based on mining and discrete-event simulation for sizing recovery unit dedicated inside hospital. A decision aid framework is proposed help hospital managers make crucial decisions, such as hospitalization...
Automation in healthcare is a major challenge to improve quality of service while compressing costs. In particular, correct administration medicines patients crucial ensure care during hospitalization and minimize medication errors. Mistakes are more likely happen when medicine done manually (dispensing, ordering or administrating). To reduce the risks related errors, automation pharmacy processes appears as an appropriately tool solve this situation. paper, we have proposed new mathematical...
This paper considers the deterministic vehicle routing problem with service time requirements for delivery. Service requests are also available at different times known initial of route planning. presents an approach based on generation sequences (routes) using randomness. Both single-vehicle and multiple-vehicle cases studied. Our is validated random-generated data compared against optimal solution obtained by mathematical programming small-sized instances, as well lower bounds medium to...
This paper considers the deterministic vehicle routing problem with service time requirements for delivery. Service requests are also available at different times known initial of route planning. presents an approach based on generation sequences (routes) using randomness. Both single-vehicle and multiple-vehicle cases studied. Our is validated random-generated data compared against optimal solution obtained by mathematical programming small-sized instances, as well lower bounds medium to...
Objective To assess the effectiveness of different machine learning models in estimating pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II diagnosis, based on clinical risk index determined by analysis comorbidities. Materials methods In this cross-sectional study, we have used data from 11,028 anonymized records patients admitted to a high-complexity hospital Bogota, Colombia between 2017–2019 primary diagnosis Diabetes. These cases were classified...
In this paper, we deal with transportation services' configuration in the context of centralization sterilization service for a multi-hospital network. We address problem as Multi-trip VRP pickup and delivery, time windows release dates. The objective is to design logistics trips between network hospitals center pick-up contaminated reusable medical devices distribute sterile ones while minimizing costs. propose mixed integer programming model provide numerical experiments on randomly...