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A Hybrid Simulation Approach to Dynamic Multi-Skilled Workforce Planning of Production Line Yuan Feng, Wenhui Fan, Tsinghua University. Winter Simulation Conference, 2014.

Workers cross-trained with multiple tasks can improve the workforce flexibility for the plant to handle
variations in workload. Therefore, it is necessary to study the dynamic multi-skilled workforce planning
problem of production line with the application of cross-training method.
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Return to Work Behavior of People with Disabilities: A Multi-Method Approach Mariusz Balaban, MYMIC LLC. Winter Simulation Conference, 2014.

This paper discusses the development of a simulation model to mimic a return to work phenomenon of Social Security Disability Insurance (SSDI) enrollees in the United States. Agent Based and Bayesian Network methods are used within a multi-method simulation model to capture system conditions and enrollee behavior.
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Modeling Human Behavior – An (ID)entity Crisis? Sally C. Brailsford, University of Southampton. Winter Simulation Conference, 2014.

Agent-based modeling (ABM) has gained great popularity in recent years, especially in application areas where human behavior is important, because it opens up the possibility of capturing such behavior in great detail. Hybrid models which combine ABM with discrete-event simulation (DES) are particularly appealing in service industry applications.
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Towards Closed Loop Modeling: Evaluatng The Prospects for Creating Recurrently Regrounded Aggregate Simulation Models Using Particle Filtering Nathaniel Osgood, Juxin Liu, University of Saskatchewan 106 Wiggins Road, University of Saskatchewan Saskatoon. Winter Simulation Conference, 2014

Public health agencies traditionally rely heavily on epidemiological reporting for notifiable disease control, but increasingly apply simulation models for forecasting and to understand intervention tradeoffs. Unfortunately, such models traditionally lack capacity to easily incorporate information from epidemiological data feeds.
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An Agent-Based Explanation for 20th Century Living Situation Changes in America’s Severely and Persistently Mentally Ill Population Kyle L. Johnson, Dr. Dimitris Alevras, IBM Global Business Services; Dr. John Docherty, Dr. Erin Falconer, Otsuka Medical Affairs. AnyLogic Conference 2014

The largest public mental health facility in the United States is not a hospital; it is the Los Angeles County Jail. This paper describes an agent-based approach to explaining why prisons and jails house so many of America’s most seriously mentally ill. It traces this fact to the differing ways in which various housing situations react to mental illness and to legislation passed in the 1960’s, which allocated public funding away from state mental hospitals.
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Simulating A Physical Internet Enabled Mobility Web: The Case Of Mass Distribution In France D. Hakimi, B. Montreuil, and others. 9th International Conference of Modeling, Optimization and Simulation

Physical Internet (PI) is a novel concept aiming to render more economically, environmentally and socially efficient and sustainable the way physical objects are transported, handled, stored, realized, supplied and used throughout the world. It enables, among other webs, the Mobility Web which deals with moving physical objects within an interconnected set of unimodal and multimodal hubs, transits, ports, roads and ways.
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Quantitative analysis of bidding strategies: a hybrid agent based–system dynamics approach Arash MAHDAVI and Makarand HASTAK, Purdue University

Economic slowdown and construction demand shrinkage reduces the profit backlog for construction contractors and bites into their profit margin. The resulting fierce competition for jobs forces construction companies to look for more sophisticated analytical tools to analyze and improve their bidding strategies. For each contractor, bidding strategy is a decision-making process that is driven by the firm’s financial goals with the final objective of maximizing the firm’s gross profit and surpassing the breakeven point. This paper proposes a methodology to model and analyze different bidding strategies with hybrid agent based-system dynamics (ABSD) simulation.
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Electric Vehicle Driver Simulation using Agent-Based Modeling Beaudry Kock, Recargo, Inc., 2014

Recargo has been developing an agent-based model with the AnyLogic tool to help us simulate the charging patterns of electric vehicle drivers in California. Our goal is to better understand the potential value from delivering electricity grid services with these vehicles. Development has only been underway for a few weeks, but in that time we’ve been able to use AnyLogic’s accessible interface and Java coding tools to quickly build and test a proof-of-concept model with which we can explore the potential for a more sophisticated and complex effort.
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Development of an Agent-Based Model for the Secondary Threat Resulting from a Ballistic Impact Event Matthew J. Bova, Frank W. Ciarallo, Raymond R. Hill

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. An assessment of the risk of such a fire begins with a complete characterization of the secondary threat resulting from the impact, including debris fragment sizes, states of motion, and thermal properties. In the aircraft survivability community, there is a need for an analytical tool to model this complete threat.
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Comparison between Individual-based and Aggregate Models in the context of Tuberculosis Transmission Tian, Y. and Osgood, N., Proceedings, The 29 th International conference of the System Dynamics Society. July 2011, Washington, D.C.

The desire to better understand the transmission of infectious disease in the real world has motivated the representation of epidemic diffusion in the context of quantitative simulation. In recent decades, both individual-based (such as Agent-Based) models and aggregate models (such as System Dynamics) are widely used in epidemiological modeling. This paper compares the difference between system dynamics models and agent-based models in the context of Tuberculosis (TB) transmission, considering smoking as a risk factor.
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