基于遗传算法模拟退火算法解决修边损耗问题

Genetic Simulated Annealing Algorithm Solve Resumption Problem in Carton Manufactory

纸箱包装行业是一个传统的产业,在纸箱生产中需要拼单来降低修边损耗以减少成本。本文根据生产上的实际经验提出了问题的数学模型,针对该模型,本文将遗传算法和模拟退火算法结合,解决了遗传算法的收敛过快以及局部搜索能力不强的问题。在选择操作中直接保存优秀个体,来增强算法的收敛性。在变异和交叉操作中采用自适应的变异和交叉概率,增强了搜索解空间的均匀性,并引入了记忆功能,最终获得问题的近似最优解。

Carton packing industry is a traditional manufacture industry. The factories need to combine orders to save the materials and cut down resumptions at the same time. This article raises a optimizing model with its experience, and uses Simulated Annealing Algorithm and Genetic Algorithm to solve the slow convergence and weak partial searching ability of Genetic Algorithm. The article saves good results directly to strengthen the algorithm convergence at the selection operation. And uses self-adapted cross and mutation probability to make the searching scope more equally, and the article also introduces memory function to get a near best solution.