基于多目标规划的公交车调度方法研究

公交调度要考虑公司和乘客的多方利益。目标规划法是运筹学中解决多目标问题的有效方法,而优先因子的采用可以区分调度中的各方利益的主次。同一优先因子下加入了权重,可根据实际情况做出灵活有效的调度方案。     

航道货运量预测方法及其应用

为了精确预测航道未来货运量需求,克服传统预测方法无法实现复杂非线性拟合的缺点,分析了航道货运量的影响因素,探讨了遗传规划方法在航道货运量预测问题上的应用.根据遗传规划巾复制、交换、变异等进化方式,建立了基于遗传规划的航道货运量预测模型.以江苏省连申线苏北段历年货运量及区域经济发展状况为样本,采用遗传规划方法自动找出货运量随时间变化的规律,并对未来货运量进行了预测.结果表明,本箅法的计算相对误差很小,预测结果合理可信,且优于常用的回归预测,为解决航道货运量等非线性系统预测提供了一条新的途径.     

Application of genetic algorithms for joint optimization of signal setting parameters and dynamic traffic assignment for the real network data

This paper presents the joint optimization of signal setting parameters and dynamic user equilibrium (DUE) traffic assignment for the congested urban road network. The simulation-based approach is employed to obtain the DUE condition for the case of multiple-origin multiple-destination traffic flows. The dynamic traffic assignment simulation program (DTASP), developed in C language is used to assign the traffic dynamically on the road network, whereas method of successive averages (MSA) is modified and used to arrive at the DUE condition. The artificial intelligence technique of genetic algorithms (GAs) is applied to obtain the optimal signal setting parameters and path flow distribution factor for DUE condition. The methodology developed in such a way that joint optimization of signal setting parameters with DUE is obtained. The proposed method is applied to the real network data of Fort Area of Mumbai city comprising of 17 nodes and 56 unidirectional links with 72 Origin–Destination pairs, where all the 17 nodes are signalized intersections. The traffic flow condition for the optimized signal setting parameters is considerably improved compared to the existing signal settings. The results prove that the GA is an effective technique to solve the joint optimization problem for the real network data.     

A bi-objective dynamic programming approach for airline green fleet planning

Ensuring a fleet of green aircraft is a basic step in mitigating aviation pollution issues that are expected to be worsen in the coming years due to rapid air traffic growth. This study proposed a novel methodology in green fleet planning in which both profit and green performance of airline are considered simultaneously and explicitly. To do this, a Green Fleet Index (GFI) is derived as an indicator to quantify the green performance of airline’s fleet. It measures the degree of airline compliance with a standard requirement in terms of emission, noise, and fuel consumption. A bi-objective dynamic programming model is then formulated to find optimal aircraft acquisition (lease or purchase) decision by minimizing GFI and maximizing profit. Several interesting results are obtained: (1) considering environmental issue as secondary objective yields a greener fleet; (2) airline’s profit is affected, but could be recovered from environmental cost savings; (3) increasing load factor is an effective operational improvement strategy to enhance airline’s green performance and raise profit level. It is anticipated that the framework developed in this study could assist airlines to make a smart decision when considering the need to be green.     

Liner ship route schedule design with port time windows

This paper examines a practical tactical liner ship route schedule design problem, which is the determination of the arrival and departure time at each port of call on the ship route. When designing the schedule, the availability of each port in a week, i.e., port time window, is incorporated. As a result, the designed schedule can be applied in practice without or with only minimum revisions. This problem is formulated as a mixed-integer nonlinear nonconvex optimization model. In view of the problem structure, an efficient holistic solution approach is proposed to obtain global optimal solution. The proposed solution method is applied to a trans-Atlantic ship route. The results demonstrate that the port time windows, port handling efficiency, bunker price and unit inventory cost all affect the total cost of a ship route, the optimal number of ships to deploy, and the optimal schedule.     

二层线性规划的模糊数学解法(Ⅱ)

针对上层有约束条件、下层有Ｎ个独立的决策单元的二层线性规划问题，提出了一种模糊数学解法。首先，把这种规划分解为若干单层规划；然后引入隶属函数，用３个定理对各单层规划的解进行讨论，最终把这种二层线性规划转化为求解一个单层线必规划问题。使问题得到了简化。     

基于R树面向对象的航海资料数据模型

提出了基于面向对象技术和Ｒ树索引结构的航海资料数据模型。它通过面向对象技术的继承性有效地解决了信息存储的冗余问题，体现了航海资料的自然层次结构，与航海人员的思维定式相吻合。同时，通过Ｒ树建立了航海资料二维动态索引，实现了航海信息快速的查询。     

Containership scheduling with transit-time-sensitive container shipment demand

This paper examines the optimal containership schedule with transit-time-sensitive demand that is assumed to be a decreasing continuous function of transit time. A mixed-integer nonlinear non-convex optimization model is first formulated to maximize the total profit of a ship route. In view of the problem structure, a branch-and-bound based holistic solution method is developed. It is rigorously demonstrated that this solution method can obtain an ε-optimal solution in a finite number of iterations for general forms of transit-time-sensitive demand. Computational results based on a trans-Pacific liner ship route demonstrate the applicability and efficiency of the solution method.     

铁路罐式集装箱空箱调配优化模型及遗传模拟退火算法

针对罐式集装箱运输的特殊性,考虑重箱流和空箱流调配的综合优化,以罐箱运输费用最小为目标,建立铁路罐式集装箱空箱调配优化多商品网络流模型,并构造了1种嵌入模拟退火操作的遗传算法对之进行求解。为了使模型与算法可得到更符合实际、操作性更强的结果,给出了3种空罐箱调配的策略,作为隐含条件加入到算法求解过程中。利用自适应遗传模拟退火算法对随机生成的实际规模问题算例进行求解,并与用通用代数建模系统软件GAMS的计算结果进行对比。结果表明,前者得出的结果与最优解差距不大,而且运算速度更快,更能满足解决实际问题的需要,为铁路罐箱调配优化提供了良好的决策支持模型和算法。     

A design methodology for mechatronic vehicles: application of multidisciplinary optimization,multibody dynamics and genetic algorithms

A design methodology for mechatronic vehicles is presented. With multidisciplinary optimization (MDO) methods, strongly coupled mechanical, control and other subsystems are integrated as a synergistic vehicle system. With genetic algorithms (GAs) at the system level, the mechanical, control and other relevant parameters can be optimized simultaneously. To demonstrate the feasibility and efficacy of the proposed design methodology for mechatronic vehicles, it is used to resolve the conflicting requirements for ride comfort, suspension working spaces and unsprung mass dynamic loads in the optimization of half-vehicle models with active suspensions. Both deterministic and random road excitations, both rigid and flexible vehicle bodies and both perfect measurement of full state variables and estimated limited state variables are considered. Numerical results show that the optimized vehicle systems based on the methodology have better overall performance than those using the linear quadratic Gaussian (LQG) controller. It is shown that the methodology is suitable for complex design optimization problems where: (1) there is interaction between different disciplines or subsystems; (2) there are multiple design criteria; (3) there are multiple local optima; (4) there is no need for sensitivity analysis for the optimizer at the system level; and (5) there are multiple design variables.