Academic articles

Evaluation of The Effect of Chickenpox Vaccination on Shingles Epidemiology Using Agent-Based Modeling


Biological interactions between varicella (chickenpox) and herpes zoster (shingles), two diseases caused by the varicella zoster virus (VZV), continue to be debated including the potential effect on shingles cases following the introduction of universal childhood chickenpox vaccination programs. Researchers investigated how chickenpox vaccination in Alberta impacts the incidence and age-distribution of shingles over 75 years post-vaccination, taking into consideration a variety of plausible theories of waning and boosting of immunity.

Analyzing Emergency Evacuation Strategies for Mass Gatherings using Crowd Simulation and Analysis framework: Hajj Scenario


Hajj is one of the largest mass gatherings where Muslims from all over the world gather in Makah each year for pilgrimage. A mass assembly of such scale bears a huge risk of disaster either natural or man-made. In the past few years, thousands of casualties have occurred while performing different Hajj rituals, especially during the Circumambulation of Kaba (Tawaf) due to stampede or chaos. During such calamitous situations, an appropriate evacuation strategy can help resolve the problem and mitigate further risk of causalities. It is however a daunting research problem to identify an optimal course of action based on several constraints. Modeling and analyzing such a problem of real-time and spatially explicit complexity requires a microscale crowd simulation and analysis framework. Which not only allows the modeler to express the spatial dimensions and features of the environment in real scale, but also provides modalities to capture complex crowd behaviors. In this paper, we propose an Agent-based Crowd Simulation & Analysis framework that incorporates the use of AnyLogic Pedestrian library and integrates/interoperate AnyLogic Simulation environment with the external modules for optimization and analysis. Hence provides a runtime environment for analyzing complex situations, e.g., emergency evacuation strategies.

Simulation-Based Design and Traffic Flow Improvements in the Operating Room


A simulation model was created to model the traffic flow in the operating room. A key research challenge in operating room design is to create the most efficient layout that supports staff and patient requirements on the day of surgery. The simulation allows comparison of base model designs to future designs using several performance measures. To develop the model, we videotaped multiple surgeries in a set of operating rooms and then coded all activities by location, agent and purpose. Our current analysis compares layouts based on total distance walked by agents, as well as the number of contacts, measured as the number of times agents must change their path to accommodate some other agent or physical constraint in the room. We demonstrate the value and capability of the model by improving traffic flow in the operating room as a result of rotating the bed orientation.

Data-Driven Simulation for Healthcare Facility Utilization Modeling and Evaluation


Utilization evaluation for healthcare facilities such as hospitals and nursing homes is crucial for providing high quality healthcare services in various communities. In this paper, a data-driven simulation framework integrating statistical modeling and agent-based simulation (ABS) is proposed to evaluate the utilization of various healthcare facilities. A Bayesian modeling approach is proposed to model the relationship between heterogeneous individuals’ characteristics and time to readmission in the hospital and nursing home. An ABS model is developed to model the dynamically changing health conditions of individuals and readmission/discharge events. The individuals are modeled as agents in the ABS model, and their time to readmission and length of stay are driven by the developed Bayesian individualized models. An application based on Florida’s Medicare and Medicaid claims data demonstrates that the proposed framework can effectively evaluate the healthcare facility utilization under various scenarios.

Agent-Based Modeling Framework for Simulation of Complex Adaptive Mechanisms Underlying Household Water Conservation Technology Adoption


Using new technologies to maintain, construct, and reuse naturally created products like asphalt, soils, and water can reserve the environment. The objective of this study was to specify and model the behavior of households regarding the installation of water conservation technology and evaluate strategies that could potentially increase water conservation technology adoption at the household level. In particular, this study created an agent-based modeling framework in order to understand various factors and dynamic behaviors affecting the adoption of water conservation technology by households. The model captures various demographic characteristics, household attributes, social network influence, and pricing policies; and then evaluates their influence simultaneously on household decisions in adoption of water conservation technology. The application of the proposed simulation model was demonstrated in a case study of the City of Miami Beach. The simulation results identified the intersectional effects of various factors in household water conservation technology adoption and also investigated the scenario landscape of the adoptions that can inform policy formulation and planning.

