时间有色佩特里网在航班进离港中应用

为了解决目前和以后越来越严重的空中交通拥挤问题,文中对终端区空中交通流量管理中航班进离港部分连续航班动态飞机排序问题进行研究.提出了基于时间有色佩特里网(TCPN)的航班进离港的调度方法,用MATLAB软件中的M函数编写了算法程序,然后进行了仿真.仿真结果表明,该方法选用的算法是合理的,而且可以明显的减少航班总的延误,优化效率高,算法简单可行,而且更符合实际的要求.该方法在一定程度上可以帮助空管人员进行调度决策,从而更加快速有序地对飞机进离港进行调度,对目前空管优化调度,减少延误等情况的处理具有一定的参考研究价值.     

基于多目标分阶段的跑道调度计划算法*

针对多条跑道环境下离港飞机调度问题,提出了一种基于多目标、两阶段算法。算法第一阶段以飞机重量类型为主要分解参数,生成离港飞机序列。该参数在跑道调度计划问题上比其他参数更具影响力和稳定性。算法第二阶段从离港飞机队列池中选取可用序列,将特殊航班指配到目标类型序列中,生成优化的飞行航班时刻表。实验表明,采用两阶段跑道调度计划算法进行多跑道离港飞机调度比采用先来先服务算法调度在跑道总吞吐量上有明显改善,能有效降低机场航班延误,提高跑道运营效率。     

航班过站地面服务的优化调度算法

航班过站服务流程是定位型和零工型的混合流程，其调度问题是一个有时间窗和作业调整时间的多目标多设备并行作业动态排序问题．在分析其区别于一般制造业生产作业排序特点的基础上，给出一个考虑了不同设备加工能力的新的启发式算法——设备能力差分配法．通过对服务作业分类，将多目标优化问题转化为服务类作业排序最优化问题．算例分析显示，能力差分配算法在减少航班延误数量、时间上以及平衡设备生产能力上均优于现有的先到先服务和最小负荷调度算法．     

航班队列调度算法的研究与性能分析

针对航班流量激增造成的航班延迟问题,提出了几种航班队列调度算法.通过对几种调度算法的原理进行分析研究,结合航班进离港过程的特点,针对这些算法的优缺点,搭建了仿真平台.采用理论分析与仿真相结合方法,对比研究了几种调度算法的性能,发现在不同权值下WFQ算法都表现出了较优的性能,而且权值越大、延迟越小;所以在没有突发事件的情况下,可以优先考虑选择使用WFQ算法来进行调度,使其减少延迟.对有效解决交通拥挤问题提供了重要的参考价值.     

基于贝叶斯网络的航班串运行脆弱性研究

为研究航班串中某一关键航班发生进离港延误对该航班串中其它航班进离港准点率造成的影响,采用贝叶斯网络对航班串运行进行建模,识别并评价关键航班的脆弱性,并提出降低航班串运行脆弱性的建议。在受到管制、天气、军事活动、航班在机场异常过站以及其它异常情况等不确定因素影响下,研究航班串进离港准点率关系,并找出航班串运行时的脆弱节点以及评价关键航班的延误状态。通过对比分析增加关键航班过站时间航班串运行前后的进离港准点率,结果表明,增加关键航班过站时间可以降低航空公司航班串运行脆弱性。     

基于贝叶斯网的离港航班滑行时间动态估计

为提升离港航班运行效率，根据机场协同决策规范（A-CDM）中关于离港航班可变滑行时间（EXOT）的有关规定，分析了相关影响因素。根据数据分析处理和民航专家知识建立了一种基于贝叶斯网的离港航班滑行时间动态估计模型。贝叶斯网是一种将概率统计应用于复杂领域、进行不确定性推理和数据分析的工具。应用其增量学习特点对模型进行动态调整，实现了对场面实时变化的把控。以国内某大型枢纽机场为例，使用期望优化（EM）算法实现了对随机缺失数据的处理，并验证了模型的有效性。对实验结果与该机场实际运行数据对比表明，所建模型能有效地估计离港航班滑行时间且具有较高的置信度。     

协同模型在航班进离港调度系统中的应用

针对当前航班进离港调度系统中调度效率较低等问题,引入了基于角色的协同模型,利用这个模型提出了基于协同模型的航班进离港调度系统.利用关注点分离的思想,通过将系统分为3个层次,即底层飞机实体,对飞机协同和监控的角色以及负责协同角色行为的协调器,将系统的功能行为与协同行为分离,可以对飞机实体动态绑定,动态调整系统的协同行为,动态配置系统的决策规范,解决了系统的开放性和动态性等问题.     

