分布式制造供应链低碳集货模型与算法研究

Study on Low-carbon Cargo Consolidation Model and Algorithm of Distributive Manufacturing Supply Chain

针对我国当前严峻的环境形势和节能减排的现实要求,研究了低碳环境下分布式制造供应链节点集货问题,研究过程中应用了最小碳排放和最短路径的多目标优化模型。算法上设计了三阶段集成启发式方法分步求解,首先采用遗传算法找到最短路径的集货方案;在此基础上采用节约算法的基本思想进行路径拆分和车辆匹配,降低碳排放水平;最后通过计算碳排放节约值对集货方案进行局部重组,进一步优化碳排放指标。仿真实验表明,分阶段的求解方法可以设计出碳排放更优的解,同时满足经济效益和环境效益。

In this paper, in view of the several environment condition in China and the realistic demand of the initiative to cut energy consumption and emissions, we studied the issue of cargo consolidation of a distribution manufacturing supply chain within the low-carbon environment, during which the optimization model that intended to both minimize carbon emissions and distribution distance was used; the on such basis, we approached the route division and vehicle matchmaking following the fundamental principle of the saving algorithm so as to lower the carbon emissions level; and at the end, we regrouped partially the cargo consolidation solution in light of the calculation resuh to further optimize the carbon emissions index, the validity of which was demonstrated through a simulation study.