基于滚动窗口策略下场桥实时调度研究

在实施场桥调度方案时,总会出现各种不确定干扰因素使得原先的方案不能正常实行。对此,提出一种在滚动窗口策略下处理不确定干扰因素的场桥调度方案,即当出现干扰时,触发窗口再调度机制,以减少干扰的影响。并且建立以任务完成最小延迟量的混合整数规划模型,用改进的遗传算法求解。考虑到滚动窗口策略方法受到几个参数的影响,还对滚动策略的滚动窗口大小进行研究,通过算例分析得出较合适的参数搭配,使得场桥作业效率提高,达到任务完成最大延迟量最小化的目标。     

基于滚动策略的集装箱码头连续泊位与桥吊集成调度

针对连续泊位与桥吊集成调度大规模求解困难的问题,提出一种基于滚动策略的优化方法。首先,建立了最小化船舶偏离偏好泊位的成本以及延迟靠泊、延迟离港的惩罚成本的基本的多目标优化模型;然后,采用滚动调度方法根据动态抵泊的船舶抵达顺序将调度过程分成连续的调度窗口,并设计窗口的平移策略、当前窗口对下一窗口的参数更新方式;对每个窗口内船舶进行调度优化,根据每个窗口内的优化结果,更新下一个窗口中数学模型的输入参数;通过选取以船舶数量表示的滚动计划窗口和冻结船舶的数量,持续滚动获得每个窗口的最优解,叠加后获得对所有船舶的靠泊计划。通过算例分析表明,滚动调度能够解决较大规模的调度问题,其效率受滚动窗口大小、冻结船舶数量及滚动次数影响     

基于滚动窗策略的岸桥集卡联合调度问题研究*

为应对集装箱码头突发事件的发生,采用滚动窗策略研究岸桥集卡的联合调度问题,在每个窗口内建立以最小化最大完工时间为目标的数学模型,并用遗传算法进行求解。在此基础上,讨论了以固定任务数量及固定时间长度为滚动窗口的情况,对比发现以时间为窗口的滚动窗策略更为适用。并分析了系统发生岸桥故障时滚动窗策略的性能,结果表明滚动窗策略能很好地应对突发事件。最后并将其与已有的动态调度研究成果作比较分析。     

不确定环境下码头堆场设备集成调度优化

集装箱码头堆场设备调度优化中,对确定条件下的内集卡和场桥的联合调度研究较多,且没有考虑外集卡的随机到达情况。考虑内集卡和场桥作业过程中的不确定性因素,包括：内集卡行驶速度,场桥行走速度和作业时间,并考虑外集卡随机到达堆场对于内集卡调度作业的影响,构建了不确定因素条件下的堆场设备集成调度优化模型,其优化目标是在考虑外集卡随机到达的情况下,最优化堆场设备的作业时间。设计了求解模型的粒子群算法,并比较了一般确定性模型和考虑不确定因素优化模型的结果。算例结果表明,所建立的模型和算法能有效真实地反映不确定因素对集装箱码头堆场设备作业的影响。     

速度不确定下作业时间最短的集卡调度优化

在集装箱码头装卸作业中,提高集卡调度对作业效率有非常大的影响。而集装箱卡车重车和空车行驶速度的不确定性,增加了集卡调度的难度。为此提出建立不确定环境下作业时间最短的集卡调度优化模型。在完成固定装卸任务的前提下,通过对集卡的合理调度,达到不确定情况下集卡总作业时间最短。针对不确定模型的特点,采用粒子群算法快速求解,同时保证了解的有效性。算例表明上述模型和算法合理解决了不确定环境下集卡调度优化问题,得到较好的计算结果,并且降低了运算复杂度,提高了调度效率。     

不确定环境下的岸桥集卡协调调度耦合模型建立与求解

集装箱码头系统是一个由多个子系统组成的复杂的生产系统,系统内资源的调度也是非线性的复杂问题,同时涉及多种多样的不确定性因素。从不确定性的角度出发,主要考虑码头装卸设备运行参数的概率分布,研究岸桥和集卡之间的协调调度问题。采用多学科变量耦合优化设计的方法,同时考虑了集装箱任务的时间窗约束,分别建立集卡分派子模型和集卡配置子模型。并将完工时刻和集卡数量作为公用设计变量连接两个子模型,建立了协调调度耦合模型。选取上海港某码头的数据编写算例,在Visual Studio 2012环境下调用Gurobi4.0求解该耦合模型,反复迭代计算后得出最优的集卡分派方案相对于最初的调度方案,总延误时间成本下降了90.69%,集卡数量下降了30.76%,验证了本模型的有效性和实用性。     

一种基于粗集的广义控制滚动调度策略

提出了一种基于粗集理论和广义控制思想的滚动调度建模策略。在调度工件加工过程中,考虑到有:工件加工完成;工件到期时间改变;急加工工件和新加工工件到达等突发事件发生时,对滚动调度窗口中的工件进行再选择,对窗口中工件实施再调度。仿真结果显示,利用该方法,不仅能适应动态加工环境的变化,而且降低了调度问题求解的规模。     

