A high-performance logistics optimization system using Mixed-Integer Linear Programming to determine the ideal distribution center network based on lead time and cost constraints.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install warehouse-network-optimizer-skills
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install warehouse-network-optimizer-skills using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Warehouse Network Optimizer?
The Warehouse Network Optimizer is a professional-grade logistics planning tool designed for Openclaw Skills users. It utilizes PuLP and CBC solvers to perform Mixed-Integer Linear Programming (MILP) optimizations, allowing businesses to determine the most efficient Regional Distribution Center (RDC) locations based on weighted average lead times.
By analyzing complex variables such as trunk line efficiency, distribution cycles, and specific geographical constraints, this skill provides actionable insights into supply chain topology. It supports sophisticated scenarios including transit routes, mandatory warehouse inclusion, and automated lead-time calculations across every stage of the delivery lifecycle, from the origin to the final district-level destination.
Warehouse Network Optimizer Use Cases
- Designing a new logistics network from scratch by identifying optimal RDC locations.
- Optimizing existing warehouse networks to reduce weighted average lead times for retail stores.
- Evaluating the impact of adding or removing specific distribution centers using mandatory and forbidden constraints.
- Automating the conversion of legacy Excel logistics data into structured formats for analysis within Openclaw Skills.
How Warehouse Network Optimizer Works
- Prepare source data files in Excel format covering store locations, trunk lines, and distribution cycles.
- Convert Excel datasets into structured JSON format using the built-in data conversion scripts for standardized processing.
- Configure optimization parameters, such as the maximum number of RDCs (<= N) and specific warehouse constraints.
- Execute the MILP solver to calculate the global minimum for the weighted lead-time objective function.
- Generate a comprehensive Excel report detailing RDC selection, supply chain relationships, and executive performance metrics.
Warehouse Network Optimizer Setup
To get started with this skill in Openclaw Skills, install the required Python dependencies and run the optimization script:
pip install pulp pandas openpyxl
# Convert trunk line data
python3 convert_data.py --trunk trunk_data.xlsx --output-dir .
# Run the optimization with specific constraints
python3 run_optimization.py --store-file stores.xlsx --rdc-count 5 --output result.xlsx
Warehouse Network Optimizer Data Schema & Taxonomy
The Warehouse Network Optimizer organizes data through a structured pipeline to ensure accuracy in Openclaw Skills projects:
| Data Type | Format | Description |
|---|---|---|
| Store Data | Excel/JSON | Geographic hierarchy including province, city, and district levels. |
| Trunk Data | Excel/JSON | Warehouse-to-warehouse transit lines including hourly/daily lead times. |
| Distribution Cycles | Excel/JSON | Multi-sheet data containing reporting periods and local delivery times. |
| Optimization Report | Excel | Multi-sheet output including Executive Summary, RDC Selection, and Supply Chain Mapping. |
Warehouse Network Optimizer Advanced Features
- Flexible RDC Limit: Automatically determines the optimal number of warehouses up to a user-defined limit (<= N) rather than a fixed count.
- Intelligent Unit Detection: Automatically detects and converts time units (hours to days) for trunk line efficiency calculations.
- Multi-Level Store Matching: Uses prioritized logic starting from city-level precision with province-level fallbacks.
- Comprehensive Lead Time Modeling: Includes transit time, distribution time, and waiting time caused by dispatch schedules.
SKILL.md
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