目的针对冷链运输中的生鲜打包及装载优化问题,提出一种允许货物以体积恒定为前提进行尺寸变化的包装装载方案,以最大化集装箱的空间利用率。方法基于上述问题,构建非线性混合整数规划模型,为了方便CPLEX或LINGO等求解器对该非线性混合整数规划模型进行求解,采用一种分段线性化方法,将该非线性模型进行线性化处理。由于所研究问题具有NP-hard属性,无论是CPLEX还是LINGO都无法有效求解大规模算例,因此设计一种有效结合遗传算法与深度、底部、左部方向优先装载(Deepest bottom left with fill,DBLF)的算法。结果大小规模算例实验验证结果表明,混合遗传算法能够在合理时间内获得最优解或近似最优解。结论所提出的可变尺寸包装方案有效提高了装载率,有益于客户和物流公司。
在三维装箱问题中,启发式算法和遗传算法都能够较好地解决问题。本文在可放置点生成的启发式算法和遗传算法的基础上,将二者结合生成了新的混合算法来研究三维装箱问题,并通过真实应用场景数据对新的混合算法进行测试,混合算法的装载率和原传统算法相比稳中有增,尤其是针对货物规格种类较多的情况下,混合算法的优势更为明显。In the three-dimensional packing problem, both heuristic algorithms and genetic algorithms can effectively solve the problem. Based on the heuristic algorithm for generating placeable points and the genetic algorithm, this paper integrated the two algorithms into a new hybrid algorithm for the three-dimensional packing problem. The new hybrid algorithm was tested by real data in the application, and its loading rate was stable and had increased compared to the original traditional algorithm. Especially for cases with multiple types of goods, the advantages of the hybrid algorithm were more obvious.
