基于改进沙猫群优化算法的绿色多式联运路径优化

Research on Multimodal Transportation of Green Vehicle Logistics Based on Mixed Sand Cat Swarm Optimization Algorithm

为了解决整车多式联运物流过程中涉及的因素种类多,各方面成本难以平衡,且对节能减排要求较大的问题,综合考量运输成本、中转成本、风险成本、燃料消耗成本、碳排放成本和服务时效成本六个部分,并且加入对货运新能源车能耗排放的考虑,构建了一种绿色整车物流多式联运模型,该模型能够更好地反映出实际整车多式联运物流过程中的成本构成。为了更好地求出合理的配送方案,提出了一种混合沙猫群优化算法,在沙猫群优化算法的基础上,通过随机分布和Kmeans聚类算法优化初始沙猫的位置,并引入粒子协同机制和随机游走策略。通过与其它在基准函数中测试的算法进行比较,证明了所提算法在收敛精度和速度上都具有更好的性能。最后,将所提算法用于实际多式联运整车物流运输问题,实验结果表明,混合沙猫群优化算法在解决多式联运路径规划问题方面具有很大的优势。

To solve the problem that there are various factors that affect multi-modal transport logistics and the difficulty in achieving balance between various costs,there are six elements carefully considered:transportation cost,transfer cost,risk cost,fuel consumption cost,carbon emission cost and service timeliness cost.In addition,the energy consumption and emissions of new energy lorries are also considered.Then,a green vehicle logistics multimodal transport model is constructed to better reflect the structure of costs incurred by vehicle multimodal transport logistics.In order to better develop a reasonable distribution plan,a mixed sand cat swarm optimization(MSCSO)algorithm is proposed.Through the sand cat swarm optimization algorithm,random distribution and K-means clustering algorithm,the initial sand cat position is optimized.Furthermore,particle collaboration mechanism and random walk strategy are introduced.Through a comparison drawn with other algorithms tested against the benchmark function,it is demonstrated that the proposed algorithm performs better in the accuracy and pace of convergence.Finally,the proposed algorithm is applied to solve the practical problems with multimodal vehicle logistics transportation.The experimental results show that the mixed sand cat swarm optimization algorithm is advantageous in multimodal transport path planning.