基于改进多目标遗传算法的农村低碳物流配送路径优化

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。

Route optimization of rural low-carbon logistics based on improved multi-objective genetic algorithm

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.