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IBM Project Published in Journal of Medical Systems and Recognized by the United States House of Representatives


IBM Project Published in Journal of Medical Systems and Recognized by the United States House of Representatives

At the AnyLogic Conference 2014, Kyle Johnson, Global Business Services in Advanced Analytics and Optimization at IBM partnered with Otsuka Pharmaceuticals, presented a “Multi-Agent-Based Simulation of a Complex Ecosystem of Mental Health Care.” To date, the research paper was published in the Journal of Medical Systems and received the attention of the United States House of Representatives [1]. The project revolves around the housing cycle of the United State’s Severely and Persistently Mentally Ill (SPMI). An SPMI patient defines someone with a diagnosis of Schizophrenia, Bipolar Disorder, or Major Depressive Disorder, and this group constitutes about 1.7% of the US population. To better understand the condition and possible improvements, IBM Global Research and Otsuka Pharmaceuticals used an agent-based approach to model the SPMI living situations over the second half of the 20th century.

What the Nobel Prize in Economics Means for (Agent-based) Simulation


What the Nobel Prize in Economics Means for (Agent-based) Simulation

The latest winner of the Nobel Prize in Economics, Angus Deaton is showcased in Ben Schuman’s blog from October 19th, 2015. Ben Schumann a senior modeler at decisionLab with a Ph.D. in Complex Systems Simulation. Moreover, as you know, Ben is a significantly experienced AnyLogic User, who works with diverse clients such as the MoD, Rolls-Royce or GSK. He handles project delivery from start to end, managing team members, and client communication. Typically, both economics researchers and mathematical modelers take into account certain parameters and the model outcomes are defined by the modeler. In the case of Deaton’s work and agent-based modeling, a different approach is used by defining individual behavior, and studying how it plays out.

An Agent-Based Explanation for the Housing Plight of America’s Mentally Ill


An Agent-Based Explanation for the Housing Plight of America’s Mentally Ill

The largest public mental health facility in the United States is not a hospital; it is the Los Angeles County Jail. A Severely and Persistently Mentally Ill (SPMI) patient generally defines someone with a diagnosis of Schizophrenia, Bipolar Disorder, or Major Depressive Disorder, and this group constitutes about 1.7% of the US population. To better understand the condition and possible improvements, IBM Global Research and Otsuka Pharmaceuticals used an agent-based approach to model the SPMI living situations over the second half of the 20th century. Kyle Johnson, Managing Consultant with IBM Global Business Services presented this project at the 2014 AnyLogic Conference. He works within the Advanced Analytics and Optimization branch of IBM BAO.

Disruptive Technology Change in Distribution Center Automation


Disruptive Technology Change in Distribution Center Automation

There has been a dramatic increase in investment by both venture capital and strategic investors in new robotics technologies for supply chain automation. These investments have been driven by rapid changes in expectations for consistent, fast, flexible consumer experience across all channels. Traditionally, automation and associate order fulfillment software has been optimized around a limited set of products (or SKU’s) and for the requirements and constraints of a specific channel between manufacturer and consumer. This may result in different, and potentially incompatible technical solutions being implemented at the same distribution center. The new expectation is that the retailer provide a consistent, and hopefully superior, consumer experience, regardless of which channel is most convenient for the consumer to use.

Modeling Healthcare at Different Abstraction Levels


Modeling Healthcare at Different Abstraction Levels

There are many cases of simulation modeling in healthcare. Application areas can vary, from process optimization in hospitals to macrolevel agent-based epidemiology models. Due to its multimethod nature, AnyLogic allows models to be built at various abstraction levels. A good illustration of how researchers and consultants can apply the same tool to different problems is the three models built by the Stockholm County Health Administration in Sweden. The models included macro, meso, and micro abstraction level applications in healthcare simulation. The microlevel model simulated the maternity ward in a hospital that was currently under construction. The purpose of the model was to support discussions related to which resources, capacity, and work methods were required in the new ward. One relevant discussion was whether to keep mother and child in the same room during their entire stay or to have dedicated rooms for antenatal care, delivery, and postnatal care.

