需求与碳交易价格不确定下多式联运路径优化

Optimization of multimodal transportation under uncertain demand and stochastic carbon trading price

考虑不确定性的模糊路径优化研究能够满足高质量发展形势下市场、经济和环保等多方面的现实需要.针对需求与碳交易价格双重不确定下的多式联运路径优化问题,建立混合鲁棒随机优化模型,设计基于蒙特卡罗采样的灾变自适应遗传算法并进行有效性检验.以具有15个节点的多式联运网络为对象开展算例研究,比较不同模式下多式联运的运输方案及成本并分析不确定参数的影响.结果表明:受遗憾值约束的影响,需求不确定的鲁棒优化会增加多式联运总成本,而碳交易价格随机性的提高并不一定意味着成本的增加.合理设置碳交易价格的随机性,综合考量需求不确定的最大遗憾值与成本之间的关系等是提升双重不确定下多式联运运输效率与环保效益的有效方式.

The research on the optimization of low-carbon multimodal transportation path under uncertainty can present important theoretical significance,which can satisfy the practical needs of market,economy and environmental protection.This paper establishes a hybrid robust stochastic optimization model aiming at the problem of path optimization with double uncertainty of demands and carbon trading prices.Afterwards,a catastrophe adaptive genetic algorithm based on Monte Carlo sampling is designed and tested for validity.The multimodal transportation schemes and costs under different modes are compared and the impacts of uncertain parameters are analyzed by a 15-node multimodal transportation network case.The results reflect that:1) Robust optimization with uncertain demands could increase the total cost of multimodal transportation due to the pursuit of stability;2) The regret value of robust optimization will affect the total cost of multimodal transportation.However,the randomness of carbon trading prices should also be considered comprehensively in the double uncertainty mode;3) Carbon trading policy is an effective carbon emission reduction policy.The increasing randomness of carbon trading price is likely to reduce the total cost.Based on the above research results,making full use of the incentive effect of carbon trading policy to set the uncertainty of carbon trading prices and comprehensively considering the relationship between the maximum regret value of uncertain demands and costs can effectively improve the efficiency and environmental benefits of multimodal transportation under dual uncertainties.