A discrete event simulation model was developed for a network of eight major intensive care units (ICUs) as well as high-acuity units (HAUs) in British Columbia, Canada. The simulation model will be used to develop strategies for managing the combined impacts of COVID-19 and seasonal influenza without the need for extensive public health interventions to limit transmission.
The present study explores a simulation model combining system dynamics and discrete-event simulation for an electric vehicle charging system. The model allows for different parameters to be set individually, enabling the exploration of different system and usage scenarios. Multiple simulation runs were performed to analyze the considered energy system over a 1-year period and compare relevant output parameters for different system configurations and system locations.
Sustainability assessment is a multi-faceted, dynamic, and complex paradigm in the context of buildings with several social, economic, and environmental interactions. Currently, the building sector lacks a sustainability evaluation and benchmarking mechanism. So, a system-thinking approach that can solve these challenges due to its ability to evaluate complex systems was developed.
Demands for health care are becoming overwhelming for healthcare systems around the world regarding the availability of resources, particularly in emergency departments (EDs). This paper provides a case study of the Uppsala University Hospital, where a data-driven simulation model was designed to examine the current state of the patient flow and to investigate potential logistics solutions for improving that flow through a novel strategy.
Despite numerous attempts to quantify the impacts of factors influencing productivity in the construction industry, such factors are still perceived as static and independent, resulting in unrealistic productivity estimates. Two generic agent-based models were built to simulate the outcomes of a project through varying levels of detail, each investigating a certain set of impacts. Findings proved the accuracy of the proposed comprehensive approach in estimating durations compared to planned durations and to those obtained from the traditional approach.
The Covid-19 virus has substantially transformed many aspects of life, impacted industries, and revolutionized supply chains all over the world. System dynamics modeling can aid in predicting future outcomes of the pandemic and generate key learnings. This tutorial describes how the system dynamics simulation model was constructed for the Covid-19 pandemic using AnyLogic Software. The model serves as a general foundation for further epidemiological simulations and system dynamics modeling.
The constant rise of e-commerce coupled with extremely fast deliveries is a significant contributor to saturate city centers’ mobility. To address this issue, the development of a convenient Automated Parcel Lockers (APLs) network improves last-mile distribution by reducing the number of transportation vehicles, the distances driven, and the delivery stops. An agent-based model was implemented in the current paper to forecast parcel demand placed on APLs based on socio-economic factors.
The bullwhip effect, a phenomenon of progressively larger distortion of demands across a supply chain, can cause chaos and disorder with amplified supply and demand misalignment. An agent-based simulation model was developed to evaluate how risk pooling and information sharing between distinct entities in a supply chain can reduce the bullwhip effects. In agent-based paradigm different components of a system were described as agents which interact with each other in an environment.
This article presents a simulation model for feed supply networks consisting of a number of customers, retailers, and manufacturers which are agents and placed on Geographic Information System (GIS) map. The simulation model proposed a fuzzy-based decision strategy for customers to decide when to order specific products. The AnyLogic model described a possible decision strategy for retailers to optimize their transport routes by selecting viable manufacturers.
This study investigates a different source of uncertainty, which is the waiting time for the next vessel that is scheduled on a specific route, connecting two international ports. The aim of this research was to determine the booking size for vessels in oversea delivery to minimize transportation costs. The simulation model and all the respective processes included in the oversea supply chain were developed in AnyLogic with a discrete-event paradigm.