基于低碳排放的应急物流车辆路径优化

Routing Optimization for Emergency Logistics Vehicles Aimed at Low Carbon Emissions

针对应急物流的车辆路径优化问题,基于低碳绿色物流思想,以车辆固定成本、运输过程中产生的成本、未满足需求量的惩罚成本以及碳排放成本之和最低为优化目标,建立了基于低碳排放的应急物流车辆路径优化模型。由于受灾点的需求具有不确定性,模型运用三角模糊数来估计需求量。为了求解模型,采用混合遗传算法,利用节约算法获得初始解,并利用大规模邻域搜索算法“破坏”与“修复”的思想改进遗传算法的局部搜索操作。最后通过算例仿真,验证了模型的实用性与算法的有效性。

In order to optimize the routing of emergency logistics vehicles,based on the concept of low-carbon green logistics,we established a routing optimization model of emergency logistics vehicles to realize low-carbon emissions,with the objective of minimizing the sum of vehicle fixed cost,expenses in the transportation process,penalty for failing to meet demand,and cost of carbon emissions.Next,considering the uncertainty of demand across the disaster-stricken areas,we estimated the demand of the areas using the triangular fuzzy number method,and solved the model with hybrid genetic algorithm,obtaining the initial solution via the saving algorithm.Then,using the idea of"destroy"and"restore"in the large-scale neighborhood search algorithm,we improved the local search function of the genetic algorithm.Finally,we verified the practicability of the model and the effectiveness of the algorithm through a simulation example.