The initial battery level – when a transporter is initiated for the first time – is stochastic and assigned according to the triangular probability distribution and parameters (min, max, mode).

We’ve chosen liters as the most appropriate unit from the list in the Fluid Library. The battery’s capacity is set to 1000 liters. Let’s take a closer look at the Battery’s properties.įlowchart to simulate battery life cycle and the Battery’s properties (click to enlarge)

In this model, a Tank block helped simulate the AGV battery level (in %) and a Valve block – the charging and discharging of the tank (battery). The Fluid Library helps to simulate the storing and transfer of large amounts of discrete items, for example, liquid, grain, and energy. Later, you can call this stopDelay() function from the AGV’s statechart using Java code.īattery flowchart inside an AGV agent typeĪnother simple flowchart is used to describe the battery life cycle. One more step to making the model work correctly is to change the mode (type) of the Delay block to “Until stopDelay() is called”. According to the logic described in the flowchart, once the charging task is complete, the AGV gets back to its routine. The “task priority” parameter value equals 1000 which makes it more important than the unloading task. This query is supposed to interrupt the truck unloading process since the “task may preempt” policy is chosen in the properties of the chargetask block. AGV charging model’s logic – chargetask block’s properties (click to enlarge) Meanwhile, the second flowchart has more actions and starts with the Enter block, which simulates a manually created query – “СhargeTask”. Then transporters seize them for a “to-rack” delivery. Tasks assignment Flowcharts describing task assignment for AGVs: SKU placement (click to enlarge)Īs you can see in the picture, the first flowchart makes the SKUs appear. The most important part of the model’s logic is the charging-related processes inside an AGV agent.

On the right side of the warehouse, there’s an unloading gate where the tasks for transporters appear. In the lower left corner, there’s an AGV base and a charging station. The model’s layout is quite simple yet serves as a good example of the interconnection between the agent’s nature and its discrete-event behavior modeled with the Fluid Library. Another one – Material Handling Library – is especially useful for manufacturing and warehousing modeling and in this case it helped with AGVs (transporters) and racks. In this model, one of them – Fluid Library – was used to simulate the battery life cycle. On top of that, the model is comprised of the discrete-event and agent-based simulation modeling approaches.įor discrete-event modeling, AnyLogic offers built-in libraries. The model describes a truck unloading process and SKU placement according to the available storage slots in a warehouse. The model was developed in AnyLogic 8.7.12 featuring new Material Handling Library functionality, such as new storage racks and respective logic blocks Store and StorageSystem, that aids warehouse operations modeling.

Anylogic statechart tutorial