Migrate Module

Automate the migration of research code modules from the SDR_stochastic project into the reusable vrp-toolkit architecture.

Migration Workflow

Step 1: Identify and Plan

1. Determine the module to migrate

   • Check migration_map.md for file mappings
   • Identify source file in /SDR_stochastic/new version/
   • Note the destination path and refactoring requirements

2. Read and analyze the source code

   • Read the entire source file
   • Identify dependencies and imports
   • Note any paper-specific hardcoded values
   • Understand the module's purpose and interface

Step 2: Refactor for Generalization

1. Extract hardcoded values

   • Identify magic numbers, file paths, dataset names
   • Convert to function parameters or config objects
   • Common patterns: battery capacity, time windows, location names

   Example transformation:

   # Before
   def solve():
       capacity = 100  # Hardcoded
   
   # After
   def solve(capacity: float = 100):
   

2. Decouple architecture layers

   • Separate problem definitions from algorithms
   • Extract algorithm-specific code to appropriate layer
   • Follow interfaces in architecture.md

3. Generalize data structures

   • Replace paper-specific types with generic abstractions
   • Ensure compatibility with Instance, Solution, Solver interfaces

Step 3: Implement in New Location

1. Create or update the destination file

   • Place code in the correct layer (Problem/Algorithm/Data)
   • Follow vrp-toolkit directory structure
   • Use appropriate naming conventions (see architecture guide)

2. Add documentation

   • Add docstrings to public functions/classes
   • Use Google or NumPy docstring style
   • Include parameters, return values, and examples

   def solve_pdptw(instance: PDPTWInstance, config: ALNSConfig) -> Solution:
       """Solve PDPTW using ALNS algorithm.
   
       Args:
           instance: Problem instance to solve
           config: Algorithm configuration parameters
   
       Returns:
           Solution object containing routes and objective value
       """
   

3. Update imports

   • Change import paths to new package structure
   • Use relative imports within vrp_toolkit
   • Update any external dependencies

Step 4: Create Test Case

Create a simple test to verify functionality:

def test_basic_functionality():
    """Basic smoke test for migrated module"""
    # Create minimal instance
    instance = create_test_instance()

    # Run migrated function
    result = migrated_function(instance)

    # Verify basic properties
    assert result is not None
    assert result.is_feasible()

Step 5: Verify Migration

1. Check imports resolve correctly

   • Try importing the new module
   • Verify no circular dependencies

2. Run the test case

   • Ensure basic functionality works
   • Compare behavior with original code if needed

3. Verify architectural compliance

   • Check layer separation (no Problem code in Algorithm layer, etc.)
   • Ensure following the Solver/Instance/Solution interfaces

Special Cases

Migrating Jupyter Notebooks

When migrating .ipynb files to tutorials:

1. Clean up for educational clarity

   • Add markdown explanations between code cells
   • Remove debugging/experimental code
   • Structure: Problem → Setup → Solve → Visualize → Interpret

2. Ensure reproducibility

   • Keep examples runnable in <30 seconds
   • Use small test instances
   • Include all necessary imports

3. Follow tutorial naming convention

   • Format: 0X_descriptive_name.ipynb
   • Update README with tutorial description

Merging Multiple Files

When multiple source files map to one destination (e.g., order_info.py + demands.py → generators.py):

1. Identify common functionality
2. Create unified interface
3. Preserve all unique features from each source
4. Organize as separate classes or functions within the same module

Key References

• File mappings: See migration_map.md
• Architecture patterns: See architecture.md
• Project guidelines: See CLAUDE.md in project root

Design Principles

• ✅ Minimal viable clarity over perfection
• ✅ Generalize from specific to reusable
• ✅ Decouple layers cleanly
• ❌ No over-engineering before 2+ use cases
• ❌ No documentation before code works