基于岛屿并行偏置随机密钥遗传算法的三维拖车装载问题
An Island-Based Parallel Biased Random-Key Genetic Algorithm for the Three-Dimensional Trailer Loading Problem
- Intelligent Systems Department Gradiant Vigo, Spain（格拉迪安特智能系统部）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对NP难的三维拖车装载问题，提出基于岛屿并行框架PANGEA加速的偏置随机密钥遗传算法，提升种群多样性、缓解早熟收敛并显著降低计算时间，经真实装载过程验证有效。
AI中文摘要：
三维拖车装载问题（3D-TLP）涉及在拖车的受限空间内确定异构物品的最佳放置位置和朝向，同时最大化体积利用率并满足一系列复杂的物流和安全约束。3D-TLP是NP难的，这使得精确优化方法在大规模工业应用中计算上不可行。为了解决这一挑战，我们提出了一种增强的偏置随机密钥遗传算法（BRKGA），并通过一种新颖的基于岛屿的并行化框架PANGEA进行加速。所提出的方法将BRKGA的搜索效率和鲁棒性与遗传算法的多群体进化方案相结合。这种岛屿模型策略促进了种群多样性，缓解了过早收敛，并显著减少了计算时间。所提出的解决方案在真实的拖车装载过程中得到了验证，为现实世界的大规模物流提供了一种有效的求解方法。
英文摘要：
The Three-Dimensional Trailer Loading Problem (3D-TLP) involves determining the optimal placement and orientation of heterogeneous items within the confined space of a trailer while maximizing volume utilization and satisfying a wide range of complex logistical and safety constraints. The 3D-TLP is NP-hard, rendering exact optimization approaches computationally impractical for large-scale industrial applications. To address this challenge, we propose an enhanced Biased Random-Key Genetic Algorithm (BRKGA) accelerated through a novel island-based parallelization framework, PANGEA. The proposed method combines the search efficiency and robustness of BRKGA with a multi-population evolutionary scheme for genetic algorithms. This island-model strategy promotes population diversity, mitigates premature convergence, and significantly reduces computational times. The proposed solution was validated in a real trailer loading process, providing an effective solution approach for real-world large-scale logistics.
