基于滚动窗策略的港口海铁联运集装箱转运优化

为了解决港口海铁联运转运集装箱作业规模过大的问题,采用滚动窗策略方法研究港口船舶与列车之间转运进口集装箱作业问题,在每个窗口内建立以列车在港停留时间和集装箱在堆场的堆存时间总时间最小为目标的整数规划模型,设计双层遗传算法进行求解。在此基础上,分别讨论了以固定任务数量和固定时间长度为滚动窗口的情况,对比发现以固定时间长度为窗口的滚动窗策略更适用,并将其与已有的调度策略研究成果作比较分析。最后,设置实验比较双层遗传算法和单层遗传算法,并对设备的工作能力进行灵敏度分析。结果表明,滚动调度策略可以灵活解决大规模集装箱转运问题,双层遗传算法的解优于单层遗传算法的解,增加装卸线数和轨道起重机工作能力可以提高集装箱转运效率。     

考虑装卸顺序的岸桥与集卡协调调度问题研究*

为解决集装箱港口岸桥和集卡资源紧张的现状,减少集装箱处理时间,针对岸桥和集卡协调调度问题,在只有进口箱的条件下,综合考虑岸桥干涉和集装箱优先级等约束,建立一个以最小化最大完工时间为目标的混合整数线性规划模型,并使用遗传算法(GA)求解该模型。其次对不同规模的问题分别使用遗传算法(GA)和粒子群算法(PSO)求解并比较。实验结果表明,对于该问题模型遗传算法(GA)算法优于粒子群算法(PSO)算法,遗传算法是有效的。     

铁路集装箱物流中心系统建模与仿真分析

铁路集装箱物流中心作业系统是复杂的随机系统,难以用数学模型准确描述。采用系统仿真的方法,依据集装箱物流中心的作业流程,构建基于eM-Plant的铁路集装箱物流中心整体作业模型。通过仿真实验,研究到站集装箱列车所载箱箱型比例、外卡到站时间间隔和内卡数量等模型参数对集装箱物流中心作业性能的影响。仿真结果表明集装箱列车所载箱箱型比例为6∶1∶3时装卸设备利用率最高,外卡在到站时间间隔超过8 min时无需等待,内卡数为5~7时辅助中转箱转运作业时间最少。     

铁路集装箱中心站内部集卡调度和箱位配置

针对铁路集装箱中心站集装箱专列卸箱作业的效率问题,将内部集卡的任务调度和集装箱箱位分配进行集成,建立集装箱列车卸车作业的多目标优化模型,包括内卡在轨道门吊和辅助箱场等待时间,以及走行时间对作业效率的影响。采用混合遗传算法进行求解。将模拟结果与实际操作数据作比较,并对20次计算模拟进行平均时间和方差分析,验证了该方法的有效性。     

铁路集装箱中心站资源分配与作业调度联合优化

铁路集装箱中心站主作业区资源分配与作业调度联合优化对其经营效益和运作效率有重要影响.基于“轨道吊-集卡”协同装卸方案,引入轨道吊动态配置原则,以最大化作业均衡率和最小化作业成本为目标,构建多目标非线性混合整数规划模型,综合研究作业区域动态划分、贝位分配以及多轨道吊调度多层次联合优化问题. 根据问题特点,融合启发式规则、遗传算法和模拟退火算法,设计3层混合启发式算法求解模型.通过不同规模算例,对比Cplex与所设计算法的实验结果,验证模型的正确性和算法的有效性,并借助于不同划分原则、不同优化策略与不同间隔约束下的对比实验,验证优化模型与算法普适性.结果表明,新型装卸方案、动态作业区域以及联合优化策略可大幅度均衡轨道吊作业量,避免资源过度负载、降低中心站作业成本,为中心站的实际运营管理提供决策支持.     

龙门吊与集卡协同调度问题研究

在港口码头作业中,龙门吊的作业与集卡的作业之间存在着非常紧密的联系,如何更好地同步龙门吊与集卡之间的作业,对减少集装箱船的在港时间非常有效。同时考虑了龙门吊与集卡的协同调度,很好地考虑了龙门吊与集卡之间的同步。在此基础之上,首次考虑了实际作业中存在的一些约束,如多台龙门吊在同一个箱区作业时由于共享一个双向轨道而存在的龙门吊间不能相互跨越的约束。另外还有龙门吊间的安全距离约束,工作优先约束等。构建了一个数学模型来描述问题,目标是使得所有作业总完工时间最短。由于问题计算的复杂性,引进了一种多层遗传算法来求解模型,最后算例验证了算法的有效性。     

