Authors

Abstract

Agriculture is the foundation of the national economy, and agricultural materials are the basis of agricultural development. As the three rural issues (agriculture, countryside, and farmers) become increasingly important, the distribution of agricultural materials attracts extensive attention. Given the slow development of rural logistics, the traditional agricultural material distribution process encounters many problems, such as cumbersome distribution links, high distribution costs, and low profit for enterprises, which in turn cause high production costs and low income for farmers. In consideration of battery energy consumption and soft time window constraints, this study adopted the agricultural material distribution route as the study object and established an optimization model of the agricultural material distribution route with fixed , transportation, energy consumption, time window penalty, and carbon emission costs as the objective functions. With regard to the algorithm, the operation of differential update and chaotic disturbance was innovatively enhanced and applied to the improved ant colony algorithm to simulate the model and obtain the optimal distribution route optimization model. Results show that the traditional ant colony algorithm improved by differential updating and chaotic disturbance has the advantages of low distribution cost, reasonable route, small number of activated vehicles, and short convergence time. Compared with the traditional ant colony algorithm, the improved ant colony algorithm can converge to the global optimum faster. This study provides guidance and suggestions on route selection and vehicle configuration to reduce costs and increase efficiency and offers certain theoretical support to alleviate urban traffic pollution and implement carbon trading policies in the future.

Abstract in Chinese

农业是国民经济的基础,而农业生产资料的支持是农业发展的基础，在"三农"问题愈发重要的今天，农业生产资料的配送问题也随之显得十分重要。由于农村物流发展缓慢,传统的农资配送过程中存在着配送环节过多、配送成本过高、企业获得利润过少的问题,同时也造成了农民生产成本过高、收入低的现象。基于此，本文以农资配送路径对象，以电池能耗和软时间窗约束为前提，以固定成本、运输成本、能耗成本、时间窗惩罚成本和碳排放成本为目标函数，建立了农资配送路径优化模型。在算法方面，创新性地改进了微分更新和混沌扰动的操作，将微分更新和混沌扰动应用于改进的蚁群算法对模型进行仿真求解，得到了最优分配路径优化模型。仿真结果表明：采用微分更新和混沌扰动对传统蚁群算法进行改求解模型，得到的分配成本更低，路线更好，激活车辆数量更少，算法收敛时间更短，并且与传统蚁群算法相比，改进蚁群算法可以更快地收敛到全局最优。研究结果也可以帮助企业对农资配送车辆路线的选择和车型配置决策提供指导和建议，帮助企业降本增效，并为未来缓解城市交通污染和碳交易政策的广泛实施提供一定的理论支持。