A multi-paradigm, whole system view of health and social care for age-related macular degeneration Joe Viana, Stuart Rossiter, Andrew A. Channon, Sally C. Brailsford, Andrew Lotery, WSC-2012, Berlin

This paper presents a hybrid simulation model for the management of an eye condition called age-related macular degeneration, which particularly affects the elderly. The model represents not only the detailed clinical progression of disease in an individual, but also the organization of the hospital clinic in which patients with this condition are treated and the wider environment in which these patients live (and their social care needs, if any, are met). The model permits a ‘whole system’ societal view which captures the interactions between the health and social care systems
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Hybrid simulation of renewable energy generation and storage grids Peter Bazan, Reinhard German, WSC-2012, Berlin

The share of renewable energy sources in energy production is growing steadily. Domestic homes can be equipped with solar panels, micro combined heat and power systems, batteries, and they can become adaptive consumers. They can also deliver energy to the grid and react to the energy supply. This paper presents a hybrid simulation approach for the analysis of a grid of domestic homes equipped with different technological options with respect to efficiency and costs. For energy storage and energy flows the system dynamics modeling paradigm is used whereas control decisions are modeled as statecharts. The highly intermittent solar irradiation and also the electric power and heat demands are implemented as stochastic models. The component-based design allows for quick creation of new case studies. As examples, different homes with batteries, micro combined heat and power systems, or energy carrier carbazole as energy storage are analyzed
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Complex agent interactions in operational simulations for aerospace design Benjamin Schumann, James Scanlan, Hans Fangohr, WSC-2012, Berlin

Product complexity in the aerospace industry has grown fast while design procedures and techniques did not keep pace. Product life cycle implications are largely neglected during the early design phase. Also, aerospace designers fail to optimize products for the intended operational environment. This study shows how a design, simulated within its anticipated operational environment, can inform about critical design parameters, thereby creating a more targeted design improving the chance of commercial success. An agent-based operational simulation for civil Unmanned Aerial Vehicles conducting maritime Search-andRescue missions is used to design and optimize aircrafts. Agent interactions with their environment over the product life-cycle are shown to lead to unexpected model outputs. Unique insights into the optimal design are gained by analysis of the operational performance of the aircraft within its simulated environment
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Hybrid simulation with loosely coupled system dynamics and agent-based models for prospective health technology assessments Anatoli Djanatliev, Reinhard German, Peter Kolominsky-Rabas, Bernd M. Hofmann, WSC-2012, Berlin

Due to the ageing of the world population, the demand for technology innovations in healthcare is growing rapidly. All stakeholders (e.g., patients, healthcare providers and health industry) can take profit of innovative products, but the development degenerates often into a time consuming and cost-intensive process. Prospective Health Technology Assessment (ProHTA) is a new approach that combines the knowledge of an interdisciplinary team and uses simulation techniques to indicate the effects of new innovations early before the expensive and risky development phase begins. In this paper, we describe an approach with loosely coupled system dynamics and agent-based models within a hybrid simulation environment for ProHTA as well as a use-case scenario with an innovative stroke technology
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The service productivity learning cockpit – a business-intelligence tool for service enterprises Prof. Dr. Torsten Eymann, Universität Bayreuth, Lehrstuhl für Wirtschaftsinformatik

Computer simulation is a way to imitate business processes based on reality. Due to the fact that the environment in hospitals is highly dynamic with local autonomy of stakeholders participating in the business processes, we found an agent–based modeling and simulation (ABMS) approach to be most suitable and it is therefore applied in this context. From an inception to a running simulation, followed by an analysis of the output, we need to keep in mind our user’s physical problem as well as their capability of digesting the results. An interface between a computer modeler/programmer’s deliverable and a user like a hospital manager who learns from the simulated behavior of physical reality, is a visualization tool. We call this tool a “Learning Cockpit” (LC). Although a manager has experience in managing their business and they use personal qualities to positively drive their organization in challenging business environments, a simulation provides them additional support in decision process. With the help of simulation, they should be able to clearly and concisely grasp the information about the current operations, the resources involved and the inherent costs to get an output. They should be able to measure the performance of the current setup, and if necessary, make some changes and bring more value to the organization
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A multi-structural framework for adaptive supply chain planning Ivanov D.A., Sokolov B., Kaeschel J., European Journal of Operational Research, 2009

