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中文摘要：
为优化动态交通下物流配送成本及服务水平，依据交通流量将运输时间分为不同时段的不同分布，建立了具有时间窗约束与物流成本最小的车辆路径混合整数非线性模型，设计了自然数插值编码的遗传算法对模型进行求解，对不同交通状况下配送方案选择进行了仿真比较。仿真结果显示遗传算法是收敛的，依据交通状况选择相应的配送方案，不仅物流成本降低了2%，而且服务水平也提高了5%。
英文摘要：
In order to optimize logistics delivery cost and consumer service level under dynamic traffic, transportation time was assorted into different distributions according to traffic, a mixed integer non-linear model of vehicle routing choice with time window constraints was set up to minimize logistics cost, a genetic algorithm with natural number coding was designed to solve the model, the simulation results of different delivery projects were compared. Comparison result shows that the algorithm is convergent, the logistics cost is reduced by 2%, the service level is improved by 5% to vehicle routing choice according to traffic condition. 3 tabs, 5 figs, 16 refs.
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The evolvement and relevant factors of price discovery: A case study of cross-listed stocks in China
A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling proble
The evolvement and relevant factors of price discovery: A case study of cross-listed stocks in China
A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling proble
A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop schedulin
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