A high-performance logistics optimization tool using Mixed-Integer Linear Programming to determine optimal warehouse locations and delivery routes.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install warehouse-network-optimizer
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 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 system designed to solve complex supply chain distribution challenges. By utilizing Mixed-Integer Linear Programming (MILP) powered by the PuLP and CBC solvers, it enables businesses to model and optimize their Regional Distribution Center (RDC) networks with precision. This tool is a standout among Openclaw Skills for its ability to handle multi-level constraints, including transit times, wait times due to vehicle schedules, and complex supply chain hierarchies.
It provides a robust framework for minimizing weighted average lead times across entire store networks. The system intelligently handles various route types, such as direct delivery, single-transfer routes, and transit center (TC) pathways, ensuring that the resulting logistics network is both cost-effective and highly efficient.
Warehouse Network Optimizer Use Cases
- Strategic planning for Regional Distribution Center (RDC) site selection.
- Minimizing weighted average lead times for large-scale retail store networks.
- Simulating the impact of warehouse constraints and mandatory/excluded sites.
- Analyzing logistics efficiency across provinces and cities with multi-tier matching logic.
- Calculating total lead time including trunk line transit, distribution cycles, and vehicle wait times.
How Warehouse Network Optimizer Works
- Data Acquisition: Import store locations, trunk line transit data, and distribution cycle schedules from Excel or JSON files.
- Preprocessing: Utilize the smart unit detection and conversion scripts to standardize time measurements and geographic mapping.
- Parameter Configuration: Define optimization constraints such as RDC quantity upper limits, mandatory warehouses, and forbidden sites.
- MILP Solving: The core engine applies Mixed-Integer Linear Programming to minimize the objective function (Sum of store count × route lead time).
- Reporting: Generate a detailed multi-sheet Excel report containing executive summaries, RDC selections, and granular supply chain relationship maps.
Warehouse Network Optimizer Setup
Prerequisites
Install the required Python environment and libraries:
pip install pulp pandas openpyxl
Data Preparation
Convert your logistics data into a processable format:
# Convert trunk line data (supports auto-unit detection)
python3 convert_data.py --trunk trunk_data.xlsx --output-dir .
# Convert distribution cycle data
python3 convert_data.py --distribution distribution_cycles.xlsx --output-dir .
Execution
Run the optimization engine by specifying the store file and RDC limit:
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 across several key structures to ensure precision in Openclaw Skills workflows:
| Data Type | Format | Key Fields |
|---|---|---|
| Store Data | Excel/JSON | Province, City, District, Detailed Address |
| Trunk Data | Excel/JSON | Route Origin/Destination, Transit Time, Frequency |
| Distribution | Excel/JSON | Warehouse Reporting Cycles, Local Delivery Lead Time |
| Output Report | Excel | Executive Summary, RDC Selection, Supply Chain Mapping, Province Distribution |
Lead times are calculated as: Total Lead Time = Wait Time + Trunk Lead Time + Distribution Lead Time.
Warehouse Network Optimizer Advanced Features
- Flexible RDC Limits: Optimization logic now uses 'less than or equal to N' to find the mathematically optimal number of warehouses without over-building.
- Smart Unit Detection: Automatically converts hours to days and standardizes time metrics across disparate data sources.
- Geographic Fallback Logic: Implements a priority-based matching system (City Exact Match > Province Fuzzy Match > Inferred Province).
- Multi-Agent Integration: Designed to work within automated AI agent workflows for iterative supply chain simulation.
- Comprehensive Logistics Decomposition: Breaks down lead times into granular components (wait time, transit, and delivery) for bottleneck analysis.
SKILL.md
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