Bin Packing (Pallet & Container Stacking)

A toolset and reference implementation for solving bin-packing problems focused on stacking pallets and containers to minimize damage and ensure load stability. The project models product characteristics, placement constraints and handling rules to generate safe, efficient packing plans.

Goals

• Minimize damage and instability risks during transport and storage.
• Respect product-specific constraints (weight distribution, fragility, flammability).
• Produce practical stacking plans for pallets and non-rectangular items.
• Provide configurable algorithms and heuristics for different operational requirements.

Key concepts

• Container/Bin: the storage unit (pallet, crate, truck bay) with fixed dimensions and weight limits.
• Item/Product: an object to be placed with properties such as dimensions, weight, orientation constraints and handling tags.
• Stability: rules to ensure center of gravity and load-support relationships keep the stack stable.
• Objective: minimize damage risk and unused space while satisfying constraints.

Product types & handling rules

• Heavy — Prefer lower positions; avoid stacking heavy items on top of light or crushable items.
• Large — May require dedicated regions or special orientation; impacts packing compactness.
• Crushable / Fragile — Prefer top positions and avoid supporting heavy loads above them.
• Flammable — Must be grouped and routed from a designated handling area; may have placement/segregation rules.
• Awkward shape — Non-rectangular or irregular items require custom packing heuristics (approximate bounding boxes, support modeling).

Constraints to model

• Dimensional fit (width, depth, height).
• Weight limits per container and per support surface.
• Load-bearing relationships between items (support, overhang limits).
• Handling/segregation rules (flammable, hazardous, temperature-controlled).
• Orientation and rotation allowances.

Usage (high level)

• Define container geometry and constraints.
• Provide a list of items with properties (dimensions, weight, type tags, allowable orientations).
• Run the packing algorithm (configurable heuristics or optimizers).
• Inspect generated packing plan and validate stability/constraints before deployment.

Example configuration (conceptual)

• container: { width: 1200, depth: 800, height: 2200, maxWeight: 1000 }
• item: { id: "A1", w: 400, d: 300, h: 200, weight: 50, tags: ["heavy"] }

Algorithms & extensibility

• Supports heuristics (first-fit, best-fit, stacking-aware heuristics) and candidate space for optimization solvers.
• Designed to be extended with custom stability checks, cost functions (damage risk, handling cost), and domain-specific constraints.

Development

• Structure repository into modules: model, constraints, solver(s), visualization.
• Include unit tests for constraint enforcement and stability checks.
• Provide example datasets and reproducible scenarios for benchmarking.