基于遗传算法的离场航班时刻优化

针对枢纽机场航班延误和放行正常率低的现象,对离场航班进行合理的调度,将航班延误降到最低,提高放行正常率。作者从机场运行角度出发,考虑了机场实际放行能力、航班调度难度和管制间隔、跑道容量等因素,建立了离场航班优化调度模型,引入遗传算法对该模型进行求解。并利用AirTop对优化结果仿真验证,以上海虹桥国际机场为例,利用所提算法对离港航班时刻进行优化,AirTop仿真结果表明,优化后的航班总延误时间减少了32.3%,延误航班数量也减少了28%。因此,上述算法可以有效地提高机场运行效率,减少航班延误,从而提高经济效益。     

混合量子算法及其在flow shop问题中的应用

量子进化算法（QEA）是目前较为独特的优化算法,它的理论基础是量子计算。算法充分借鉴了量子比特的干涉性、并行性,使得QEA求解组合优化问题具备了可行性。由于在求解排序问题中,算法本身存在收敛慢,没有利用其它未成熟个体等缺陷,将微粒群算法（PSO）及进化计算思想融入QEA中,构成了混合量子算法（HQA）。采用flow shop经典问题对算法进行了测试,结果证明混合算法克服了QEA的缺陷,对于求解排序问题具有一定的普适性。     

基于满意度准则的机场终端区容量利用和流量分配的多目标优化模型研究

为优化跑道容量和定位点空中交通流量,将机场终端区的进离港定位点和跑道看作一个系统;在 服从整个系统容量限制的前提下,引入了满意度准则,通过分析跑道容量和定位点流量的相互影响,建立了 多目标优化模型．算例研究表明,通过对进离港航班流量需求的协调分配,给定时段内的所有航班均得到有 效调度．整体航班需求排队队列减少了约10%,容量利用满意度为0.75,在机场终端区实现了跑道容量的有 效利用和定位点流量的合理分配．     

单向航道下连续泊位分配与船舶调度集成优化

为改善单向航道连续泊位港口的运营效率,研究泊位分配与船舶进出港调度集成优化.考虑潮汐、进出港时段交替与偏好泊位的影响,建立0-1整数线性规划模型,以船舶偏离偏好泊位成本和滞期成本为优化目标,确定各艘船舶的靠泊位置与进出港时刻.针对问题情境和其特有的约束条件,将原数学模型通过Dantzig-Wolfe分解方法分成主问题模...     

Iterative improvement to solve the parcel hub scheduling problem

This paper presents iterative improvement algorithms to solve the parcel hub scheduling problem (PHSP). The PHSP is combinatorial optimization problem that consists of scheduling a set of inbound trailers to a small number of unload docks. At the unload docks, the inbound trailers must be unloaded and the parcel sorted and loaded onto outbound trailers. Because the transfer operation is labor intensive, the transfer of parcels must be done in such a way as to minimize the timespan of the transfer operation. Local search (LS) and simulated annealing (SA) algorithms are developed and evaluated to solve the problem. The performances of the algorithms are compared to the performance of an existing genetic algorithm (GA). The computational results show that the LS and SA algorithms offer solutions that are superior to those offered by the GA.     

基于改进蚁群协同算法的枢纽机场场面滑行道优化调度模型

滑行道连接停机位和跑道,是机场场面调度的重要关键环节。基于飞机滑行时的冲突约束和跑道资源的动态分配,采用改进蚁群协同算法与滑动窗口控制相结合的方法,对滑行道进行优化调度。在保证滑行道零冲突、兼顾单个航班滑行时间的前提下,缩小机场进出港航班总滑行时间。对国内某枢纽机场的滑行道调度仿真实验表明,所提出的方法和模型具有明显的优势,可为枢纽机场的场面滑行调度提供决策支持。     

Genetic-based scheduling to solve the parcel hub scheduling problem

The parcel hub scheduling problem (PHSP) is scheduling problem that is common in the parcel delivery industry. The PHSP consists of scheduling a set of inbound trailers containing a batch of heterogeneous parcels to a set of unload docks with the objective of minimizing the time span of the transfer operation. The transfer operation involves unloading the inbound trailers and sorting and loading the parcels onto outbound trailers. The unload schedule can have a significant impact on the amount of time required to complete the transfer operation. In this paper, a mathematical model is proposed to solve small-size problems and a genetic algorithm is presented to solve the large-size problems with minimum computational time compared to other competing approaches.     

