An integrated programming model for storage management and vehicle scheduling at container terminals

In this paper, we study the optimization of yard operations, which are critical for the terminal efficiency. A linear mixed integer programming (MIP) model is proposed for scheduling different types of equipment and planning the storage strategy in an integrated way. We also investigate a nonlinear mixed integer programming (NLMIP) model to reduce the number of constraints and the computational time. A set of numerical results are carried out for the comparison between the linear model and the nonlinear model. Finally, we propose a genetic algorithm for the MIP model to illustrate how large scale problems can be solved and to show the effect of different factors on the performances of the optimization model.     

The time slot allocation problem under uncertain capacity

This paper presents two stochastic programming models for the allocation of time slots over a network of airports. The proposed models address three key issues. First, they provide an optimization tool to allocate time slots, which takes several operational aspects and airline preferences into account; second, they execute the process on a network of airports; and third they explicitly include uncertainty. To the best of our knowledge, these are the first models for time slot allocation to consider both the stochastic nature of capacity reductions and the problem’s network structure. From a practical viewpoint, the proposed models provide important insights for the allocation of time slots. Specifically, they highlight the tradeoff between the schedule/request discrepancies, i.e., the time difference between allocated time slots and airline requests, and operational delays. Increasing schedule/request discrepancies enables a reduction in operational delays. Moreover, the models are computationally viable. A set of realistic test instances that consider the scheduling of four calendar days on different European airport networks has been solved within reasonable – for the application’s context – computation times. In one of our test instances, we were able to reduce the sum of schedule/request discrepancies and operational delays by up to 58%. This work provides slot coordinators with a valuable decision making tool, and it indicates that the proposed approach is very promising and may lead to relevant monetary savings for airlines and aircraft operators.     

Assigning fighter plane formations to enemy aircraft using fuzzy logic

Fighter aircraft protect specific facilities on alert in the air by patrolling expectation zones. These zones are located in the direction from which enemy aircraft attacks are expected; fighter formations are sent from them to intercept enemy aircraft. The problem considered in this paper is to determine the optimum assignment of fighter plane formations to enemy formations. The proposed solution is based on fuzzy logic and integer linear programming. A numerical example is given to illustrate the application possibilities of the proposed solution.     

Future electricity demand from battery‐powered vehicles

An important decision faced by airline schedulers is how to adapt the flight schedule and aircraft assignment to unforeseen perturbations in an established schedule. In the face of unforeseen aircraft delays, schedulers have to decide which flights to delay, and when delays become excessive, which to cancel. Current scheduling models deal with simple decision problems of delay or cancellation, but not with both simultaneously. But in practice the optimal decision may involve results from the integration of both flight cancellations and delays. In Part I of this paper, a quadratic programming model for the integration decision problem is given. The model can formulate the integration of flight cancellations and delays as well as some special cases, such as the ferrying of surplus aircraft and the possibility of swapping different types of aircraft. In this paper, based on the special structure of the model, an effective algorithm is presented, sufficient computational experiments are conducted and some results are reported. These show that we can expect to obtain a sufficiently good solution in terms of reasonable CPU time.     

基于双层规划模型的地下综合管廊PPP项目入廊定价研究

为解决地下综合管廊项目PPP模式运作过程中存在的入廊定价难的突出问题,首先,基于现阶段入廊价格管制的需要以及各参与方在入廊定价决策过程中的主从递阶结构特点,决定利用双层规划模型研究PPP模式下地下综合管廊项目的入廊定价问题,将政府方作为定价决策的上层,社会资本方和管线单位作为决策的下层,建立政府、社会资本方与管线单位分别以社会福利最大化、预期收益最大化和保留效用最大化为目标的双层规划模型,以期求解得到令三方满意的入廊费、日常维护费与补贴额度。然后,以延安市地下综合管廊PPP项目为例,检验模型与求解方法的可行性。最后,基于研究结论提出加强顶层设计、强化价格监管、建立动态调价机制的合理化建议。     

A linear complementarity method for the solution of vertical vehicle–track interaction

A new method is proposed for the solution of the vertical vehicle–track interaction including a separation between wheel and rail. The vehicle is modelled as a multi-body system using rigid bodies, and the track is treated as a three-layer beam model in which the rail is considered as an Euler-Bernoulli beam and both the sleepers and the ballast are represented by lumped masses. A linear complementarity formulation is directly established using a combination of the wheel–rail normal contact condition and the generalised-α method. This linear complementarity problem is solved using the Lemke algorithm, and the wheel–rail contact force can be obtained. Then the dynamic responses of the vehicle and the track are solved without iteration based on the generalised-α method. The same equations of motion for the vehicle and track are adopted at the different wheel–rail contact situations. This method can remove some restrictions, that is, time-dependent mass, damping and stiffness matrices of the coupled system, multiple equations of motion for the different contact situations and the effect of the contact stiffness. Numerical results demonstrate that the proposed method is effective for simulating the vehicle–track interaction including a separation between wheel and rail.     

双频组合观测值解算北斗模糊度的探讨

针对北斗双频组合方式,基于矩阵变化和迭代过程,以两种不同线性组合的扩波方法,采用一种新的迭代过程,在满足波长较长以及降低模糊度解相关性的条件下,减小了模糊度搜索空间大小,提高了模糊度解算效率和速度。     

需求不确定状态下的航班机型分配问题研究

机型分配问题是飞机、机组排班问题的基础,是整个航班计划中的关键环节。文章针对市场需求高度不确定的情况,建立了两阶段随机混合整数规划模型,其中第一阶段考虑机族层次的分配,确保每个航节均分配1种且仅有1种机族,第二阶段根据进一步的市场需求信息及预测,考虑每个航节所分配的机族内具体机型的分配问题。针对建立的模型,设计了Benders分解算法,并通过算例验证了该模型较传统确定型模型更具有效性,能够为航空公司的机型分配提供决策支持。     

基于动态规划理论的船厂码头移泊作业问题研究

基于动态规划理论,建立了船厂码头移泊作业问题的动态规划模型.分析了移泊作业的特点,在此基础上建立起问题的动态模型和对应的赋值有向图,把问题转化为求解赋值有向图的最优路问题.依据图论相关知识,对赋值有向图进行改进,从而得到图的最优路,最终求得问题的最优方案.通过实例计算结果与船厂实际操作安排进行比较和分析,得出算法研究的有效性和优越性的结论.