An Improved Genetic Tabu Search Algorithm for Rectangular Layout Optimization

To address the issues of excessive cutting operations and low material utilization rates in rectangular cutting for special-purpose vehicle manufacturers, improved genetic tabu search algorithm is proposed. First, a two-stage mathematical model for plate cutting was established to limit the number of product shearing operations. Next, an improved genetic-tabu hybrid search algorithm is proposed to generate optimized layout schemes. A genetic algorithm incorporating predation strategy was employed to construct initial solutions, with the iteration process further enhanced through the integration of inferior solution acceptance mechanisms and adaptive forgetting strategies. Finally, comparative experiments with two existing benchmark algorithms were conducted using authentic production datasets from Company E. The experimental results demonstrated that the proposed algorithm achieves a material utilization rate exceeding 90% while satisfying cutting constraints, significantly reducing large-area material waste. It outperformed the two benchmark algorithms and better aligns with the company's operational requirements.