交通运输系统工程与信息

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (6): 265-275.DOI: 10.16097/j.cnki.1009-6744.2025.06.024

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考虑碳排放成本的轻型载货汽车-公交车协同配送问题优化

张志坚* ，张婷，邸振，郭军华   

  1. 华东交通大学，交通运输工程学院，南昌330013
  • 收稿日期:2025-07-24 修回日期:2025-08-24 接受日期:2025-09-01 出版日期:2025-12-25 发布日期:2025-12-24
  • 作者简介:张志坚(1978—)，男，江西丰城人，教授。
  • 基金资助:
    国家自然科学基金(72161010, 52262048)

Optimization of Collaborative Distribution of Light Trucks and Buses Considering Carbon Emission Costs

ZHANG Zhijian*, ZHANG Ting, DI Zhen, GUO Junhua   

  1. School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China
  • Received:2025-07-24 Revised:2025-08-24 Accepted:2025-09-01 Online:2025-12-25 Published:2025-12-24
  • Supported by:
    National Natural Science Foundation of China (72161010, 52262048)

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摘要： 为应对城市物流配送网络扩张所引发的交通压力与碳排放问题，本文提出一种考虑碳排放成本的轻型载货汽车-公交车协同配送优化模型。该模型融合公交固定线路、客户需求时间窗、碳排放因素与运输成本，以最小化总成本为优化目标，系统刻画公交车辆与轻型载货汽车协同配送的复杂约束与多因素决策问题。针对问题高维、复杂和易陷入局部最优等特点，设计改进遗传算法，通过节约里程法生成高质量初始种群，并引入锦标赛选择、自适应交叉与变异策略，显著提升算法的收敛速度与全局搜索能力。实验结果表明：本文所提出的算法在算例规模为60时实现19.48%的成本优化；4种不同节点规模下，运行20次数据的最大成本、最小成本和平均值成本均得到有效降低；相比单一配送模式，公交协同模式可在一定范围内有效降低运输成本、碳排放成本和时间窗惩罚成本，提升配送效率与服务质量。

关键词: 物流工程, 路径优化, 改进遗传算法, 轻型载货汽车-公共交通协同配送, 碳排放成本

Abstract: In order to cope with the traffic pressure and carbon emission caused by the expansion of the network in urban logistics and distribution, this paper proposes an optimum model of light trucks-public transportation collaborative distribution considering the cost of carbon emissions. The model integrates the fixed bus routes, customer demand time windows, carbon emission factors and transportation costs. It systematically depicts the complex constraints and multi-factor decision-making problems in the collaborative distribution of light truck-public transportation with the optimization goal of minimizing the total cost. In view of the characteristics of the problem, such as high dimensionality, complexity, and easy to fall into local optimum, this paper designs an improved genetic algorithm. It generates high-quality initial populations through the mileage-saving method, and introduces championship selection, adaptive crossover and mutation strategies, which significantly improves the convergence speed and global search ability of the algorithm. Experimental results show that the proposed algorithm achieves a cost optimization of 19.48% on the 60-node scale problem. Under four different node sizes, the maximum, minimum and average costs of running data for 20 times are effectively reduced. Compared with the mode of single distribution, the mode of public transport coordination can effectively reduce the costs of transportation, carbon emission and time window penalty within a certain range, and improve distribution efficiency and service quality.

Key words: logistics engineering, route optimization, improved genetic algorithm, light truck-public transport collaborative distribution, carbon emission cost

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