Signal Phase Timing Impact on Traffic Delay and Queue Length-a Intersection Case Study


Traditional intersection traffic signal control strategy is pre-determined signal with certain phase timing length for each circle. Studies focusing on adaptive traffic signal strategy have somewhat achieved the goal of reducing traffic system delay to some extent. However, few of them capture the benefit of using the queue length as the criteria under the connected vehicle environment, and this paper focuses on firstly identifying the potential saving of average system delay with agent-based simulation modeling, and secondly finding out the relationship between average system delay and average queue length for traffic approaching the signalized intersections. Through applying the agent-based simulation modeling approach in AnyLogic, findings show that average system delay could be reduced using optimized parameters (e.g. arrival rate, signal phase length, etc.), specifically, 5.29% saving of total average system time, 4%-28% traffic queue reduction for different traffic lanes, and a positive relationship between average system de-lay and the average traffic queue length is detected.

Simulation testbed for the analysis of beneficial business strategies for the airbus A350 production ramp-up


The production ramp-up of new aircraft is characterized by high complexity and planning and control chal-lenges caused by complex product design, supply chain and production processes. In the past, this resulted in significant delays and increased costs of the production ramp-up. Novel business strategies and planning and scheduling technologies promise better production control and risk mitigation during the ramp-up phase. The European research project ARUM has developed those business strategies and a new distributed decision support solution based on knowledge processing technologies. A simulation testbed was used to identify the most beneficial business strategies and to evaluate linked control strategies for the industrial use case of the Airbus A350 production ramp-up. This paper discusses the potential of simulations for the business strategy definition and for the validation of linked control strategies from the industrial end-user perspective.

Auction policy analysis: an agent-based simulation optimization model of grain market


National grain reserve is important in terms of responding to disasters and the unbalance between supply and demand in many countries. In China, the government supplements grain supply through online auctions. This study focuses on the auction policy of national grain reserve. We develop an agent-based simulation model of China’s wheat market with detail descriptions of different agents, including national grain reserve, grain trading enterprises and grain processing enterprises. Based on this model, the Optimal Computing Budget Allocation (OCBA) simulation optimization method is adopted to analyze the characteristics of optimal decision variables under different scenarios, with an objective to minimize the fluctuation of wheat price. We obtain some insights about operations of national grain reserve. As the first agent-based simulation model about national grain reserve and grain market, this model can be widely used in agricultural economics, and can provide policy supports to the government.

Increasing capacity utilization of shuttle trains in intermodal transport by investing in transshipment technologies for non-cranable semi-trailers


For shuttle trains with a fixed transport capacity which are the dominant operating form in intermodal transport, increasing capacity utilization is of crucial importance due to the low marginal costs of transporting an additional loading unit. Hence, offering rail-based transport services for non-cranable semi-trailers can result in additional earnings for railway companies. However, these earnings have to compensate for the investment costs of the technology. Based on a dynamic investment calculation, this paper presents a simulation model to evaluate the economic profitability of transshipment technologies for non-cranable semi-trailers from the railway company’s perspective. The results depend on the capacity utilization risk faced by the railway company. In particular, if the railway company does not sell all the train capacity to freight forwarders or intermodal operators on a long-term basis, investing in technology for the transshipment of non-cranable semi-trailers can be economically profitable.

A structured approach for constructing high fidelity ED simulation


This paper presents a structured approach to building a high-fidelity simulation for an emergency department. Our approach has three key features. First, we use the concept of modules as a building block for modeling. A module is a minimum unit that has clinical or administrative meanings in ED operation, and it consists of low level operational activities. Second, we use a structured template to formally represent modules, and we adopt notations and grammars from the business process modeling notation. This provides an enhanced clarity and transparency, which proves very useful in extracting necessary data from a hospital database or from interviewing ED staff. Finally, we define an interface, specifically data structure and handler, for converting information represented in the modules into simulation languages. This interface makes it possible to seamlessly link the modeling process to the implementation process in the simulation construction.