Petri-Net Warehouse Workflows for Flexible WMS–ASRS Integration
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Solution Overview
Problem
The integration of Warehouse Management Systems (WMS) with automated storage and retrieval systems in warehouses is costly, time-consuming, and inflexible, with modifications leading to high risks of errors and requiring extensive knowledge of the underlying system semantics, making it difficult to accommodate changing material flow handling (MFH) processes and integrate with adjacent hardware components.
Innovation Solution
A computer-implemented method using a petri net model and domain-specific language (DSL) for warehouse operations, allowing warehouse operators to modify workflows easily and reducing integration complexity by using a constrained set of abstractions and cooperative threads for concurrent task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the WMS is hard-coded to integrate with the automated storage and retrieval system, then the integration is stable and reliable, but the system becomes inflexible and difficult to modify when MFH processes need to change
Solution Approach 1:
The system is segmented into distinct layers: the WMS layer, the operating system layer with DSL interpreter, and the hardware control layer. This segmentation allows the WMS to remain stable while the DSL scripts at the intermediate layer can be modified to accommodate changing MFH processes without affecting the core WMS integration.
Solution Approach 2:
The system introduces dynamic script files written in DSL that can be executed by the operating system. These scripts provide the flexibility to modify MFH processes dynamically without requiring changes to the hard-coded WMS integration, allowing the system to adapt to changing requirements while maintaining stable core functionality.
2Adaptability or versatility
If the integration between WMS and automated storage and retrieval system is modified to accommodate changing MFH processes, then the system becomes more flexible, but the risk of errors and bugs increases significantly
Solution Approach 1:
The DSL interpreter and script files serve as an intermediary layer between the WMS and the automated storage and retrieval system. This intermediary allows process modifications to be made through script changes rather than core system modifications, significantly reducing the risk of introducing bugs while maintaining flexibility.
Solution Approach 2:
Instead of modifying the core WMS integration code, the system uses separate DSL script files that copy or replicate the necessary process logic in a modifiable format. These scripts can be updated, tested, and validated independently before execution, reducing the risk of errors in the core system.
3Adaptability or versatility
If multiple parameters are used to define MFH process variants to accommodate different warehouse operators, then the system becomes more adaptable, but the number of variants explodes combinatorially making testing impossible
Solution Approach 1:
The system uses dynamic DSL scripts that can be configured and executed differently for various warehouse operators without creating separate code paths for each combination. This allows unlimited customization while maintaining a manageable number of script variants through parameterized script execution rather than combinatorial code multiplication.
Solution Approach 2:
The system allows MFH process variants to be defined through parameter changes in DSL scripts rather than creating entirely separate process definitions. This enables different warehouse operators to have customized processes by modifying script parameters and configurations, avoiding the combinatorial explosion of full process variants while maintaining adaptability.
4Productivity
If the WMS integration is hard-coded with detailed knowledge of automated storage and retrieval system semantics, then the integration is precise and efficient, but the cost and time required for initial integration increases
Solution Approach 1:
The system extracts the detailed semantic knowledge of automated storage and retrieval operations from the WMS and places it in separate DSL script files. This extraction allows the WMS to integrate more quickly without embedding all the complex domain knowledge, while the DSL scripts provide the necessary operational semantics when executed.
Solution Approach 2:
The DSL scripts are prepared in advance with the detailed operational knowledge embedded, allowing the WMS integration to proceed more quickly. The preliminary preparation of script files with domain-specific semantics enables faster initial integration while maintaining precise control over automated storage and retrieval operations.
Data Source
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Figure 3A~3B
AI summary
The disclosure relates to a computer-implemented method for processing a material flow handling operation in a warehouse comprising an automated storage and retrieval system, wherein the method is carried out using one or more processors of one or more computing devices, and wherein the method comprises: identifying an execution model associated with a workflow, wherein the execution model identifies a plurality of tasks to be carried out in the warehouse in response to a workflow-initiating event, wherein the plurality of tasks are associated with the workflow-initiating event using a petri net model; and executing the plurality of tasks using a plurality of cooperative threads executing in user space of the one or more computing devices.