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。     

Comparison of operations of AGVs and ALVs in an automated container terminal

In an automated container terminal, the automated guided vehicles (AGVs) and the automated lifting vehicles (ALVs) are the most popular candidates to be used for transporting containers between the quayside and the storage yard. In this paper, we compare the operational productivities of the two types of vehicles when used in combination with the quay cranes of various performances. We assume a flexible path layout in which the vehicles can move almost freely in any vertical and horizontal directions. The traffic control scheme employed in our simulation finds a minimum- time route and schedules the travel to avoid deadlocks. Simulation experiments show that the ALVs reach the same productivity level as the AGVs using much less number of vehicles due to its self-lifting capability. However, the results also reveal that the AGVs eventually catch up the performance of the ALVs in most cases if the number of vehicles given is large enough. An exception is when the tandem double-trolley QCs are used for loading, in which case the AGVs cannot catch up the ALVs no matter how many more vehicles are added.     

面向哈佛体系结构的集装箱码头场桥作业调度

集装箱码头堆场作业调度问题一直是国内外相关研究的热点和难点,但由于码头作业的动态性、开放性、强耦合性和复杂性,堆场主要装卸设备场桥的调度配置问题一直未能有较好的解决方案。故提出面向哈佛体系结构的基于Agent建模和仿真模式,并将计算机操作系统中的磁盘臂调度算法和基于仿真的优化思想引入到上述模型中。通过构建相应的多Agent系统仿真得出敏捷高效鲁棒的场桥调度和配置解决方案,从而帮助集装箱码头提高服务水平和竞争力。     

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.      

自动化码头考虑缓冲区的设备协调调度优化

为研究自动化码头缓冲区的设置对装卸设备作业协调性的影响,针对“双小车岸桥+AGV+缓冲支架+自动化轨道吊”的装卸工艺,利用缓冲有限的柔性流水车间调度理论建立集成调度优化模型,设计了以NEH启发式算法产生初始解的遗传算法对模型进行求解,得出相应的设备调度优化方案与完工时间,并通过对比遗传算法与粒子群算法的运算结果验证了提出的模型与算法的有效性,进而分析了不同缓存区容量对完工时间以及设备使用率的影响。结果表明,设置缓冲区能有效提高不同设备之间的作业协调性,显著减少AGV的使用数量与作业完工时间。     

Quay crane scheduling in automated container terminal for the trade-off between operation efficiency and energy consumption

At present, the automation of handling equipment has changed the operation mode in the automated container terminal. This paper investigates the automated quay crane scheduling problem (AQCSP) for the automated container terminal. The operation process of AQCSP is decomposed, and formulated it as a mixed integrated programming model. In the numerical experiments, the relation between operation efficiency and energy consumption has been quantitative analyzed by case study. Moreover, the sensitivity analysis of the ratios for all tasks in a vessel bay and the tasks in each stack are also presented. The findings of this study will provide a theoretical reference for the study on the trade-off operation efficiency and energy consumption on the operational level.     

平行系统方法在自动化集装箱码头中的应用研究

平行系统是一种建立在人工社会和计算实验基础上的科学研究方法,它的特点是既能真实反映现实系统的动态过程,又能实时优化现实系统的控制过程.自动化集装箱码头是一类典型的复杂系统,既存在不计其数的作业方案,同时也有大量的约束条件.如何在最短时间和最低能源消耗的前提下,完成具有间歇和批次特征的集装箱转运任务,是涉及到数学、控制、管理和计算机等多个学科的重大课题.本文采用数据引擎作为人工社会中的基本计算单元,构成一个复杂的平行系统,用于自动化集装箱码头信息控制系统的研究.数据引擎作为一种面向图形化元件组态的计算环境,非常适用于复杂系统的建模与计算.在可视化和动态重构技术的支持下,利用380个数据引擎对一个具有8台岸桥、25辆AGV和16台龙门吊组成的港机系统进行了自动化作业过程的计算实验.研究结果表明,数据引擎技术是实现平行系统的有效方法,由多数据引擎组成的计算环境,能够大幅度降低自动化集装箱码头信息控制系统建模的复杂程度,能够将码头系统的管理和控制过程无缝地融合在一起.该平行系统可直接与港机设备对接,建立“人工码头”和“物理码头”之间的平行关系,从而实现对港机设备的最优控制.     

