基于人工鱼群和模拟退火算法的电商快递纸箱规格优化

目的 对快递纸箱规格优化进行研究,在满足商品对纸箱需求和不增加纸箱规格种类的前提下,达到尽可能减少包装材料使用量的目标.方法 根据商品对快递纸箱的实际需求,以瓦楞纸使用总量最小化为目标函数,以商品和纸箱规格的匹配关系为约束条件建立优化模型.使用人工鱼群与模拟退火算法融合的求解算法对模型求解.结果 在仿真实验中,使用文中提出的建模求解方法得到的纸箱规格优化方案较中国邮政现行纸箱规格体系瓦楞纸使用量节省了23％,而采用k-means聚类分析得到的纸箱规格优化设计方案中瓦楞纸使用量仅节省了11％.结论 该方法可以满足商品批量小、种类多、尺寸各异、动态更新的电商环境下快递纸箱规格优化的需求,有助于改善快递行业包装过度使用的现状.

E-Commerce Express Carton Specification Optimization Based on Artificial Fish Swarm and Simulated Annealing Algorithm

This paper aims to achieve the goal of reducing the use of packaging materials as much as possible on the premise of meeting the demand of commodities for cartons and not increasing the types of carton specifications through research on the optimization of express carton specifications. According to the actual demand of the commodities for express cartons, the objective function is minimized total amount of corrugated paper used, and the matching relationship between the commodity and the carton specifications is used as the constraint condition, to establish an optimization model. The solution algorithm obtained by fusing artificial fish swarm algorithm and simulated annealing algorithm is used to solve the model. In the simulation experiment, the carton specification optimization scheme obtained by using the modeling solution method proposed in this paper saves 23% of corrugated paper compared with the current carton specification system of China Post Group Corporation, while the carton specification optimization scheme obtained by k-means cluster analysis only saves 11% of corrugated paper. The method proposed in this paper can meet the needs of express carton specifications optimization in an e-commerce environment featured by small batch size, many types, different sizes, and dynamic updates, helping reduce the excessive use of packaging in the express industry.