基于A~*算法的机场滑行路径优化研究

研究机场场面航班的滑行路径分配和滑行冲突问题。针对目前滑行效率制约机场调度。滑行冲突影响飞行安全的状态,提出将安全间隔、滑行规则和冲突避免限制规则作为约束条件建立了滑行路径优化模型。采用了A*算法给出了问题的优化,在保证滑行道调度零冲突的前提下,缩小航班的总滑行时间,提高了滑行效率。以重庆江北国际机场为例,与管制员依据机场细则得到的路径相比,结果表明提出的滑行道调度模型和优化方法是可行的,在实现较优的滑行路径的基础上能智能的发现冲突、解决冲突,可为繁忙机场的机场调度提供了决策支持。     

A dynamic load-balancing scheme for the parcel hub-scheduling problem

In this paper, a dynamic load-balancing algorithm (DLBA) was proposed to solve the parcel hub-scheduling problem (PHSP), which is a combinatorial optimization problem commonly found at a parcel consolidation terminal in the parcel delivery industry (PDI). The problem consists of processing a large number of inbound trailers at a much smaller number of unload docks. The parcels in the inbound trailers must be unloaded, sorted and transferred to the load docks, and loaded onto the outbound trailers. Because the transfer operation is labor intensive and the PDI operates in a time-sensitive environment, the unloading, sorting, transferring, and loading of the parcels must be done in such a way as to minimize the timespan of the transfer operation. A DLBA that considered the state of the system was proposed and evaluated to schedule the inbound trailers to the unload docks. The performance of the algorithm was compared to the performance of an efficient static load-balancing algorithm (SLBA) and to an existing lower bound. An experimental analysis shows that the DLBA offers solutions that are 8.6% better than the solutions offered by the SLBA for large-size problems and that the average computational time is only 1.34 min for the DLBA compared to 15.04 min for the SLBA.     

Quay crane and yard truck dual-cycle scheduling with mixed storage strategy

In order to enhance the efficiency of port operations, the scheduling problem of the quay cranes and yard trucks is crucial. Conventional port operation mode lacks optimization research on efficiency of port handling operation, yard truck scheduling, and container storage location. To make quay crane operations and horizontal transportation more efficient, this study uses a dual-cycle strategy to focus on a quay crane and yard truck scheduling problem in conjunction with a mixed storage strategy. A dispatching plan for yard trucks is considered, as well as the storage location of inbound containers. Based on the above factors, a mixed-integer programming model is formulated to minimize vessels’ berth time for completing all tasks. The proposed model is solved using a particle swarm optimization-based algorithm. Validation of the proposed model and algorithm is conducted through numerical experiments. Additionally, some managerial implications which may be potentially useful for port operators are obtained.     

Scheduling trucks in cross docking systems with temporary storage and repetitive pattern for shipping trucks

Cross docking is a logistic concept in which product items are unloaded from inbound trucks into a warehouse and then are sorted out based on customer demands and loaded into outbound trucks. For a dock holding pattern for outbound trucks, two possible scenarios can be defined. In the first scenario, whenever a truck goes into a shipping dock, it does not leave the dock until all needed product items are loaded into outbound truck. In the second scenario, outbound trucks can enter and leave the dock repeatedly. Therefore, in the second scenario it is possible that an outbound truck loads some of its needed products from shipping dock, leaves the dock for another outbound truck, waits and goes into the shipping dock again to load all or part of its remaining product items. This paper proposes a genetic algorithm-based framework for scheduling inbound and outbound trucks in cross docking systems with temporary storage of product items at shipping dock for the second defined scenario such that minimizes total operation time. In order to show the merit of the proposed method in providing a sequence that minimizes the total operation time, the operation time of the proposed method is compared to a well-known existing model by several numerical examples. The numerical results show the high performance of the proposed algorithm.     

Minimizing the completion time of the transfer operations in a central parcel consolidation terminal with unequal-batch-size inbound trailers

This paper addresses the scheduling of inbound trailers to unload docks at central parcel consolidation terminals in the parcel delivery industry, an industry that operates in a time-critical environment. The scheduling function can have a significant impact on the amount of time required to unload the inbound trailers and sort and load the parcels to the outbound trailers. This problem is known as the parcel hub scheduling problem (PHSP). To solve the PHSP, a simulation-based scheduling approach with an embedded genetic algorithm is proposed. The results show that the proposed scheduling approach is able to reduce the amount of time required to unload the inbound trailers by approximately 3.5 percent compared to a previously developed algorithm and about 16.1 percent compared to an approach that is representative of current industry practice.     

多目标柔性车间调度的文化基因非支配排序粒子群算法

本文以离散型柔性制造车间为对象, 以缩短生产周期、减少机器空转时间和提高产品合格率为优化目标, 提出一种文化基因非支配排序粒子群算法. 该算法采用二维编码方式. 首先, 分别对工序和机器分配进行不同的变异操作, 建立了多目标离散型资源优化调度模型. 然后, 采用非支配排序策略和随机游走法获得Pareto最优解, 接着利用层次分析法给出资源优化配置方案. 最后, 利用实际生产数据进行仿真, 结果表明所提出的优化算法具有平衡全局搜索能力和局部搜索能力的特性.