Description

This virtual downloadable product comprises a Python framework with an executable example application. The framework can be used for modeling pallet warehouses and pallet stacks operated by forklifts or handling machines, integrated into a production plant. The project is developed in Python, using SimPy (a free and simple simulation engine in Python), and supports parametrization of relevant model settings. This downloadable virtual product furthermore also supports tracking of relevant KPIs. You can use the downloaded framework to model, dimension, and analyze your own pallet stack or pallet warehouse (automated or manually).

If you want to deviate from the current assumptions, customization will be required. This means that you have to edit the framework code. If you need assistance with this, you are welcome to contact the responsible model developer. Adjusting the framework to your individual specific additional needs can usually be done within a few consulting hours.

Assumed pallet stack layout and framework elements

Below illustration highlights the assumed layout components: Aisle, rack, ASRS machine or forklift, and interchange.

 

Next, a side view of the assumed high-bay pallet storage and its elements.

Parameters – assumptions, constraints, and configuration

The default version of this framework makes the following assumptions:

• Random compartment selection
• Defined speed and acceleration for forklift / handling machine, for x and y axis
• Defined handling time, for load handling at compartment and at inlet/outlet (= interchange)
• Only one forklift or handling machine per aisle
• Every compartment has the same height and width
• There is only one storage zone, i.e. any pallet can be stored anywhere in the aisle, in any of the compartments
• All aisles are assumed to have the same dimensions, i.e. same length and same rack dimensions
• Two interchanges per aisle, with defined and configurable positions
• AGVs deliver pallets to the storage aisle, and after their pallet has been put away, they receive another pallet from the same aisle

Deviations from above assumptions can be implemented by customizing the framework and its code. The responsible model developer can help you with this. Please contact us for support.

The default framework supports the following parametrization, which can be easily adjusted via a configuration file:

• machine speed x: top speed of forklift or handling machine in x axis direction, in [m/s]
• machine speed z: top speed of forklift or handling machine in z axis direction, in [m/s]
• machine acc x: linear acceleration of forklift or handling machine in x axis direction, in [m/s2]
• machine acc z: linear acceleration of forklift or handling machine in z axis direction, in [m/s2]
• machine handlingtime comp: load handling time at compartment, in [s]
• machine handlingtime ic: load handling time at interchange (inlet / outlet), in [s]
• machine delaytime: additional delay time (for e.g. safety speed distance), in [s]
• n aisles: number of aisles in the high-bay storage
• n aisle columns: number of columns per rack, per aisle
• n aisle column width: width of a column, in [m]
• n aisle tiers: number of tiers in a rack
• aisle tierheight offset: height, in z direction, of the first tier (height offset of the first tier), in [m]
• aisle tierheight: height of a compartment, in [m]
• aisle xoffset ic: offset of the inlet/outlet interchange, from aisle center point in x direction, in [m]
• aisle zoffset ic: surface area offset (on which pallet rests for pick or delivery by forklift / handling machine), at interchange, in z direction [m]
• throughput: target throughput (number of retrievals and putaways, in sum – each with a 50% share)
• sim lenght: length of simulation run, in [s]

Exemplary model output

The framework, by default, supports the collection and tracking of storage / warehousing related data, specifically with regards to throughput and state distribution of the relevant machines (or forklifts, in case of a manually operated warehouse). The amount of busy and idle time, moves, putaways, retrievals, and the cycle time history are tracked for each machine / forklift. Using this data, by default, the framework e.g. supports visualizations such as the ones displayed below.