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

• URL: http://arxiv.org/abs/2601.11325v1
• Date: Fri, 16 Jan 2026 14:19:03 GMT
• Title: GENPACK: KPI-Guided Multi-Objective Genetic Algorithm for Industrial 3D Bin Packing
• Authors: Dheeraj Poolavaram, Carsten Markgraf, Sebastian Dorn,
• Abstract summary: The three-dimensional bin packing problem (3D-BPP) is a longstanding challenge in operations research and logistics.<n>We propose a pipeline for industrial 3D-BPP that integrates key performance indicators directly into a multi-objective fitness function.<n>On the BED-BPP benchmark of 1,500 real-world orders, our Hybrid-GA pipeline consistently outperforms state-of-the-art methods.
• Score: 3.0204520109309843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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