不确定需求下考虑混合时间窗的多式联运路径优化

针对多式联运运输网络复杂等问题,提出一种基于不确定的客户需求,引入混合时间窗约束,以总成本、碳排放量最小为优化目标的多式联运优化模型,运用三角模糊数以及机会约束规划理论对模型进行清晰化.考虑粒子群算法的局限性,将模拟退火算法的思想与其结合,对基本粒子群算法进行改进.通过实例分析以及运用灵敏度分析法,分析了运量的不确定性以及节点运输能力与中转能力对路径优化产生的影响.结果表明,基于模拟退火的粒子群算法的寻优能力优于粒子群算法.随着客户对需求量的满意度增加,总运输成本和碳排放量也会增加,增强各种运输方式的运输能力和节点中转能力可以有效降低运输成本,优化运输路径,为决策者选择运输方案提供依据.

Multimodal transport path optimization considering mixed time windows under uncertain demand

Aiming at the complexity of multimodal transport network, this paper proposes a multi-modal transport optimization model based on uncertain customer demand, introducing mixed time window constraints and minimizing total cost and carbon emission.The model is clarified by using triangular fuzzy number and chance constrained programming theory. Considering the limitation of particle swarm optimization, the idea of simulated annealing algorithm is combined to improve the basic particle swarm optimization. Through case study and sensitivity analysis, the influence of uncertainty of traffic volume and node transportation capacity and transfer capacity on route optimization is analyzed. The results show that the optimization ability of particle swarm optimization based on simulated annealing is better than that of particle swarm optimization. With the increase of customer satisfaction with the demand, the total transportation cost and carbon emissions will also increase. Enhancing the transportation capacity and node transfer capacity of various transportation modes can effectively reduce the transportation cost, optimize the transportation path, and provide the basis for decision-makers to choose the transportation scheme.