基于动态博弈的美国西部封港事件的剖析

根据动态博弈理论和列昂惕夫模型对美国西部封港风波进行了分析，并运用纳什讨价还价模型对其可能出现的结果进行了研究．研究表明，经过双方的博弈，会得到在一段时间内的帕累托改进，即得到一个相对稳定的均衡点．随着时间和环境的变化，这个均衡点会发生相应的波动，再相对稳定(在一段时间内)，周而复始．因此，可以预计，美国的封港事件还会发生．     

Robust design of a passive linear quarter car suspension system using a multi-objective evolutionary algorithm and analytical robustness indexes

This paper deals with the robust design of a passive vehicle suspension system. A robust design methodology based on a multi-objective evolutionary algorithm (MOEA) is used to handle the trade-off between the considered conflicting performance requirements under uncertainty and feasibility constraints. A constrained multi-objective optimisation problem is formulated and the notion of Pareto-optimality is used to increase the quality of the candidate design solutions obtained at each generation by the MOEA. To save computation time, a simplified physical model (quarter car) is considered and the optimisation is performed in the frequency domain, using relevant transmissibilities of the system. The robustness is directly investigated by means of analytical robustness indexes. Time-consuming a posteriori methods, like designs of experiments or Monte Carlo analysis, are therefore avoided. A set of non-dominated solutions is obtained. Thus the designer not only selects a special design, in accordance with the wanted vehicle configuration, but also includes the robustness of each performance requirement in his final decision.     

Solving the Median Shortest Path Problem in the Planning and Design of Urban Transportation Networks Using a Vector Labeling Algorithm

This paper proposes an alternative algorithm to solve the median shortest path problem (MSPP) in the planning and design of urban transportation networks. The proposed vector labeling algorithm is based on the labeling of each node in terms of a multiple and conflicting vector of objectives which deletes cyclic, infeasible and extreme-dominated paths in the criteria space imposing cyclic break (CB), path cost constraint (PCC) and access cost parameter (ACP) respectively. The output of the algorithm is a set of Pareto optimal paths (POP) with an objective vector from predetermined origin to destination nodes. Thus, this paper formulates an algorithm to identify a non-inferior solution set of POP based on a non-dominated set of objective vectors that leaves the ultimate decision to decision-makers. A numerical experiment is conducted using an artificial transportation network in order to validate and compare results. Sensitivity analysis has shown that the proposed algorithm is more efficient and advantageous over existing solutions in terms of computing execution time and memory space used.     

航空公司合作博弈下的时隙交换模型

针对航班波运行方式下的地面等待问题，提出了时隙交换策略。以参与交换各方的最小旅客延误为目标函数，以Pareto改进解为约束条件，运用合作博弈理论建立了2个航空公司间的时隙交换模型。将时隙交换模型分解成时隙指派模型与时隙组合模型，使用时隙指派模型求给定时隙状况下的最优指派方案与目标函数值，使用时隙组合模型求2个航空公司的最优时隙分配方案。计算结果表明：在无时隙交换下，航空公司1的旅客总延误为238955min，航空公司2的旅客总延误为145170min；当2个航空公司相互交换6个时隙后，航空公司1的旅客总延误为232945min，航空公司2的旅客总延误为142130min，分别下降2．5％与2．1％；当航班信息完全时，时隙交换能够使航空公司获得最优Pareto改进解；当航班信息不完全时，航空公司可以通过讨价还价的方法获得较好的时隙交换方案。     

基于Epsilon 约束法的高速铁路客票多目标定价研究

高铁客票定价是多目标定价问题,既要保障旅客福利,又要增加企业合理利润. 以企业利润和旅客福利作为高铁定价目标,考虑旅客差异和不同运距情境下的多种运输方式竞争,研究高铁客票多目标定价. 结合Epsilon约束法构建双层规划模型,根据分层序列思想分解多目标问题,设计松弛算法得到纳什均衡解,由解对应的目标值得到帕累托边界,确定多目标定价最优决策. 计算结果表明,对比量价波动和目标值改进,多目标定价适合票价改革要求,时间敏感型客票和短途客票有较高涨价空间,多种运输方式竞争影响定价实施效果. 将 Epsilon 约束法应用于求解高铁客票定价多目标问题,有利于优化客运企业与旅客利益,为铁路部门提供高铁客票定价思路.     

