铁路冷链物流运输多目标机会约束规划

Multi-Objective Chance-Constrained Programming of Railway Cold Chain Logistics

摘要:

以运输网络总成本、碳排放量和货损率最低为目标，建立了随机需求、运能限制的铁路冷链物流网络优化模型，采用机会约束规划理论将模型进行确定性转化，设计了自适应遗传?模拟退火算法（A?SAGA），通过MATLAB仿真计算最优冷链物流运输方案集，并对运价补贴、列车旅速、冷链货物品类进行灵敏度分析。结果表明，在同时考虑以上3个冲突目标下，当采取5%的运价补贴时，或仅提高列车旅速至70 km?h^-1时，铁路市场份额的提升效果明显；不同基础设施条件下可灵活采用这两种方式；对运输时间敏感程度不同的货物直接影响货损率的变化以及铁路市场份额的初始值。

Abstract:

Aimed at the lowest total cost， carbon emission， and freight loss rate of transportation network， an optimization model of railway cold chain logistics network with stochastic demand and transportation capacity constraints was established. The model is transformed deterministically using the chance-constrained programming theory， and an adaptive genetic-simulated annealing algorithm （A-SAGA） is designed. The optimal cold chain logistics transportation scheme set is obtained by MATLAB simulation calculation. The sensitivity analysis of freight subsidy， train travel speed and freight category are conducted. The simulation results show that under the multiple conflicting objectives of considering transportation costs， carbon emissions， and cargo loss rates， rail cold chain market share can be improved significantly when 5% of the freight subsidy is applied， or only 70 km?h^-1 of train travel speed is increased. These two methods can be used flexibly under different infrastructure conditions. Cargo with varying degrees of sensitivity to transit time directly affects the change in the rate of cargo loss and the initial value of rail market share.