基于滚动窗口的WCDMA无线网络规划

借鉴滚动窗口优化的基本原理, 研究了大规模WCDMA无线网络基站的规划问题. 提出了基于滚动窗口的WCDMA无线网络基站滚动规划方法, 把原优化问题的整体求解分散在滚动窗口内进行, 设计了二维空间内的滚动策略, 同时对WCDMA网络中的相互干扰滚动预测, 证明了这种基于滚动窗口的规划方法能够保证全局优化目标单调下降. 通过对不同滚动策略的仿真计算, 说明了滚动策略设计对二维空间内滚动窗口优化的重要性.     

混堆集装箱箱区内场桥调度模型与算法

在混堆模式下的集装箱港口中,场桥（YC）调度是否合理直接影响着堆场的作业效率。考虑到混堆箱区内各任务对应的内集卡或外集卡到达时刻的不同,以及内外集卡优先级别的差异,构建了一个以所有集卡的等待成本和场桥的总移动成本最小为目标的场桥调度（YCS）模型,并设计了对应的遗传算法,给出了相应遗传算子的操作规则,通过算例的求解验证了模型与算法的有效性。     

风力与火力发电并网优化调度仿真研究

在风力和火力发电并网的优化调度节能减排的研究中,风电出力的随机性使得风电并网时达到最优调度成为难题.为解决传统方法进行优化调度仿真时,在煤耗成本上易陷入局部最优,并兼顾优化效率,提出了两层策略对风电并网的优化调度问题进行求解.外层采用加入部分贪心变异策略的量子离散粒子群算法确定机组启停,内层使用二次规划法求解负荷经济分配问题.以含风电场的10机组系统为算例,求解了机组分钟级爬坡速率约束下和不同置信度水平情况下的调度方案.计算结果表明,优化结果明显优于传统方法的求解精度和效率,为风电并网优化调度提供了新思路.     

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 cross docking operations under full,partial and no information on inbound arrivals

Cross docking is a logistic technique which seeks to reduce the inventory holding, order picking, transportation costs and delivery time. Little attention has been given to the transshipment operations inside a cross dock. In this article, we study the transshipment scheduling problem in a single receiving and a single shipping door cross dock under three scheduling policies: In the first policy, we assume to have complete information on the order of arrivals and the contents of all inbound trucks. The second and the third policies assume the availability of partial and no information on the sequence of upcoming trucks. An optimal graph based model is proposed for the full information case, and a polynomial time algorithm is given. Heuristics are developed for the other two cases. The comparison of the costs associated to each policy helps evaluating the value of information in cross dock scheduling problems.     

干扰约束下考虑分组作业面的岸桥AGV联合调度

为解决自动化码头海侧多阶段设备作业的协调问题,加快集装箱在码头内部的周转过程。考虑干扰约束下分组作业面的的岸桥自动导引小车（AGV）联合调度问题。以岸桥、AGV完工时间和AGV等待时间加权总和最小为目标,考虑岸桥实际操作中的干扰约束与AGV堵塞等待等情况,建立岸桥与AGV联合调度优化模型。提出岸桥动态调度与AGV分组作业面调度模式,设计不同规模的算例,并采用遗传算法（GA）进行求解,将计算结果与传统调度模式进行对比。结果表明,该算法能有效提高岸桥与AGV作业效率,降低AGV的等待时间与堵塞次数,为码头实际作业提供依据。     

5G超密集异构网络带内无线回传资源分配方案

为实现5G超密集异构网络中无线回传链路和接入链路之间的最优资源分配,研究多用户场景下双层异构网络的联合用户调度和功率分配问题,在队列稳定和无线回传资源有限的情况下,综合考虑用户调度、功率分配和干扰控制等因素,对带内无线回传的最优资源分配问题进行数学建模并求解,基于李雅普诺夫优化理论提出联合用户调度和功率分配的优化算法。将优化问题解耦为网络内各个用户的调度以及宏基站和小基站的功率分配过程,采用MOSEK求解器和二分类方法获得用户调度向量,利用拉格朗日乘子法求解功率分配问题,并通过队列的时刻更新过程实现最优资源分配。仿真结果表明,在多用户场景下,该方案能够有效提升网络总吞吐量以及网络效用,并且毫米波频段的通信性能优于传统蜂窝网络频段。     

Optimal user scheduling and power control in multi-user cognitive broadcast systems

