An agent-based simulation approach for dual toll pricing of hazardous material transportation

Abstract:

A dual toll pricing is a conceptual policy in which policy maker imposes toll on both hazardous materials (hazmat) vehicles as well as regular vehicles for using populated road segments to mitigate a risk of hazmat transportation. It intends to separate the hazmat traffic flow from the regular traffic flow via controlling the dual toll. In order to design the dual toll pricing policy on a highly realistic road network environment and detailed human behaviors, an extended BDI framework is employed to mimic human decision behaviors in great detail. The proposed approach is implemented in AnyLogic® agent based simulation software with using a traffic data of Albany, NY. Also, search algorithms in OptQuest® are used to determine the optimum dual toll pricing policy which results in the minimum risk and travel cost based on the simulation results. The result reveals the effectiveness of the proposed approach in devising a reliable policy under the realistic road network conditions.

Application of a generic simulation model to optimize production and workforce planning at an automotive supplier

 
Abstract:

This paper presents a comprehensive simulation project in the area of an automotive supplier. The company produces car styling serial and original accessory parts made from plastic for internal and external applications in passenger cars. For the foaming division, which is identified as the bottleneck, different personnel and qualification scenarios, set-up optimizations and lot-sizing strategies are compared with the current situation. Key performance measures reported are inventory, tardiness and service level. The changes in organizational costs (e.g. employee training, additional employees, etc.), due to the scenarios, are not considered and are traded off with the logistical potential by the company itself. Results of the simulation study indicate that a combination of an additional fitter during night shift, minor reductions of set-up times and reduced lot-sizes leads to an inventory reduction of ~10.6% and a service level improvement of ~8% compared to the current situation.

Agile logistics simulation and optimization for managing disaster responses

  Abstract:

Catastrophic events such as hurricanes, earthquakes or floods require emergency responders to rapidly distribute emergency relief supplies to protect the health and lives of victims. In this paper we develop a simulation and optimization framework for managing the logistics of distributing relief supplies in a multi-tier supply network. The simulation model captures optimized stocking of relief supplies, distribution operations at federal or state-operated staging facilities, demand uncertainty, and the dynamic progression of disaster response operations. We apply robust optimization techniques to develop optimized stocking policies and dispatch of relief supplies between staging facilities and points of distribution. The simulation framework accommodates a wide range of disaster scenarios and stressors, and helps assess the efficacy of response plans and policies for better disaster response.

Comparison between Individual-based and Aggregate Models in the context of Tuberculosis Transmission

 Abstract:

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 models and aggregate models (such as System Dynamics) are widely used in epidemiological modeling. This paper compares the difference between aggregate models and individual-based models in the context of Tuberculosis (TB) transmission, considering smoking as a risk factor. The merits and impact of capturing individual heterogeneity is examined via representing Bacillus Calmette-Gurin vaccination and reactivation in both models. The simulation results of the two models exhibit distinct discrepancies in TB incidence rate and prevalence. Results also suggest that, at the level of practical application, individual-based models offer significantly greater accuracy and easier extension, especially when representing a decreasing reactivation rate, waning of immunity and heterogeneous individual attributes. Another experiment sought to evaluate the impact of network structure on TB diffusion. 

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

Abstract:

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. This paper approaches the problem by proposing an agent-based simulation model of the fragments in a debris cloud. An analytical/empirical impact fragmentation model is developed for incorporation into the simulation model, which determines fragment sizes and states of motion. Future work focuses on an agent-based approach to modeling the thermal profile of the threat, treating each fragment in the cloud as an individually (though not autonomously) cooling “lump” of uniform temperature. Development and study of this proof-of-concept effort leads to a deeper understanding of such secondary threats and demonstrates the value of agent-based simulation models as analytical tools.

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