Packaging Model Optimization Based on Spatial Segmentation and Chaotic Firefly Algorithm

Abstract

In this paper, we propose a 3D box optimization model based on space partitioning algorithm and chaotic firefly algorithm, focusing on the synergistic application of the underlying layflat strategy,packing strategy and intelligent optimization algorithm in complex packaging problems. First, the problem is formalized as a 3D-MBSBPP problem by establishing an average loading rate model, defining the key mathematical conditions such as volume constraints, quantity constraints, and box type selection constraints. Secondly, design the space partitioning algorithm based on the underlying tiling strategy and packing strategy to realize high-density crating through pre-packing processing, dynamic subspace partitioning with a selection mechanism, and introduce a space merging strategy to avoid the fragmentation problem. Finally, the chaotic firefly optimization model is constructed, the Tent chaotic mapping is used to initialize the population to enhance the global search capability, and the box specifications are optimized iteratively through the veneer rate threshold constraint and the fitness function. The model realizes dynamic volume allocation through adaptive space partitioning strategy, and its mixed integer programming framework effectively balances computational efficiency and solution accuracy, which improves the average loading rate by 3.62% in the standard test cases, showing strong engineering applicability.