Introduction

The Three-Dimensional Bin Packing Problem (3D-BPP) is crucial in modern industrial and logistics systems. With the total social logistics costs reaching 18.2 trillion RMB, accounting for one-seventh of the Gross Domestic Product (GDP), the logistics industry has become a key sector in the national economy. Packaging is a core aspect of logistics, and effective 3D bin packing strategies can maximize transportation space utilization, reduce shipping costs, and ensure the stability and safety of goods. Under the framework of Industry 4.0, the use of robotic arms for 3D bin packing is becoming increasingly prevalent. Effective coordination of robotic arms in packing processes can enhance automation and intelligence in the packaging industry, reduce packaging material waste, and promote environmental sustainability.

This work makes the following major contributions:

1. Provides a rich set of real industrial data targeted for the industry.
2. Develops a robust simulation environment for testing bin packing algorithms and introduces industrial-relevant evaluation metrics focused on robotic arm operations.
3. Conducts extensive tests on existing classical methods: A comprehensive evaluation of existing classical 3D bin packing algorithms and a summary of the test results.

We set up three settings:

• Setting 1: No consideration of physical simulation, no consideration of robotic arms.
• Setting 2: Consideration of physical simulation, no consideration of robotic arms.
• Setting 3: Consideration of physical simulation and robotic arms.

The placement results of these eight heuristic methods and the learning-based PCT method and TAP method under the three settings are shown in the Benchmarks section.