Academic articles

Large-scale events are always vulnerable to natural disasters and man-made chaos which poses great threat to crowd safety. Such events need an appropriate evacuation plan to alleviate the risk of causalities. We propose a modeling framework for large-scale evacuation of pedestrians during emergency situation. Proposed framework presents optimal and safest path evacuation for a hypothetical large-scale crowd scenario. The main aim is to provide the safest and nearest evacuation path because during disastrous situations there is possibility of exit gate blockade and directions of evacuees may have to be changed at run time. The recommended simulation framework incorporates Anylogic simulation environment to design complex spatial environment for large-scale pedestrians as agents.

Fortifying facilities within a supply chain network can mitigate facility failures caused by disruptions. In this study we build an agent-based simulation model to study the r-interdiction median problem with fortification (RIMF), considering two types of facility disruptions: naturally-caused and human-caused disruptions. The objective of this study is to develop a simulation model that analyzes facility disruption and fortification as a repeated Stackelberg competition, where fortification decisions are made anticipating disruptions.

This study analyzes the potential benefits and drawbacks of taxi sharing using agent-based modeling. New York City (NYC) taxis are examined as a case study to evaluate the advantages and disadvantages of ride sharing using both traditional taxis (with shifts) and shared autonomous taxis. Compared to existing studies analyzing ride sharing using NYC taxi data, reserarchers from the Purdue University proposed a model that incorporates individual heterogeneous preferences; compared traditional taxis to autonomous taxis; and examined the spatial change of service coverage due to ride sharing.

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.

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.

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.

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.

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.

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.

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.