Hybrid approach for solving real-world bin packing problem instances using quantum annealers

Area of Science:

• Artificial Intelligence
• Operations Research
• Quantum Computing

Background:

• The Bin Packing Problem is a fundamental optimization challenge with significant industrial and logistics applications.
• The three-dimensional Bin Packing Problem (3D BPP) is particularly relevant to real-world scenarios.
• Existing solutions often struggle to incorporate complex, realistic constraints.

Purpose of the Study:

• To introduce a novel hybrid quantum-classical framework, Q4RealBPP, for solving real-world 3D BPP instances.
• To address practical constraints beyond simple volume packing.

Main Methods:

• Development of a hybrid quantum-classical algorithm (Q4RealBPP).
• Integration of realistic constraints: package/bin dimensions, overweight restrictions, item affinities, and ordering preferences.
• Application to real-world oriented 3D BPP instances.

Main Results:

• Demonstration of Q4RealBPP's capability to handle complex, realistic 3D BPP scenarios.
• Successful incorporation of multiple practical constraints into the packing optimization.

Conclusions:

• Q4RealBPP offers a promising approach for optimizing logistics and industrial packing operations.
• The framework's ability to handle real-world constraints enhances its practical applicability.