Research on Optimization of Cutting Sequence for Panel-Type Customized Furniture Based on Hybrid Strategy

This study breaks through the traditional nesting optimization and single algorithm improvement paradigm, and proposes a hybrid strategy of dynamic genetic algorithm and local search (DGA-LS) to address the efficiency bottleneck problems of delayed order completion time, increased cache demand, and increased scheduling complexity caused by the random online production of raw material panels in the panel customized furniture production line. This strategy innovatively integrates dynamic encoding mechanism, adaptive genetic operator, and hybrid local search technology by constructing a multi-objective collaborative optimization model of order component board. Dynamic encoding adjusts chromosome structure based on component spatial correlation, adaptive operator balances global exploration and local development, and hybrid search combines 2-opt path optimization, taboo search memory, and simulated annealing probability acceptance mechanism. Experiments have shown that in simulated data and actual production line testing of 20-40 orders and 350-700 board scales, this strategy significantly reduces the average processing distance by 42% and the maximum span by 55% compared to traditional genetic algorithms (GA) and random strategies. The computation time is shortened to 31% of GA, while reducing cache requirements by 38%, improving equipment utilization by 22%, and shortening the completion time by 40%. It maintains an approximate linear complexity growth under a scale of 1000 orders, demonstrating superior adaptability to large-scale production scenarios. This study provides a universal optimization framework for discrete manufacturing production line scheduling that combines theoretical depth and engineering value through closed-loop innovation of "problem modeling mixed solution scenario verification".