![]() We examine the impact of individual heterogeneity and different network topologies, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Using contagious disease as an example, we contrast the dynamics of a stochastic AB model with those of the analogous deterministic compartment DE model. Because resources are limited, the costs and benefits of such disaggregation should guide the choice of models for policy analysis. AB models relax aggregation assumptions, but entail computational and cognitive costs that may limit sensitivity analysis and model scope. 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. ![]() Simulation experiments demonstrate the purposive functionality of the model and visualize the significant influence of technical failure on the overall project performance Processrelated disturbances can be considered easily within the presented simulation model. In order to meet these defiances, this paper presents a multimethod simulation model to investigate the advancement rate of tunnel boring machines. Transparent evaluation of applicable tunnel boring machine designs is essential in order to improve the productivity, avoid unplanned interruptions and to estimate the project duration in general. Due to the sequential character the malfunction of one element might evoke cascading-effects which may result in a complete standstill of the tunneling progress. Furthermore, disturbances of critical machine components can have such impact on the production that unforeseen modifications become necessary. Gizem Günes: _Agent Based Simulation an Example in Anylogic_,ģ.Ilya Grigoryev: Anylogic 7 in Three Days: A Quick Course in Simulation Modeling.In mechanized tunneling a significant loss of performance resulting from weak spots in the supply chain or unforeseen geological conditions is a frequent and costly problem. Andrei Borshev: _The Big Book of Simulation Modeling: Multimethod Modeling with Anylogic 6, The ANYLOGIC software will be available in the lab.ġ. This practical part of the course amounts to 80% of the module.įurthermore, all case studies must be solved using the computer (AMSEL laboratory R 5306). This will take place in the PC lab AMSEL "Angewandte Mathematik, Statistik und eLearning" in Room 5306. Students will have the opportunity to practice on the computer. Projects: Traffic lights/traffic system/chairlift etc. Simulation of a production process/service area in the bankĭ. Basics of event-oriented simulation technologyģ.The simulation language AnyLogic und case studies - projectsĬ. Techniques of discrete event-oriented simulationĪ. Mathematical stochastic basics: Statistical measures and stochastic modellingĢ. Simulation methods: event-oriented, process-oriented, agent-basedĭiscrete and continuous dynamic simulationĭ. Definitions - characterization of systems and their conditionsĬ. Lastly, they will be able to find the best system configuration by studying the systemĪ. They will be able to apply methods for the _safe_ evaluation and comparison of simulation results from different scenariosĦ. Students will be able to estimate which performance measures are suitable for the evaluation of the simulation studies (utilization, queue length, dwell times, etc.) and how to determine them.ĥ. They will be able to draw up test designs for the investigating different simulation scenarios.Ĥ. Students will be able to model discrete systems and the behavior of such systems using the programming language ANYLOGIC in a suitable simulation methodology.ģ. They will be capable of choosing the simulation methodology in an event-oriented, agent-based, meaningful way and of implementing it in the language.Ģ. After successfully completing this module, students will be able to model discrete systems and the behavior of such systems by means of an abstract description language. There are therefore 105 hours available for class preparation and follow-up work and exam preparation.Īpplied Mathematics, Statistics, and eLearning (5306)ġ. The total student study time is 150 hours (equivalent to 5 ECTS credits). during the winter term).Ħ0 class hours (= 45 clock hours) over a 15-week period. The total workload is distributed on the semester (01.04.-30.09. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam. Each ECTS credit represents 30 working hours. Workload of student for successfully completing the course.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |