To address the issues of packaging material waste and low container space utilization caused by unreasonable carton specification design in automotive parts logistics, the work aims to construct an optimization model with the goal of minimizing the total packaging costs, explicitly considering the characteristics of heterogeneous mixed packing of multi-category components in real-world scenarios, to explore efficient methods for solving logistics carton size design schemes. Firstly, a packaging cost optimization model was established based on enterprise orders and product dimension data. The Sobol sequence was then employed to generate a uniform initial population to compensate for the deficiencies of random initialization. Subsequently, a Q-Learning control mechanism was integrated into the Differential Evolution algorithm to achieve dynamic adaptive adjustment of key parameters, thereby balancing global search and local optimization capabilities. Furthermore, a constructive greedy packing strategy was utilized to solve for the mixed packing scheme and actual carton quantity in line with geometric and weight constraints. Simulation experiments demonstrated that the proposed algorithm significantly outperformed the traditional Genetic Algorithm, Simulated Annealing, and conventional Differential Evolution algorithm in terms of convergence speed and optimization accuracy. Compared with the original scheme, the total packaging cost for the same batch of orders was reduced by approximately 53% after the optimization of logistics carton specifications. This method is applicable to complex logistics packaging scenarios characterized by high-frequency fluctuating orders, large spans of product dimensions, and multi-specification products, effectively achieving cost reduction and efficiency enhancement through optimized carton type design.
Key words
logistics carton /
specification optimization /
differential evolution algorithm /
Q-Learning /
Sobol sequence
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