The main objective of this paper is to provide a simulation-based decision-support tool for the healthcare industry. This tool will help the hospital management decide on resource utilization, in particular bed allocation, for the next few months. With it, hospitals could predict admissions and see how newly implemented policies impact the patient’s flow.
During the pandemic specialty physicians are working as frontline workers due to hospital overcrowding and a lack of providers. This places them as a high-risk target of the epidemic. Within these specialties, anesthesiologists are one of the most vulnerable groups as they come in close contact with the patient's airway.
An agent-based simulation model was developed using AnyLogic software to test various staffing policies within the anesthesiology department of the largest healthcare provider in Upstate South Carolina.
The overhead crane scheduling problem has been of interest to many researchers. While most approaches are optimization-based or use a combination of simulation and optimization, this research suggests a combination of dynamic simulation and reinforcement learning-based AI as a solution.
The goal of this steel plant simulation project was to minimize the crane waiting time at the LD converters by creating a better crane schedule.
This case study focuses on the simulation of a soon-to-be-implemented automation system within a Walmart Canada warehouse. This new system's aim is more efficient warehouse operations. Many stock-keeping units (SKUs) cannot be sent to retail stores in full case quantities. They are slow movers and would require individual stores to carry excessive inventory.
Breakpack is the process of breaking cases down to individual eaches (pieces) and combining them into mixed SKU cartons. Automating breakpack offers significant labor and quality savings, that are important to ensure efficient warehouse operations, but also a high degree of complexity.
Long-haul trailer operations are a critical part of supply chains in many of the world’s developed economies. In the UK, it is estimated that long-haul logistics contributes around 45% of all greenhouse gas emissions from road freight.
One way to reduce greenhouse gas emissions in this sector is by fitting a battery on the trailer. However, long-haul operations are very energy-intensive and electric vehicles would require batteries of considerable size and weight. Applying agent-based modelling and simulation, this paper aims at analyzing if electrification (e.g., electric vehicle fleet, electric road system, etc.) would help reduce greenhouse gas emissions.
Operations and maintenance (O&M) expenses can vary greatly from one energy solution to another. While a solar farm or geothermal system may need minimal ongoing maintenance, wind turbines require a skilled crew to keep them operating efficiently.
In this research, the authors use a scaled-down wind farm case study to demonstrate the potential of Reinforcement Learning (RL) in identifying an optimal O&M policy and to show the ease of use of AnyLogic simulation software and Pathmind reinforcement learning tool.
Due to numerous uncertainties such as bad weather conditions, frequent changes in the schedules of vessels, breakdowns of equipment, port managers are aiming at providing adaptive and flexible strategic planning of their facilities, especially intermodal terminals (dry ports).
This research shows that the combination of the agent-based modeling with other simulation approaches simplifies the process of designing simulation models and increases their visibility. The developed set of models allows the researchers to compute the balanced values of the parameters. Consequently, it helps achieve effective operation of a seaport – intermodal terminal system. The provided case study on one of the busiest ports in China proves the adequacy and validity of the developed simulation models.
Forest equipment planning and availability depend on forest management and harvesting regimes in addition to the market demand. This project aims to support the equipment planning process by estimating the future need of forest equipment with different forest management options. The number of required machinery depends on how much feedstock is available. It also depends on how much biomass was processed by previous machines in the system. The number of products that the machine in question has to process varies based on the supply chain structure.
An incisional hernia (IH) is a ventral hernia that develops after surgical trauma to the abdominal wall, a laparotomy. IH repair is a common surgery that can generate chronic pain, decreased quality of life, and significant healthcare costs caused by hospital readmissions. The goal of this study is to analyze the clinical pathway of patients having an IH using a medico-administrative database and predictive modeling. Predictive modeling in healthcare is used, among other things, to understand the times of occurrence of complications and associated costs. It enables the simulation of what-if scenarios to propose an improved care pathway for patients who are the most exposed.