Operator-scheduling using a constraint satisfaction technique in port container terminals

In port container terminals, the efficient scheduling of operators of handling equipment such as container cranes, yard cranes, and yard trucks is important. Because of many complicated constraints, finding a feasible solution, as opposed to the optimal solution, within a reasonable amount of computing time can be considered satisfactory from a practical point of view. The major constraints include the following: restrictions on the minimum workforce assignment to each time slot, the maximum total operating time per operator per shift, the minimum and maximum consecutive operating times for an operator, types of equipment that can be assigned to each operator, and the available time slots for each operator or piece of equipment. The operator-scheduling problem is defined as a constraint-satisfaction problem, and its solution is obtained by utilizing a commercial software. An actual problem, collected from a container terminal in Pusan, Korea, is solved through the solution procedure proposed in this study.     

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。     

铁路集装箱中心站装卸系统排队模型及分析

针对铁路集装箱中心站装卸系统的设备性能运用闭排队网络模型进行研究。根据铁路集装箱中心站装卸系统的作业特点建立其闭排队网络模型,在该模型中内部集装箱卡车作为顾客在轨道门吊和正面吊两个服务节点以及一个虚设的“运输”服务节点接受服务。由于服务节点的服务时间不能很好地用传统分布表示,导致针对乘积形式排队网络的求解方法并不适用。于是采用近似平均值分析算法求解闭排队网络模型并获得相关的设备性能参数,并分析了装卸系统的设施配置。最终通过与仿真模型所得结果的对比,验证了模型和求解算法的正确性。     

Evaluation of the floaterm concept at marine container terminals via simulation

International seaborne trade rose significantly during the past decades. This created the need to increase capacity of existing marine container terminals to meet the growing demand. The major objective of this paper is to evaluate the floaterm concept using simulation modeling and determine if it can improve terminal productivity. The main difference between floaterm and conventional marine container terminals is that, in the former case, transshipment containers are handled by off-shore quay cranes and stored on container barges. Two terminal configurations performance is compared (vessel handling times and equipment utilization) under normal and disruptive conditions. Computational experiments confirm preliminary expectations that the floaterm concept can enhance efficiency of marine container terminal operations under normal and disruptive conditions.     

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。     

Design of digital twin applications in automated storage yard scheduling

A digital twin-enabled automated storage yard scheduling framework for uncertain port dispatching is proposed in this paper. Digital twin technology is employed to establish the virtual yet realistic storage yard and the connection between them. In the proposed framework, disturbed scenarios during practical operation are monitored, and real-time data is visualized in the virtual space to adapt to the time-varying environment. The proposed framework focuses on the optimization of three main resources, viz. storage area, automated stacking cranes (ASCs), and automated guided vehicles (AGVs). In addition, three key technologies, the Internet of Things (IoT), virtual reality, and digital thread, are adopted to develop the proposed scheduling system. A case study of ASC rescheduling due to dynamic arrival is used to demonstrate the effectiveness of the proposed framework and the significance of obtaining uncertainties in port optimization. Sensitivity analysis is conducted to define the appropriate configuration required to handle all tasks. The results show that digital twin applications in automated storage yard scheduling help operators make optimization decisions.     

集装箱码头装卸作业的调度控制模型及算法设计

对集装箱码头上装卸作业的调度控制直接影响码头的整体运营效率．本文研究了集装箱码头装卸作业的调度控制问题,提出了一个基于柔性化flow shop的集成化控制模型．该问题具有非线性规划(NP：non- polynomial)难度,因此本文开发设计了两类基于优先级规则的启发式调度算法．利用该模型来对码头中多种装卸设备进行总体调度可以提高设备之间的协调性,提高码头效率,降低成本．实验研究证明算法能有效地解决该问题．     

Online preference learning for adaptive dispatching of AGVs in an automated container terminal

This paper proposes an online preference learning algorithm named OnPL that can dynamically adapt the policy for dispatching AGVs to changing situations in an automated container terminal. The policy is based on a pairwise preference function that can be repeatedly applied to multiple candidate jobs to sort out the best one. An adaptation of the policy is therefore made by updating this preference function. After every dispatching decision, each of all the candidate jobs considered for the decision is evaluated by running a simulation of a short look-ahead horizon. The best job is then paired with each of the remaining jobs to make training examples of positive preferences, and the inversions of these pairs are each used to generate examples of negative preferences. These new training examples, together with some additional recent examples in the reserve pool, are used to relearn the preference function implemented by an artificial neural network. The experimental results show that OnPL can relearn its policy in real time, and can thus adapt to changing situations seamlessly. In comparison to OnPL, other methods cannot adapt well enough or are not applicable in real time owing to the very long computation time required.     

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.     

A simulation analysis for a transtainer-based container handling facility

The efficiency of a marine cargo terminal depends primarily upon a smooth and orderly flow of material during the container loading process. It has been observed that the transtainer operation is the bottleneck in the loading process due to the frequent container rehandles and its speed. Past attempts at improving port operations have concentrated on the computerization of the load planning function and the efficient warehouse of the outbound containers. These attempts have, to some extent, been successful but have not been able to significantly eliminate the unproductive movements of the material handling equipment during the load operation.This study proposes a methodology of utilizing a buffer space as a method to increase the utilization of the material handling equipment and reduce the total container loading time. A stimulation model using a graphics simulation system is developed to compare the proposed methodology with the current practice at the Port of Portland.The results from 96 simulation runs show that the use of a buffer space significantly improves the flow of material during the loading operation, resulting in an average of 4% reduction in the total loading time. The methodology proposed in this study can be evaluated and implemented for any transtainer-based container port operation, and is anticipated to make considerable contribution to the plannig of future container port design.     

Scheduling automated transport vehicles for material distribution systems

The rise of advanced manufacturing technology has led to enhanced, efficient material handling equipment in manufacturing and container terminal environments. In order to exploit the full potential of advanced material handling equipment in real-world industrial environments, novel scheduling approaches capable of ensuring integrated operations for multiple automated transport vehicles need to be developed. This research develops a methodology for scheduling automated transport vehicles to ensure the smooth flow of materials in production and container terminal environments. The procedure consists of a mixed-integer programming model and two meta-heuristic-based algorithms that are proposed to achieve quality schedules within a reasonable amount of time. The obtained results show a significant reduction in the earliness or lateness of material delivery tasks and an improvement in operational performance, demonstrating that the proposed approaches are capable of ensuring smooth material distribution by scheduling automated transport vehicles in an integrated manner.     

A simulation study of automated guided vehicle dispatching

This paper reports a simulation study to investigate the relative performance of five automated guided vehicle (AGV) dispatching rules. The study found that distance-based rules are most effective while AGV attribute-based rules are least effective. Both AGV speed and the number of AGVs in use are factors critically affecting the performance of a flexible manufacturing system. This paper demonstrates that computer simulation is a viable tool for assisting FMS design and evaluation.     

Immunity-based autonomous guided vehicles control

The human immune system is a self-organizing and highly distributed multi-agent system. These properties impart a high degree of robustness and performance that has created great interest in implementing engineering systems. This adopted engineering analogue is called artificial immune system (AIS). This paper presents an immunity-based control framework, which has the ability to detect changes, adapt to dynamic environment and coordinate vehicles activities for goals achievement, to deploy a fleet of autonomous guided vehicles (AGVs) for material handling in an automated warehouse. A robust and flexible automated warehousing system is achieved through the self-organized and fully decentralized origination of AGVs.