多目标模糊机会约束规划的低碳多式联运路径优化

Low-carbon multimodal transportation path optimization based on multi-objective fuzzy chance-constrained programming

针对时间窗与需求量不确定性下的多式联运路径优化问题,运用梯形模糊数表示模糊需求量与模糊时间窗,并考虑碳排放成本、运输成本以及客户满意度,建立了多目标模糊机会约束模型。固定的交叉、变异概率会直接影响算法的收敛性,针对此问题,将自适应性与非支配排序遗传算法Ⅱ(NSGA-Ⅱ)结合,并通过与DOCPLEX和NSGA-Ⅱ的对比验证了所提模型与算法的有效性。最后,探究了碳税值与模糊需求量偏好值的变化对优化结果的影响。研究结果表明:碳税值的提出可有效促进“公转铁、公转水”,从而显著减少碳排放量,然而过高的碳税值并不一定意味着碳排放量的减少,还会对企业造成过高的成本;模糊需求量偏好值的提高会造成总成本的增加,意味着运输经济性与可靠性两者不可兼得。因此,合理设置碳税值与模糊需求量偏好值是提高多式联运环保效益与运输效益的有效方式。

Aiming at the optimization problem of multimodal transportation path optimization under the uncertainty of time window and demand,trapezoidal fuzzy numbers were used to express fuzzy demand and fuzzy time window,and a multiobjective fuzzy chance-constrained model was established considering carbon emission costs,transportation costs,and customer satisfaction.The fixed crossover and mutation probabilities will directly affect the convergence of the algorithm.For this problem,the adaptability was combined with the Non-dominated Sorting Genetic Algorithm-Ⅱ(NSGA-Ⅱ),and the effectiveness of the proposed model and algorithm was verified by comparing them with DOCPLEX and NSGA-Ⅱ.Finally,the influence of changes in carbon tax and fuzzy demand preference value on optimization results were explored.The research results show that the proposal of carbon tax can effectively promote“road-to-rail transportation and road-to-water transportation”significantly,thereby reducing carbon emissions.But too high carbon tax does not necessarily reduce carbon emissions,and also causes excessive costs to enterprises.And the increase of fuzzy demand preference value will lead to the increase of total cost,which means that transportation economy and reliability cannot be obtained at the same time.Therefore,setting reasonable carbon tax and fuzzy demand preference value is an effective way to improve the environmental benefit and transportation benefit of multimodal transportation.