GENPACK: KPI-Guided Multi-Objective Genetic Algorithm for Industrial 3D Bin Packing

2026-01-16

AI Summary

This work addresses the challenge of jointly optimizing multiple constraints—such as space utilization, stability, balance, and operational feasibility—in industrial three-dimensional bin packing problems. To this end, we propose a multi-objective genetic algorithm framework that innovatively integrates key performance indicators (KPIs) directly into the fitness function. The approach employs a hierarchical chromosome encoding scheme, domain-specific genetic operators, and constructive heuristic strategies to efficiently generate high-quality, feasible packing solutions. Experimental results on the real-world order dataset BED-BPP demonstrate that our method improves space utilization by up to 35%, enhances load-bearing strength by 15%–20%, and significantly reduces solution variance, exhibiting strong robustness and promising potential for large-scale deployment.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The three-dimensional bin packing problem (3D-BPP) is a longstanding challenge in operations research and logistics. Classical heuristics and constructive methods can generate packings quickly, but often fail to address industrial constraints such as stability, balance, and handling feasibility. Metaheuristics such as genetic algorithms (GAs) provide flexibility and the ability to optimize across multiple objectives; however, pure GA approaches frequently struggle with efficiency, parameter sensitivity, and scalability to industrial order sizes. This gap is especially evident when scaling to real-world pallet dimensions, where even state-of-the-art algorithms often fail to achieve robust, deployable solutions. We propose a KPI-driven GA-based pipeline for industrial 3D-BPP that integrates key performance indicators directly into a multi-objective fitness function. The methodology combines a layer-based chromosome representation with domain-specific operators and constructive heuristics to balance efficiency and feasibility. On the BED-BPP benchmark of 1,500 real-world orders, our Hybrid-GA pipeline consistently outperforms heuristic- and learning-based state-of-the-art methods, achieving up to 35% higher space utilization and 15 to 20% stronger surface support, with lower variance across orders. These improvements come at a modest runtime cost but remain feasible for batch-scale deployment, yielding stable, balanced, and space-efficient packings.

Problem

• 3D bin packing
• industrial constraints
• stability
• space utilization
• multi-objective optimization

Innovation

• KPI-guided
• multi-objective genetic algorithm
• 3D bin packing
• layer-based representation
• industrial constraints