Modeling dynamic interactions in supply chains using agentbased simulations

In this work, we present preliminary results of our research on the construction of an agent-based simulation framework suitable to support the analysis of complex supply chain interactions as the one required for the performance assessment in collaborative supply chains. In particular we focus in the modeling of dynamic interactions through agent-to-agent message communication avoiding predefined supply chain network structures. For defining the internal structure of agents we explore the application of the SCOR reference model to bring a business process perspective and adopt the requirement of making explicit separation of the execution and control and decision making processes.Sociedad Argentina de Informática e Investigación Operativ

Modeling dynamic interactions in supply chains using agentbased simulations

In this work, we present preliminary results of our research on the construction of an agent-based simulation framework suitable to support the analysis of complex supply chain interactions as the one required for the performance assessment in collaborative supply chains. In particular we focus in the modeling of dynamic interactions through agent-to-agent message communication avoiding predefined supply chain network structures. For defining the internal structure of agents we explore the application of the SCOR reference model to bring a business process perspective and adopt the requirement of making explicit separation of the execution and control and decision making processes.Sociedad Argentina de Informática e Investigación Operativ

A Hybrid Simulation Model for Studying Acute Inflammatory Response

The modeling of complex biological systems presents a significant challenge. Central to this challenge is striking a balance between the degree of abstraction required to facilitate analysis and understanding, and the degree of comprehensiveness required for fidelity of the model to its reference-system. It is likely necessary to utilize multiple modeling methods in order to achieve this balance. Our research created a hybrid simulation model by melding an agent-based model of acute local infection with a system dynamics model that reflects key systemic properties. The agent based model was originally developed to simulate global inflammation in response to injury or infection, and has been used to simulate clinical drug trials. The long term objective is to develop models than can be scaled up to represent organ and system level phenomena such as multiple organ failure associated with severe sepsis. The work described in this paper is an initial proof of concept of the ability to combine these two modeling methods into a hybrid model, the type of which will almost certainly be needed to accomplish the ultimate objective of comprehensive in silico research platforms

A template model for agent-based simulations

ABSTRACT Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decision-making agent. ABMs are very sensitive to implementation details. Thus, it is very easy to inadvertently introduce changes which modify model dynamics. Such problems usually arise due to the lack of transparency in model descriptions, which constrains how models are assessed, implemented and replicated. In this paper, we present a template ABM which aims to serve as a basis for a series of investigations, including, but not limited to, conceptual model specification, statistical analysis of simulation output, model comparison and model parallelization. This paper focuses on the first two aspects (conceptual model specification and statistical analysis of simulation output), also providing a canonical implementation of the template ABM, such that it serves as a complete reference to the presented model. Additionally, this study is presented in a tutorial fashion, and can be used as a road map for simulation practitioners who wish to improve the way they communicate their ABMs

Modeling dynamic interactions in supply chains using agent-based simulations

In this work, we present preliminary results of our research on the construction of an agent-based simulation framework suitable to support the analysis of complex supply chain interactions as the one required for the performance assessment in collaborative supply chains. In particular, we focus in the modeling of dynamic interactions through agent-to-agent message communication avoiding predefined supply chain network structures. For defining the internal structure of agents, we explore the application of the SCOR reference model to bring a business process perspective and adopt the requirement of making explicit separation of the execution and control and decision making processes.Fil: Dorigatti, Mariana Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); ArgentinaFil: Chiotti, Omar Juan Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); ArgentinaFil: Salomone, Hector Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); Argentin

Simulating the decentralized processes of the human immune system in a virtual anatomy model

BACKGROUND: Many physiological processes within the human body can be perceived and modeled as large systems of interacting particles or swarming agents. The complex processes of the human immune system prove to be challenging to capture and illustrate without proper reference to the spacial distribution of immune-related organs and systems. Our work focuses on physical aspects of immune system processes, which we implement through swarms of agents. This is our first prototype for integrating different immune processes into one comprehensive virtual physiology simulation. RESULTS: Using agent-based methodology and a 3-dimensional modeling and visualization environment (LINDSAY Composer), we present an agent-based simulation of the decentralized processes in the human immune system. The agents in our model - such as immune cells, viruses and cytokines - interact through simulated physics in two different, compartmentalized and decentralized 3-dimensional environments namely, (1) within the tissue and (2) inside a lymph node. While the two environments are separated and perform their computations asynchronously, an abstract form of communication is allowed in order to replicate the exchange, transportation and interaction of immune system agents between these sites. The distribution of simulated processes, that can communicate across multiple, local CPUs or through a network of machines, provides a starting point to build decentralized systems that replicate larger-scale processes within the human body, thus creating integrated simulations with other physiological systems, such as the circulatory, endocrine, or nervous system. Ultimately, this system integration across scales is our goal for the LINDSAY Virtual Human project. CONCLUSIONS: Our current immune system simulations extend our previous work on agent-based simulations by introducing advanced visualizations within the context of a virtual human anatomy model. We also demonstrate how to distribute a collection of connected simulations over a network of computers. As a future endeavour, we plan to use parameter tuning techniques on our model to further enhance its biological credibility. We consider these in silico experiments and their associated modeling and optimization techniques as essential components in further enhancing our capabilities of simulating a whole-body, decentralized immune system, to be used both for medical education and research as well as for virtual studies in immunoinformatics