Two-stage stochastic programming model for generating container yard template under uncertainty and traffic congestion

Yard template is a space assignment at the tactical level, which is kept unchanged within a long period of time and significantly impacts the handling efficiency of a container terminal. This paper addresses a yard template planning problem considering uncertainty and traffic congestion. A two-stage stochastic programming model is formulated for minimizing the risk of containers with no available slots in the designated yard area and minimizing total transportation distances. The first-stage model is formulated for assigning vessels in each block without considering the physical location properties of blocks, and the second-stage model is formulated for designating physical locations to all blocks. Subsequently, a solving framework based on genetic algorithm is proposed for solving the first-stage model, and the CPLEX (a commercial solver) is used for solving the second-stage model. Finally, numerical experiments and scenario analysis are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution approach.     

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.     

Storage space allocation at marine container terminals using ant-based control

This paper presents a novel approach for allocating containers to storage blocks in a marine container terminal. We model the container terminal as a network of gates, yard blocks and berths on which export and import containers are considered as bi-directional traffic. For both export and import containers, the yard blocks are the intermediate storage points between gates (landside) and berths (waterside). Our model determines the route for each individual container (i.e. assign the container to a block to be stored) based on two competing objectives: (1) balance the workload among yard blocks, and (2) minimize the distance traveled by internal trucks between yard blocks and berths. The model utilizes an ant-based control method. It exploits the trail laying behavior of ant colonies where ants deposit pheromones as a function of traveled distance and congestion at the blocks. The route of a container (i.e. selection of a yard block) is based on the pheromone distribution on the network. The results from experiments show that the proposed approach is effective in balancing the workload among yard blocks and reducing the distance traveled by internal transport vehicles during vessel loading and unloading operations.     

Storage yard management based on flexible yard template in container terminal

With the bottleneck of port operation moving from the quay side to the yard area, storage yard management is becoming increasingly important in the container terminal. This paper studies on storage yard management in container terminal, a flexible yard template strategy is proposed instead of the fixed yard template strategy. Based on the strategy, an integrated optimization model simultaneously considering space allocation and yard crane deployment for the tactical storage yard management is formulated. Besides, Numerical experiments are conduced to verify the effectiveness of the proposed strategy and mathematical model.     

Choice of loading clusters in container terminals

The storage allocation in the export yard of a container terminal determines the efficiency of container loading. Even if the yard manager has optimised the allocation of export containers to avoid rehandling, conflicts among loading quay cranes can still occur. Thus, all the possible handlings during loading must be considered when organising the yard space. In previous studies, the yard storage allocation has been assessed based on the subblock, which consists of several adjacent bays. However, to minimise all possible handlings in the loading process at the terminal, optimising more flexible storage clusters is also important. Thus, our aim in this research is to model the choice of loading clusters and derive a more flexible allocation strategy for organising the space in the export yard. A bi-objective model is built, which considers both the transportation distance and handling balance between blocks. A model aimed at minimising all possible handlings in the export yard for the loading process is also developed, and several of the insights derived can inform yard management in real-life operations. It is proven that the handling requirements have a significant effect on the choice of loading clusters, and yard managers should consider the various features of liner and loading processes when organising their storage space.     

Scheduling of collaborative operations of yard cranes and yard trucks for export containers using hybrid approaches

Optimizing collaborative operations for yard cranes (YCs) and yard trucks (YTs) is vital to the overall performance of a container terminal. This research investigates four different hybrid approaches developed for dealing with yard crane scheduling problem (YCSP) and yard truck scheduling problem (YTSP) simultaneously for export containers in the yard side area of a container terminal. First, these approaches use a load-balancing heuristic to assign containers to YCs evenly. Following this, each of them employs a specific heuristic/metaheuristic, such as genetic algorithm (GA), particle swarm optimization (PSO) or subgroups PSO (SGPSO), to generate alternative container loading sequences for each YC. Finally, a simulation model is used to simulate loading and transporting of these export containers, evaluate alternative planning results, and finally output the best planning result. Experiments have been conducted to compare these hybrid approaches. The results show Hybrid4 (SGPSO) outperforms Hybrid1 (Sort-by-bay), Hybrid2 (GA), and Hybrid3 (PSO) in terms of makespan.     

多场桥分区域平衡策划下的集装箱堆场箱位分配问题

集装箱码头堆场出口箱箱位分配和场桥调度对码头运营效率有重要影响. 为了合理分配箱位和调度场桥, 采用分区域平衡策划方法, 在给定批量任务下, 考虑场桥实际作业中的安全距离, 以均衡各场桥作业任务量和减少场 桥的非装卸时间为目标, 建立混合整数规划模型, 并设计遗传算法求解, 通过不同批量任务的实验分析验证所提出方法的有效性. 研究表明, 分区域平衡策划方法可以更好地解决箱位分配和箱区多场桥联合作业的优化问题.

