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低碳视角下改进蚁群算法的配送路径优化研究
首发时间：2024-05-20
摘要：在物流配送领域，车辆的固定及运输成本是基本开支。本文加入了顾客满意度相关的时间窗偏差惩罚成本和基于碳税政策的碳排放成本，两者分别量化顾客期望与实际配送时间的差距及将碳排放量转化为经济损失。目标是通过构建一个综合碳排放和时间窗约束的多类型车辆配送路径优化模型，最小化整个配送流程的总成本，考虑不同类型的车辆载重和顾客的软时间窗限制。在算法设计上，本研究通过改进启发函数和状态转移概率，优化全局信息素更新策略，并引入混沌扰乱机制来优化蚁群算法。最后利用matlab软件进行实例分析，将改进后的蚁群算法与传统蚁群算法的配送方案进行对比。结果显示，利用改进蚁群算法进行优化，配送路线更合理，总成本更低。同时，分析了配送成本和碳排放量如何随碳税价格变化。
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research on improving ant colony algorithm for distribution path optimization from a low carbon perspective
abstract：in the field of logistics distribution, fixed vehicle and transportation costs constitute fundamental expenses. this paper introduces penalty costs associated with time window deviations related to customer satisfaction, along with carbon emission costs based on carbon tax policies. these two factors quantify the difference between customer expectations and actual delivery times while converting carbon emissions into economic losses. the objective is to minimize the total cost of the entire delivery process by constructing a multi-vehicle route optimization model that integrates carbon emissions and time window constraints, considering varying vehicle load capacities and customers\' flexible time window limitations. in terms of algorithm design, this study improves heuristic functions and state transition probabilities, optimizes the global pheromone update strategy, and introduces a chaotic disturbance mechanism to enhance the ant colony algorithm. finally, matlab software is used for empirical analysis to compare the optimized ant colony algorithm with the traditional ant colony algorithm. the results demonstrate that the improved ant colony algorithm provides more rational delivery routes and lower total costs. moreover, the paper analyzes how delivery costs and carbon emissions change with fluctuations in carbon tax prices.
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低碳视角下改进蚁群算法的配送路径优化研究
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