基于软时间窗约束的生鲜品配送车辆路径优化

Path optimization of fresh product delivery vehicles based on soft time window constraint

随着我国经济结构的转型和居民消费模式的转变,冷链物流得到迅猛发展.为了实现低碳经济环境下生鲜产品配送的低成本以及冷链物流健康发展,提出了基于冷藏车燃油成本、货损最小化等的多目标路径优化模型.配送中心创新性引入软时间窗下的惩罚成本和碳排放成本作为新增约束条件,运用Matlab进行仿真,通过逆转变异算子和插入变异算子对遗传算法进行优化,并利用贪婪算法局部优化的特点产生更优个体,构建考虑成本因素、货损因素等为决策变量的最优路径规划模型.通过算例发现改进后的遗传算法求解的配送路径会使成本和货损均得到明显减少,并检验了改进后算法的有效性和可行性,以期为生鲜产品在配送车辆的路径规则领域提供新的理论支撑和实践依据.

With the transformation of China's economic structure and the transformation of residents'consumption mode,cold chain logistics has developed rapidly.In order to realize the low cost of fresh product distribution and the healthy development of cold chain logistics under the low-carbon economic environment,this paper proposes a multi-target path optimization model based on the fuel cost and cargo loss reduction of refrigerated vehicles.The distribution center innovatively introduced the penalty cost and carbon emission cost under the soft time window as new constraints.Matlab was used for simulation,and the genetic algorithm was optimized by reversing mutation operator and inserting mutation operator.The optimal path planning model is constructed by using the local optimization characteristics of greedy algorithm to generate better individuals,and considering cost factors and goods damage factors as decision variables.Through calculating examples,the distribution route solved by the improved genetic algorithm will significantly reduce the cost and cargo loss,and test the effectiveness and feasibility of the improved algorithm,in order to provide new theoretical support and practical basis for fresh products in the field of the path rules of distribution vehicles.