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3d-bin-packing

Solve 3D bin packing and container loading problems with optimization algorithms.

56|16|Updated Oct 18, 2025
Search Tags:#optimization#logistics#container loading#3d packing#bin packing#space utilization
One-click install
npx skills add https://github.com/kishorkukreja/awesome-supply-chain --skill 3d-bin-packing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: 3d-bin-packing
Source: https://github.com/kishorkukreja/awesome-supply-chain/tree/main/skills/3d-bin-packing
Command: npx skills add https://github.com/kishorkukreja/awesome-supply-chain --skill 3d-bin-packing

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pulp, numpy, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the complex challenge of efficiently packing three-dimensional items into containers, aiming to maximize space utilization and minimize the number of containers used.

Core Features & Use Cases

  • 3D Bin Packing: Solves the NP-hard problem of packing boxes into the minimum number of identical bins.
  • Container Loading: Optimizes the utilization of a single container's volume.
  • Algorithm Support: Implements various algorithms including Integer Programming, Bottom-Left-Back (BLB), Layer Building, Extreme Point, and Genetic Algorithms.
  • Use Case: A logistics company needs to pack irregularly shaped items into shipping containers to minimize shipping costs. This Skill can determine the optimal arrangement and orientation of items to achieve maximum density.

Quick Start

Use the 3d-bin-packing skill to pack a list of boxes with given dimensions into a container.

Frequently Asked Questions about 3d-bin-packing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize 3D bin packing for multiple containers?▼

3D bin packing is solved using advanced algorithms like Integer Programming and Genetic Algorithms to optimally place boxes into the minimum number of identical bins, maximizing space utilization while considering volume and physical constraints.

What algorithms are available for container loading optimization?▼

Container loading optimization supports Bottom-Left-Back (BLB), Layer Building, Extreme Point, Integer Programming, and Genetic Algorithms to determine the optimal arrangement and orientation of items for maximum density.

Can I use numpy and pulp for 3D packing space utilization?▼

Yes, 3D packing space utilization requires numpy and pulp as dependencies to process box dimensions and formulate the Integer Programming models needed for calculating optimal item placement.

What is the best way to pack irregularly shaped items into shipping containers?▼

Packing irregularly shaped items into shipping containers is best handled by solving the Single Container Loading problem using Extreme Point or Layer Building algorithms to maximize density and minimize shipping costs.

Does 3D bin packing handle weight constraints and physical limitations?▼

Yes, 3D bin packing handles weight constraints and physical limitations by evaluating item placement and orientation against volume and weight parameters to ensure feasible container loading arrangements.

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