Solution to Solid Wood Board Cutting Stock Problem

In the production process for wooden furniture, raw material costs account more than 50% of furniture costs, and utilization rate materials depends mainly on layout scheme. Therefore, a reasonable is an important measure to reduce costs. This paper investigates solid wood board cutting stock problem (CSP) establishes optimization model, with goal highest possible original boards. An ant colony-immune genetic algorithm (AC­IGA) designed solve this model. The solutions colony are used as initial population immune algorithm, optimal solution obtained using after multiple iterations transformed into accumulation global pheromones, which improves search ability ensures quality. abstracted construction solution. At same time, in order prevent premature convergence, several improved methods, such pheromone hybrid update adaptive crossover probability, proposed. Comparative experiments verify feasibility effectiveness AC­IGA, experimental results show that AC­IGA has better precision compared (ACA), (GA), grey wolf optimizer (GWO), polar bear (PBO). increased by 2.308%, provides effective theoretical methodological support enterprises improve economic benefits.