Optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system.

Area of Science:

• Operations Research
• Logistics and Supply Chain Management
• Computational Optimization

Background:

• Maximizing packing bin utilization is crucial for e-commerce logistics.
• Existing 3D bin packing models face challenges with open-size problems, complexity, and computation time.
• Optimizing multiple bin sizes is a significant practical challenge.

Purpose of the Study:

• To address the multiple-bin size 3D bin packing problem in e-commerce.
• To propose a novel hybrid integer programming model (3D-MODRPP) and an associated system.
• To reduce computational complexity and enhance space utilization for heterogeneous and open-size bin packing.

Main Methods:

• Developed a hybrid integer programming model: three dimensional multiple option dimensional rectangular packing problem (3D-MODRPP).
• Proposed a 3D bin packing system supporting multiple bin types and open dimensions.
• Introduced a novel assumption method for 0-1 integer variables to simplify overlap and rotation constraints.
• Refined a dataset based on the 3D-ODRPP dataset for improved outcomes.

Main Results:

• Significantly reduced model complexity and computation time.
• Demonstrated improved performance in various bin packing scenarios (heterogeneous, actual, multiple open sizes).
• Increased overall space utilization in packing operations.
• Validated the model's effectiveness through comprehensive analysis.

Conclusions:

• The proposed 3D-MODRPP model and system offer a practical solution for complex 3D bin packing problems.
• The approach effectively addresses challenges in multiple bin sizes and open dimensions, reducing waste.
• This research provides a foundation for future investigations and practical applications in logistics and warehousing.