| 摘要: |
| 针对我国县、乡、村物流配送成本居高不下，需求覆盖率严重不足以及碳排放量较高的现实问题，构建考虑需求不确定和碳排放约束的农村物流配送路径优化模型，并提出了适用于多车型的改进多目标遗传算法。结合农村物流配送数据，运用MatlabR2014a软件进行仿真试验，最终得出在需求不确定和碳排放约束下农村物流的最优配送路径方案。仿真试验结果表明:设计的改进多目标遗传算法对于求解农村物流配送路径优化问题具有较好的有效性和适用性；在需求不确定和碳排放约束下，改进多目标遗传算法能够有效降低农村物流配送成本，提高需求覆盖率；与单车型配送方案相比，多车型配送方案在农村物流配送中更具优越性。 |
| 关键词: 改进多目标遗传算法 农村物流 路径优化 需求不确定 碳排放约束 |
| DOI：10.11841/j.issn.1007-4333.2023.07.20 |
| 投稿时间：2022-08-17 |
| 基金项目:国家社会科学基金项目(19BGL177)；湖南省教育厅科学研究重点项目(21A0348)；2022年湖南省研究生科研创新项目(CX20220846) |
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| Route optimization of rural low-carbon logistics based on improved multi-objective genetic algorithm |
|
BIN Hou,ZHANG Luhang,WANG Sujie,WANG Huanfang
|
| (School of Business, Hunan University of Technology, Zhuzhou 412008, China) |
| Abstract: |
| Aiming at the practical problems of high cost, insufficient demand coverage and high carbon emissions existed in the logistics distribution of counties, townships and villages in China, a rural logistics distribution routing optimization model considering demand uncertainty and carbon emissions constraints was constructed. an improved multi-objective genetic algorithm suitable for multiple vehicles was proposed. Combined with the rural logistics distribution data, MatlabR2014a software was used to carry out simulation experiments, and the optimal distribution route scheme of rural logistics under the demand uncertainty and carbon emission constraints was finally obtained. The simulation results show that: The proposed improved multi-objective genetic algorithm has better effectiveness and applicability for solving the rural logistics distribution routing problem; Under the demand uncertainty and carbon emission constraints, the improved multi-objective genetic algorithm can effectively reduce the cost of rural logistics distribution and improve the demand coverage rate; Compared with the single-vehicle distribution scheme, the multi-vehicle distribution scheme has more advantages in rural logistics distribution. |
| Key words: improved multi-objective genetic algorithm rural logistics route optimization demand uncertainty carbon emission constraints |
