Parallel genetic algorithm improves loading of three dimensional trailers
An Island-Based Parallel Biased Random-Key Genetic Algorithm for the Three-Dimensional Trailer Loading Problem
Neural and Evolutionary ComputingDistributed, Parallel, and Cluster Computing
Summary
Fitting many different-sized items efficiently into a trailer is really complicated and takes a lot of computer time to solve perfectly. The authors worked on this packing problem and created a new way to speed up the process using a type of genetic algorithm that runs many searches in parallel, sharing information to avoid getting stuck with bad solutions. They tested their approach in a real trailer loading scenario and showed it can find good arrangements faster. This method helps logistics teams pack trailers better while respecting safety and space rules.
What this means in practice
- •For logistics planners: Optimize trailer packing plans quickly for different cargo shapes and constraints to improve loading efficiency in real operations.
- •For fleet managers: Reduce computational time to plan trailer loads at scale, enabling faster decision-making in large transportation operations.
Authors
A. del Río, L. Díaz, L. C. de Vicente, J. Cameselle, B. Fernández
Abstract
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.