Parking permits management and optimal parking supply considering traffic emission cost

Given a many-to-one bi-modal transportation network where each origin is connected to the destination by a bottleneck-constrained highway and a parallel transit line, we investigate the parking permit management methods to minimize traffic time cost and traffic emission cost simultaneously. More importantly, the optimal supply of parking spots is also discussed in the policies of parking permit. First, we derive the total travel costs and emission costs for the two cases of sufficient and insufficient parking spot provisions at the destination. Second, we propose a bi-objective model and solve the Pareto optimal parking permit distribution, given a certain level of parking supply. Third, we investigate the optimal parking supply in the policy of parking permit distribution, with the objectives of minimizing both total travel cost and traffic emission. Fourth, we provide a model of optimizing parking supply, in the policy of free trading of parking permits. Finally, the numerical examples are presented to illustrate the effectiveness of these schemes, and the numerical results show that restricting parking supply at the city center could be efficient to reduce traffic emission.     

The multi-objective railway timetable rescheduling problem

Unexpected disruptions occur for many reasons in railway networks and cause delays, cancelations, and, eventually, passenger inconvenience. This research focuses on the railway timetable rescheduling problem from a macroscopic point of view in case of large disruptions. The originality of our approach is to integrate three objectives to generate a disposition timetable: the passenger satisfaction, the operational costs and the deviation from the undisrupted timetable. We formulate the problem as an Integer Linear Program that optimizes the first objective and includes $\epsilon$-constraints for the two other ones. By solving the problem for different values of $\epsilon$, the three-dimensional Pareto frontier can be explored to understand the trade-offs among the three objectives. The model includes measures such as canceling, delaying or rerouting the trains of the undisrupted timetable, as well as scheduling emergency trains. Furthermore, passenger flows are adapted dynamically to the new timetable. Computational experiments are performed on a realistic case study based on a heavily used part of the Dutch railway network. The model is able to find optimal solutions in reasonable computational times. The results provide evidence that adopting a demand-oriented approach for the management of disruptions not only is possible, but may lead to significant improvement in passenger satisfaction, associated with a low operational cost of the disposition timetable.     

改进的遗传算法在游艇舱室布局优化设计中的应用

针对游艇舱室布局这一复杂的多目标问题,在探讨遗传算法基本原理的基础上,提出以基于Pareto前沿的多目标优化方法NSGA-Ⅱ进行求解,然后分析游艇舱室布局的数学优化模型,并用NSGA-Ⅱ算法对其进行优化计算,并用实例进行验证。结果表明,这种算法确实有效,并得到一系列满足要求的最优解。     

可变编组动车组运维一体化帕累托前沿分析

动车组运用和检修是铁路运输生产的重要过程,占据很大的成本比例. 为与客流需求相匹配,在未来实现跨线动车组列车在某些枢纽站组合和分解存在可能,充分考虑组合动车组类型一致性,动车组在不同运行线进行重联与分解的接续条件,动车组进行一二级修的里程和时间标准等,以动车组的正常接续和空走接续时间里程费用、一二级维修作业费用、动车组运用费用和动车组接续时间费用之和最小及动车组空走费用占比最小为双目标,构建基于可变编组条件下的动车组运用和维修一体化模型方法.应用改进的非支配排序遗传算法求解,将目标值进行帕累托前沿分析,发现两者很难同时达到最优.模型的计算结果体现了动车组担当长编组运行线任务前后的组合和分解过程,同时也进一步分析了在动车组类型归一化或者动车组维修地点不受限情况下动车组运用数量的变化,反应了相关运行参数对结论的影响.     

再论线性多目标规划有效解的有效率

对文（1）中的优势集进行重新定义，讨论了非劣极点优势集的性质，在此基础上修正了文（1）中有效率的定义。