Disaster Response Applications Using Agent-Based Modeling


Disaster Response Applications Using Agent-Based Modeling

Battelle is the world’s largest, non-profit, independent R&D organization, and is a worldwide leader in the development, commercialization, and transfer of technology. They manage or co-manage laboratories for the U.S. Department of Energy, the U.S. Department of Homeland Security, and an international nuclear laboratory in the United Kingdom. Battelle developed a disaster response solution for a federal government agency using AnyLogic, and in particular, agent-based modeling due to its multimethod capabilities. Joe Simkins, Economist at Battelle shared the solution with the AnyLogic community during the AnyLogic Conference 2013 in Washington D.C. Joe works in the fields of applied microeconomics, emergent simulation, and behavioral economics with experience in a wide variety of project applications ranging from health care to disaster response.

Find Optimal Product Sampling Using Agent-Based Modeling


Find Optimal Product Sampling Using Agent-Based Modeling

Before launching a new drug, a pharmaceutical company should decide the optimal promotional mix for both patient and doctors, and the optimal sampling level, high enough to induce therapy without cannibalizing paid prescriptions. Using AnyLogic Simulation Modeling, JP Tsang, Ph.D. & MBA, addresses the complexities of launching a new drug in the pharmaceutical industry, the need for a Contract Sales Organization (CSO) and the influence of Key Opinion Leaders (KOL). The project zeroed in on the medical group practice in order to better understand the dynamics between physician and patient, and between physician and medical representatives. The Agent Based Model (ABM) allows you to understand local behavior, identify bottlenecks regarding certain output variables and better grasp the big picture.

Agent-Based Model for the Secondary Threat Resulting from a Ballistic Impact Event


Agent-Based Model for the Secondary Threat Resulting from a Ballistic Impact Event

The process by which a high-velocity impact event leads to fire ignition onboard military vehicles is complex, influenced by the interaction of heated debris fragments and fuel spurting from ruptured tanks. Matthew J. Bova of the University of Cincinnati, Frank W. Ciarallo of Wright State University, and Raymond R. Hill of the Airforce Institute of Technology completed a proof-of-concept project involving the secondary threat resulting from a ballistic impact event using agent-based modeling in AnyLogic. Developing a complete characterization of the secondary threat resulting from a high-velocity projectile impact on the exterior of an aircraft body is of particular concern to the aircraft survivability community. Such ballistic impacts typically result in penetration of the body, generating clouds of debris fragments and releasing large quantities of thermal energy.

Simulation Modeling Based on Routine Healthcare Data


Simulation Modeling Based on Routine Healthcare Data

Decisions made by health care professionals require tools for planning, testing and assessment of new technologies or interventions. The complex structures, interactions and processes involved in health care, make change and innovation an ongoing challenge. Patrick Einzinger and Christoph Urach from DWH Simulation Services and Vienna University of Technology in partnership with the Austrian Association of Social Insurances (AASI) were given an opportunity to analyze public data for the purpose of critical future decision making. The AASI assembled routine care data upon reimbursement of heath care providers, which includes drugs prescribed, services rendered and diagnosis. Typical statistics and mathematical modeling were considered as a tool to analyze the data, but simulation was chosen to ensure the mass amount of data could be fully utilized, thus increasing the accuracy of the analysis and results.

What’s New in AnyLogic 7? Agent-Based Modeling Enhancements


What’s New in AnyLogic 7? Agent-Based Modeling Enhancements

“The Great Merge.” Most likely this is a familiar term, but perhaps you haven’t been able to experience the idea quite yet, or are not familiar with the many benefits “The Great Merge” permits. Let us take a moment to expand upon “The Great Merge,” other Agent-Based modeling enhancements, and what these changes mean for future model building. “The Great Merge,” in summary, is an ultimate unification of agents, entities, resources, pedestrians, trains, etc. and is useful to modelers in a multitude of ways. Read through the following list to familiarize yourself with the new developments.