考虑箱区作业均衡的ACT船舶配载鲁棒优化研究

基于自动化集装箱码头堆场与传统堆场在布局与机械配置方面的区别，将箱区作业均衡因素引入船舶配载问题中，以最小化装船作业时间和箱区作业均衡为目标建立考虑箱区作业均衡的自动化集装箱码头船舶配载混合整数规划模型；同时引入解的保守性参数，将确定性模型转化为考虑桥吊作业时间不确定性的自动化集装箱码头船舶配载鲁棒优化模型，再设计基于编号与排序的禁忌搜索算法对算例进行求解。结果证明了模型的正确性与算法的有效性，且各案例在不考虑箱区作业均衡因素时得到的箱区作业不均衡箱量比考虑该因素时得到的箱区作业不均衡箱量平均高出17.8%，不确定性情况下，“集中配载”现象更加显著。     

自动化码头堆场穿越式双ASC调度

随着人工智能等科技的发展,海铁联运的运输模式加快了集装箱码头自动化的发展进程,自动化已经成为集装箱码头发展的必然趋势。对自动化集装箱码头堆场内可穿越式双自动堆垛起重机（ASC）在同一箱区内进行作业的过程进行研究,考虑不同大小的ASC的运行速度和单位时间内耗能不一样,以及在同一贝位两个ASC存在冲突的情况,建立多目标混合整数模型,得出完成所有集装箱任务的总时间和总耗能。并通过改变不同的参数来设计三组实验,实验结果表明,可以通过调整集装箱任务量、双ASC的速度、双ASC装载和空载的耗能之比来减少码头作业集装箱任务的总时间和总能耗。     

考虑作业状态能耗的集装箱码头设备协调调度

绿色港口日渐成为港口发展的必然趋势,为了提高集装箱码头的服务水平及降低其能耗,综合分析了集装箱码头的装卸作业流程,考虑岸桥、场桥、集卡在不同作业状态下的能耗,且以总完工时间和总作业能耗最小为目标建立了多目标混合整数规划模型。使用MATLAB编码改进自适应遗传算法求解所建模型,并分别与CPLEX和原始遗传算法的求解结果作对比,证明了该算法的优秀性。更改能耗目标和作业时间目标所占权重进行求解,发现考虑各设备在不同作业状态下的能耗会影响总完工时间,且能耗与作业时间是相互冲突的目标,追求低能耗会造成作业效率的牺牲。分析结果表明,所建模型和算法在岸桥、场桥和集卡的协调调度问题中可以帮助决策者更好地权衡作业时间和能耗目标。     

以自动化码头为中心的船舶配载优化

针对自动化码头船舶配载效率较低的问题,为了提高设备资源利用率,提出了一种基于船舶配载特点设计的固定集搜索（FSS）算法。首先,在考虑一般船舶配载原则的基础上,以桥吊作业计划为依据,引入箱区作业均衡因素,将最小化箱区翻箱量、总装船时间以及尽可能的箱区作业均衡作为目标,建立自动化码头船舶配载混合整数规划模型;其次,通过固定较优解中多次出现的元素来寻求最优解。实验结果表明,不同规模的实例下,FSS算法相较于Cplex,翻箱量和不均衡箱数分别平均减少了22.3%和11.7%,目标函数值平均优化了6.5%;所提固定集搜索算法与粒子群优化（PSO）算法、遗传算法（GA）和蚁群优化（PSO）算法相比,目标函数值平均优化了2.1%,凸显了FSS算法更高的配载效率。而为了增加案例的多样性,对箱区堆栈分布和比例进行了调整。在这个情况下,FSS算法的不均衡箱数相较上述三种算法平均减少了19.3%,设备资源利用率更高。     

铁路集装箱中心站装卸设备优化调度研究

为了提高铁路集装箱中心站的作业效率,研究了带有干涉约束的轨道式门式起重机（轨道吊）调度问题,为其建立了混合整数规划模型。分析了轨道吊调度问题的作业特点,提出了一种求解该问题的改进遗传算法。在以最小化最大装卸作业时间和均衡设备负荷为目标的情况下,设计了合理的交叉算子和对局部最优个体操作的变异算子。使用随机算例对算法进行测试,结果表明：该算法能在极短的时间内找到此类调度问题的最优或近似最优解。     

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.     

A multi-objective genetic algorithm for yard crane scheduling problem with multiple work lines

Due to increasing ships and quay cranes, container terminals operations become more and more busy. The traditional handling based on work line is converted into pool strategy, namely loading and unloading containers with multiple work lines are operating simultaneously. In the paper we discuss the yard crane scheduling problem with multiple work lines in container terminals. We develop a multi-objective 0-1 integer programming model considering the minimum total completion time of all yard cranes and the maximization balanced distribution of the completion time at the same time. With the application of adaptive weight GA approach, the problem can be solved by a multi-objective hybrid genetic algorithm and the Pareto solutions can be finally got. Using the compromised approach, the nearest feasible solution to ideal solution is chosen to be the best compromised Pareto optimal solution of the multi-objective model. The numerical example proves the applicability and effectiveness of the proposed method to the multi-objective yard crane scheduling problem.     

自动化码头双小车岸桥与AGV协调调度问题研究

为研究自动化集装箱码头中自动导引运输车（Automated Guided Vehicle，AGV）与双小车岸桥（Double-Trolley Quay Crane，QC）的协调调度问题，考虑双小车岸桥中转平台及其容量限制，并以双小车岸桥门架小车时间窗为约束，建立以集装箱任务最大完工时间最小化为目标的混合整数规划模型。设计启发式算法，由中转平台的容量求得岸桥门架小车操作集装箱任务的时间窗，并采用遗传算法进行求解，给出相应的AGV调度优化方案，解决两大设备的协调调度问题。最后，以10组实验为例，比较了遗传算法与粒子群算法的优化结果。结果表明两种算法一致，且基于遗传算法的模型求解收敛速度更快，从而验证了该算法的可行性。     

A queuing network model for the management of berth crane operations

This paper focuses on the optimal management of container discharge/loading at any given berthing point, within a real maritime terminal. Productivity maximization of expensive resources, as rail-mounted berth cranes, should be matched with the vessel requirement of minimizing waiting times with an adequate rate of service completion. To this practical problem, a queuing network model is proposed. Due to its complexity, discrete-event simulation appears as the most appropriate approach to model solution. To get a systematic representation of real constraints and policies of resource allocation and activity scheduling, an event graph (EG)-based methodology has been exploited in simulator design. Alternative policies issued by the operation manager can be inserted in a suitable panel-like view of the queuing network model and then compared by means of simulation, to evaluate the average measures for all berth cranes, such as throughput and completion time. Numerical experiments for simulator validation against real data are encouraging. Some decisions on both straddle carrier assignment to berth cranes and hold assignment and sequencing upon the same crane could be improved by the proposed manager-friendly simulation tool.     

An optimization methodology for intermodal terminal management

A solution to the problems of resource allocation and scheduling of loading and unloading operations in a container terminal is presented. The two problems are formulated and solved hierarchically. First, the solution of the resource allocation problem returns, over a number of work shifts, a set of quay cranes used to load and unload containers from the moored ships and the set of yard cranes to store those containers on the yard. Then, a scheduling problem is formulated to compute the loading and unloading lists of containers for each allocated crane. The feasibility of the solution is verified against a detailed, discrete-event based, simulation model of the terminal. The simulation results show that the optimized resource allocation, which reduces the costs by [frac13], can be effectively adopted in combination with the optimized loading and unloading list. Moreover, the simulation shows that the optimized lists reduce the number of crane conflicts on the yard and the average length of the truck queues in the terminal.     

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。     

Dynamic rolling strategy for multi-vessel quay crane scheduling

This paper focuses on the container loading and unloading problem with dynamic ship arrival times. Using a determined berth plan, in combination with the reality of a container terminal production scheduling environment, this paper proposes a scheduling method for quay cranes that can be used for multiple vessels in a container terminal, based on a dynamic rolling-horizon strategy. The goal of this method is to minimize the operation time of all ships at port and obtain operation equilibrium of quay cranes by establishing a mathematical model and using a genetic algorithm to solve the model. Numerical simulations are applied to calculate the optimal loading and unloading order and the completion time of container tasks on a ship. By comparing this result with the traditional method of quay crane loading and unloading, the paper verifies that the quay crane scheduling method for multiple vessels based on a dynamic rolling-horizon strategy can provide a positive contribution to improve the efficiency of container terminal quay crane loading and unloading and reduce resource wastage.     

A quay crane dynamic scheduling problem by hybrid evolutionary algorithm for berth allocation planning

A considerable growth in worldwide container transportation needs essential optimization of terminal operations. An operation schedule for berth and quay cranes can significantly affect turnaround time of ships, which is an important objective of all schedules in a port. This paper addresses the problem of determining the berthing position and time of each ship as well as the number of quay cranes assigned to each ship. The objective of the problem is to minimize the sum of the handling time, waiting time and the delay time for every ship. We introduce a formulation for the simultaneous berth and quay crane scheduling problem. Next, we combine genetic algorithm with heuristic to find an approximate solution for the problem. Computational experiments show that the proposed approaches are applicable to solve this difficult but essential terminal operation problem.