Peer-Olaf Siebers, Uwe Aickelin, Helen Celia, Chris W. Clegg. EUROSIM 2007, September 9-13 2007, Ljubljana, Slovenia.

“Intelligent agents offer a new and exciting way of understanding the world of work. In this paper we apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents could offer potential for fostering sustainable organizational capabilities in the future. Our research so far has led us to conduct case study work with a top ten UK retailer, collecting data in four departments in two stores. Based on our case study data we have built and tested a first version of a department store simulator. In this paper we will report on the current development of our simulator which includes new features concerning more realistic data on the pattern of footfall during the day and the week, a more differentiated view of customers, and the evolution of customers over time. This allows us to investigate more complex scenarios and to analyze the impact of various management practices…”

Photon, 4th Production Equipment Conference , 05.03.2009 , Munich.

Presentation is prepared by Dr. Roland Sturm, acp-IT; Dr. HartmutGross, centrotherm Photovoltaics; JörgTalaga, centrotherm Photovoltaics.

Tutorial based on the materials of AnyLogic workshop, System Dynamics Conference 2008.

“AnyLogic allows you to build a simulation model using multiple methods: System Dynamics, Agent Based and Discrete Event (Process‐centric) modeling. Moreover, you can combine different methods in one model: put agents into an environment whose dynamics is defined in SD style, use process diagrams or SD to define internals of agents, etc, etc. Any kind of mixed architecture is possible due to flexible object‐oriented AnyLogic modeling language…”

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…”

Štefan Emrich, Sergej Suslov, Florian Judex. EUROSIM 2007, September 9-13 2007, Ljubljana, Slovenia.

“The question how to model the spread of epidemics has been approached countless times. The number of different methods used on this problem is not too small either. Ordinary differential equations (ODE) and Partial differential equations (PDE) have dominated this field for several decades, if not centuries. In the second half of the last century two alternative techniques appeared on the stage, namely cellular automata (CA) and agent based (AB) models, also called multi agent systems (MAS). The difference between the approach with differential equations and the latter two methods is big. CA and MAS are so called “bottom-up” approaches, focusing on the smallest unit of the system – a cell or agent, whereas ODEs try to model the system via causal connections on the macroscopic level. Setting up a SIR-type model using the AB approach one can take advantage of state charts to control the behavior of agents. Using AnyLogic as implementation platform agents and especially state charts can be programmed very conveniently. Especially modifications and/or extensions of the final model can be handled in an elegant way. The right figure does show all necessary adjustments to expand the SIR- to a SIRS-type epidemic (additional state transition highlighted). The results obtained by simulation with such an MAS are comparable to those of the ODE- and CA-approach, although AB modeling offers a higher degree of freedom and thus more possibilities of adjustment…”

Christian Almeder, Margaretha Preusser. EUROSIM 2007, September 9-13 2007, Ljubljana, Slovenia.

“The main idea of our approach is to combine discrete-event simulation and exact optimization for supply chain network models. Simulation models are constructed in order to mimic a real system including all necessary stochastic and nonlinear elements. Such simulation models are used as proving grounds for analyzing and improving a real situation on a trial-and-error basis. A traditional optimization method on top of a simulation model has major disadvantages: The optimization method uses the simulation model as a black-box. Information about the structure of the problem is not available and cannot be used for an intelligent optimization strategy…”

Maxim Garifullin, Andrei Borshchev, Timofei Popkov. EUROSIM 2007, September 9-13 2007, 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…”

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…”

Illustrated summary of the most important new features and improvements in AnyLogic 6.4.1

Ivanov D.A., International Journal of Integrated Supply Management, No. 5(1), 19-37., 2009.

"In the framework of supply chain (re)- design (SCD), different structures (functional, organizational, informational, etc.) are (re)- formed. These structures are interrelated and change in their dynamics. How is it possible to avoid structural incoherency and consistency and to achieve comprehensiveness by (re)- designing supply chains? This paper introduces a new approach to simultaneous multi-structural SCD with structure dynamics considerations. We elaborate a new conceptual model and propose new tools for multi-structural SCD – multi-structural macro-states and dynamical alternative multi-graphs..."