Cognitive radio systems should not only have the ability to sense and exploit "frequency spectrum holes",but also the ability to sense and utilize "spatial spectrum holes".In this paper,we consider the issue of maximizing the throughput of the cognitive systems by fully utilizing "spatial spectrum holes" brought in by multiple cognitive users,in the scenario where a pair of licensed users and a cognitive broadcast system share multiple spectrum bands.By exploiting the channel reciprocity under the premise that the licensed system adopts the time-division-duplexing (TDD) mode,we propose a more practical cognitive access scheme that can sense the interference at the licensed user caused by the cognitive transmitter,based on the existing feedback signals from the licensed user to the licensed base station.Taking both interferences from the licensed base station to the cognitive receiver and from the cognitive transmitter to the licensed user into consideration,we investigate the optimal user scheduling and power allocation scheme that can maximize the ergodic sum rate of the cognitive system.We show that scheduling the user whose channel gain to interference and noise ratio (CGINR) is the largest for each frequency band is optimal.We also derive the dynamic power allocation scheme meeting the three practical constraints,i.e.,the transmitter’s average transmission power constraint,the power amplifier’s instantaneous transmission power constraint,and the interference power constraint at the licensed user.The result shows that in different coherent time intervals and different frequency bands,the power allocation has a multi-level waterfilling structure.Theoretical analysis shows that the strategy scheduling user with the largest CGINR introduces significant performance improvement compared with the traditional strategy scheduling user with the largest channel gain to noise ratio (CGNR).We also illustrate the impact of power constraints and the number of users on system performance by simulation.     

计及混合潮流约束的热-电互联综合能源系统 多目标优化调度

为满足多样化能源需求并提高能源网络的可靠性,研究多能源系统优化管理和混合潮流问题.针对多能源的网络约束及其耦合特性,构建整合分布式发电、热电联产、电力网络和区域供热网络的热-电互联综合能源系统模型.基于梯形模糊隶属函数构建模糊化软约束,量化电力网络节点电压和区域供热网络节点供给温度的技术不满意度.考虑系统的经济运行和网络节点的能源供给质量,提出一种计及混合潮流约束的热-电互联综合能源系统多目标优化调度策略,以最小化运行成本和网络节点状态变量的技术不满意度.采用epsilon约束算法精确求解该多目标优化问题的Pareto前沿.算例分析结果表明,所构建的模型和提出的算法可以有效提高系统能源供给质量和优化决策的准确性.研究成果进一步体现了所提出的多目标优化方案在兼顾经济性、能源供给质量以及复杂的运行约束,保证系统经济稳定运行等方面的效益.     

混堆模式下基于动态规则NSGA Ⅱ的自动堆垛起重机作业优化

针对外集卡到达时间的不确定性,提出自动堆垛起重机（ASC）作业序列的动态优化,从而以减少ASC作业完成时间以及ASC和外集卡等待时间为目的,提高自动化集装箱码头堆场的作业效率。首先,结合混堆模式下集装箱作业类型与外集卡动态到达的特点,提出ASC动态匹配外集卡作业任务的策略;其次,构建ASC作业时间最短与ASC和外集卡等待时间最短的多目标模型;最后,设计基于动态规则的非支配排序遗传算法Ⅱ （DRNSGA Ⅱ）作为求解算法。在小规模算例实验中,分别运用DRNSGA Ⅱ与遗传算法（GA）求解动态策略和随机策略下的ASC作业问题。实验结果表明,DRNSGA Ⅱ求解的动态策略下目标函数值优于随机策略28.2%,并且动态策略下DRNSGA Ⅱ的求解结果优于遗传算法23.3%。在大规模算例实验中,比较了DRNSGA Ⅱ与多目标粒子群优化（MOPSO）两种算法的性能。实验结果表明DRNSGA Ⅱ的求解结果优于MOPSO算法6.7%。可见DRNSGA Ⅱ能够快速生成多样化的非支配解,为混堆模式下的ASC动态作业提供决策支持。     

Travel Time Reliability in Vehicle Routing and Scheduling with Time Windows

This paper presents calibration of the Vehicle Routing and scheduling Problems with Time Windows-Probabilistic (VRPTW-P) model which takes into account the uncertainty of travel times. Probe vehicle data of travel times were obtained from usual operation of pickup-delivery trucks in South Osaka area. The optimal solution of the VRPTW-P model resulted in reducing total cost, running times and CO₂, NOx and Particle Materials (PM) emissions compared with the usual operation. This is attributed to better routing of VRPTW-P to choose more reliable roads. Therefore, VRPTW-P can contribute to establish efficient and environmentally friendly delivery systems in urban area.     

Dynamic Scheduling and Path Planning of Automated Guided Vehicles in Automatic Container Terminal

The uninterrupted operation of the quay crane (QC) ensures that the large container ship can depart port within laytime, which effectively reduces the handling cost for the container terminal and ship owners. The QC waiting caused by automated guided vehicles (AGVs) delay in the uncertain environment can be alleviated by dynamic scheduling optimization. A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems, in which the scheduling scheme determines the starting and ending nodes of paths, and the choice of paths between nodes affects the scheduling of subsequent AGVs. This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime. A dynamic optimization algorithm, including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm, is designed to solve the optimal AGV scheduling and path schemes. A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs. Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.