A trend in up-to-date developments in supply chain management (SCM) is to make supply chains more agile, flexible, and responsive. In supply chains, different structures (functional, organizational, informational, financial etc.) are (re)formed. These structures interrelate with each other and change in dynamics. The paper introduces a new conceptual framework for multistructural planning and operations of adaptive supply chains with structure dynamics considerations. We elaborate a vision of adaptive supply chain management (A-SCM), a new dynamic model and tools for the planning and control of adaptive supply chains. SCM is addressed from perspectives of execution dynamics under uncertainty. Supply chains are modelled in terms of dynamic multi-structural macro-states, based on simultaneous consideration of the management as a function of both states and structures. The research approach is theoretically based on the combined application of control theory, operations research, and agent-based modelling. The findings suggest constructive ways to implement multi-structural supply chain management and to transit from a “one-way” partial optimization to the feedbackbased, closed-loop adaptive supply chain optimization and execution management for value chain adaptability, stability and crisis-resistance. The proposed methodology enhances managerial insight into advanced supply chain management
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The aero-engine value chain under future business environments David Buxton, Richard Farr, Bart Maccarthy. MITIP2006, 11-12 September, Budapest

Agent-based modelling is gaining popularity  for investigating the behaviour of complex systems involving interactions of many players or agents. In this paper an agent-based simulation modelling technique is applied to understand the long term implications of strategy decisions for an aerospace value chain. The industry has unique elements including new business models, high levels of collaboration, long product lifecycles and long periods before positive paybacks are realised. Forecasting market conditions over this long term lifespan is inherently problematic and adds  further complexity when devising a strategy. The model described includes all the major players and entities in the value chain and their interactions. Illustrative results are presented to demonstrate how the simulation approach can be used to evaluate strategy and policy decisions and their operational implications over the long term
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Using AnyLogic and agent based approach to model consumer market Maxim Garifullin, Andrei Borshchev, Timofei Popkov. EUROSIM 2007, September 9-13, Ljubljana, Slovenia

In the highly dynamic, competitive and complex market environments (such as telecom, insurance, leasing, health, etc) the consumer’s choice essentially depends on a number of individual characteristics, inherent dynamics of the consumer, network of contacts and interactions, and external influences that may be best captured within the Agent Based modeling paradigm. The Agent Based modeling is especially advantageous in the consumer market domain as it allows to leverage the full amount of individual-centric data from the CRM (Customer Relationships Management) systems highly available these days. Although there are no universal straightforward instructions for building Agent Based models, there are certain common steps and patterns. The goal of this paper is to introduce the patterns in consumer market modeling most frequently met in our consulting practice. The modeling language of AnyLogic is used throughout the paper
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Fully agent based modellings of epidemic spread using AnyLogic Štefan Emrich, Sergej Suslov, Florian Judex. EUROSIM 2007, September 9-13 2007, Ljubljana, Slovenia.

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Heterogeneity and network structure in the dynamics of diffusion Hazhir Rahmandad , John Sterman. MANAGEMENT SCIENCE Vol. 5. No. 5. May 2008

When is it better to use agent-based (AB) models, and when should differential equation (DE) models be used? Whereas DE models assume homogeneity and perfect mixing within compartments, AB models can capture heterogeneity across individuals and in the network of interactions among them. AB models relax aggregation assumptions, but entail computational and cognitive costs that may limit sensitivity analysis and model scope. Because resources are limited, the costs and benefits of such disaggregation should guide the choice of models for policy analysis. Using contagious disease as an example, we contrast the dynamics of a stochastic AB model with those of the analogous deterministic compartment DE model. We examine the impact of individual heterogeneity and different network topologies, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Obviously, deterministic models yield a single trajectory for each parameter set, while stochastic models yield a distribution of outcomes.
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