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Simulate
Explore powerful simulation algorithms for System Dynamics and Agent
Based Modeling. Use System Dynamics to gain insights into your
system and Agent Based Modeling to dig into the details. Types of Modeling
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Explore What Others Are Building
Here is a sample of public Insights made by Insight Maker users. This list is auto-generated and updated daily.
Bienvenue sur le simulateur du modèle à flux constants.
Ce modèle est basé sur notre cas d'étude simplifié : un groupe de guépards dans un parc national géré, où les entrées et sorties sont contrôlés par les gestionnaires (translocation entre parcs).
Fonctionnement :
Le [Variable d'état] qui représente l'effectif de guépards (le nombre d'individus).
Les [Flux] (B, I, D, E) sont le nombre fixe d'individus qui entrent ou sortent à chaque pas de temps.
Variables forçantes : Les décisions des gestionnaires sont les "variables forçantes" qui rendent ces flux constants.
Votre mission :
Utilisez les curseurs pour régler les conditions et lancez la simulation en cliquant sur le bouton "SIMULATE" en haut à droite, pour observer comment l'effectif évolue sur le graphique !
A look into the population growth in Whitby over the next 100 years. The overall population would exceed the birth and death rate. The model doesn’t account for immigration/emigration and other possible factors.
La sucesión en los bosques puede ser modelada como un proceso de reemplazo de árboles individuales de distintas especies. Consideraremos el caso de un modelo de sucesión para un bosque dominado por cuatro especies de árboles: Maple rojo (Acer rubrum), Roble rojo (Quercus rubra), Fresno blanco (Fraxinus americana) y Haya americano (Fagus grandifolia). El paso temporal del modelo corresponde a la longevidad promedio de todas las especies y en cada paso un árbol individual puede ser reemplazado por un árbol joven de la misma u otra especie. Dado que cada uno de estos reemplazos tiene una probabilidad asociada (Figura 1), puede construirse una matriz que represente las probabilidades de transición en cada caso (Tabla 1). Un supuesto importante en este modelo es que las probabilidades de transición se mantienen constantes a través del tiempo.
A random walk demonstration using an ABM. As individuals drink more they become more intoxicated and their walk becomes more random. And when they drink to much it finally kills them.
This simulation allows you to compare different approaches to influence flow, the Flow Times and the throughput of a work process.
By adjusting the sliders below you can
observe the work process without any work in process limitations (WIP Limits),
with process step specific WIP Limits* (work state WIP limits),
or you may want to see the impact of the Tameflow approach with Kanban Token and Replenishment Token
or see the impact of the Drum-Buffer-Rope** method.
* Well know in (agile) Kanban
** Known in the physical world of factory production
The "Tameflow approach" using Kanban Token and Replenishment Token as well as the Drum-Buffer-Rope method take oth the Constraint (the weakest link of the work process) into consideration when pulling in new work items into the delivery "system".
You can also simulate the effects of PUSH instead of PULL.
Feel free to play around and recognize the different effects of work scheduling methods.
If you have questions or feedback get in touch via twitter @swilluda
The work flow itself
Look at the simulation as if you would look on a kanban board.
The simulation mimics a "typical" software delivery process.
From left to right you find the following ten process steps.
Input Queue (Backlog)
Selected for work (waiting for analysis or work break down)
