Academic articles

E-commerce has increased tremendously in recent decades because of improvements in information and telecommunications technology along with changes in social lifestyles. More recently, e-grocery (groceries purchased online) including fresh vegetables and fruit, is gaining importance as the most-efficient delivery system in terms of cost and time.

This paper evaluates the effect of cooperation-based logistics policies, including horizontal cooperation, on service quality among different supermarkets in Pamplona, Spain. For that, the research team applies simulation modeling as a logistics optimization technique.

With the current COVID-19 pandemic, there is a shopping paradigm shift towards e-commerce, which even after the pandemic will not return to the old state. This paradigm shift raises the need for optimal layout and pick paths that result in better performing multi-pick warehouses.

Healthcare is facing great challenges to make processes more efficient and meanwhile provide better service to patients. Management of the intensive care unit (ICU), which is one of the most critical departments in terms of patient status and patient flow, also tries to provide better service and reduce the mortality rate.

During COVID-19, effective and efficient management is of utmost importance. A patient flow model developed in AnyLogic simulation software allows a comprehensive evaluation of eleven different management policies for controlling ICU admissions when facing capacity shortages.

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.

The paper shows the first proof-of-concept for long-distance digital twin solutions by connecting real-time data feeds from the physical system to a virtual GIS environment that can be utilized in real-time synchromodal transportation.

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.