Research on Multi-Objective and Multi-Constraint Panel Packing and Stacking Algorithms

This study proposes an innovative multi-objective optimization algorithm to address the key issue of order panel subcontracting and stacking optimization in panel custom furniture production lines. In response to the common core problems of low subcontracting efficiency, insufficient space utilization, and large filling material usage in existing methods, efficient optimization under multiple constraint conditions has been achieved by integrating improved greedy strategies and dynamic constraint satisfaction mechanisms. The algorithm innovatively designed an intelligent pre screening process and adaptive weight adjustment strategy, which significantly improved overall performance while ensuring compliance with industrial constraints such as weight limit ( ≤ 40kg), support area requirement ( ≥ 75%), and suspended layer control (≤ 1 layer). Experimental verification shows that this method reduces the total number of packages by 7.1%, reduces the amount of fillers by 78.5%, and improves the space utilization rate to 89.3% compared to existing solutions in 7409 actual orders, while maintaining a feasibility rate of over 98%. These improvements directly translate into significant industrial benefits, achieving an average annual cost savings of 2.3 million yuan in practical applications, providing a solution that combines theoretical innovation and practical value for the multi constraint packing problem.