     

基于动态平衡策划下的集装箱堆场箱位分配方案研究

针对集装箱在码头堆存时存在的箱位分配问题,在研究原有多场桥同时作业方案的基础上,提出了多场桥分区域动态平衡策划方案,弥补了原方案中因保证场桥间的安全距离而带来的缺陷。以合理利用堆存空间,提高场桥作业效率为目标,建立数学模型,结合遗传算法对所提方案进行验证。结果表明,分区域动态平衡策划方案能有效减少场桥的无效作业时间,提高场桥的工作效率,并能充分地利用堆场贝位,更好地利用堆场空间。     

动态环境下集装箱码头堆场出口箱箱位分配建模与算法研究

箱位分配是集装箱码头运作优化管理的关键问题.针对出口箱交箱时间的动态不确定性,提出两级调度策略,将出口箱位分配问题分解为贝位分配和贝内具体箱位分配,分别建立规划模型优化龙门吊大车行使距离和翻箱量,并针对两级调度策略特点分别设计开发基于禁忌搜索的求解算法.模拟实验表明,所提出的方法能够适应动态条件变化,改善调度效果,并在合理时间内获得满意解.     

Genetic mechanism-based coupling algorithm for solving coordinated scheduling problems of yard systems in container terminals

This study develops models and methods utilized for solving the coordination scheduling problem in the yard of a container terminal. Based on the information shared by the yard storage subsystem and the YC scheduling subsystem, and the interaction between these subsystems, a coordination scheduling model, which is composed of a storage subsystem model, a YC scheduling subsystem and a coordinate controller model, is developed. A coupling algorithm, which is based on a genetic mechanism, is developed to solve the coordination scheduling problem. The algorithm adopts the genetic selection, crossover and mutation operations to adjust the yard storage plan and the YC scheduling plan. The performance of the coordination scheduling model and that of the proposed coupling algorithm are confirmed with reference to a numerical example.     

Routing straddle carriers for the loading operation of containers using a beam search algorithm

This paper discusses how to route straddle carriers during the loading operation of export containers in port container terminals. The objective of the routing is to minimize the total travel distance of straddle carriers in the yard. The routing problem is comprised of the container allocation problem and the carrier routing problem. In the container allocation problem, containers in the yard are divided into multiple classes, each of which will be loaded by a quay crane. The container allocation problem is formulated as a transportation problem. In the carrier routing problem, the sequence of yard-bays that a carrier visits is determined. A beam search algorithm is developed for the carrier routing problem. A numerical experimentation is carried out in order to evaluate the performance of the algorithm.     

Mathematical modeling of yard template regeneration for multiple container terminals

As a tactical-level plan, the yard template determines the space allocation in a container terminal yard for all the arriving shipping liners. Generally speaking, the yard template will not change and it may last a matter of years. However, it has to be changed to response to the alteration of shipping liner. This paper investigates the problem of yard template regeneration for container port, which contains multiple container terminals. Firstly, the concept of yard template regeneration as well as the main influencing factors is proposed. Secondly, a multiple-objective mixed integer programming model is formulated which fully considering the minimum transportation cost, minimum template disturbance and maximum space utilization. Moreover, a case study is conducted to intuitively illustrate the regeneration problem of yard template. Finally, performance analysis and sensitivity analysis are performed to validate the effectiveness of the model.     

海铁联运港口混合作业模式下轨道吊与集卡协同调度

在集装箱海铁联运港口中,铁路作业区作为连接铁路运输和水路运输的重要节点,其装卸效率将影响集装箱海铁联运的整体效率。首先,对比分析了“船舶-列车”作业模式和“船舶-堆场-列车”作业模式的特点,并结合海铁联运港口实际作业情况提出了混合作业模式。然后,以轨道吊完工时间最短为目标构建混合整数规划模型,既考虑了班列和船舶的作业时间窗约束,又考虑了轨道吊间干扰和安全距离、轨道吊和集卡接续作业和等待时间等现实约束。针对遗传算法在局部搜索能力方面的不足,将启发式规则与遗传算法相结合设计了求解轨道吊与集卡协同调度问题的混合遗传算法（HGA）,并进行了数值实验。实验结果验证了所提模型和混合算法的有效性。最后通过设计实验分析集装箱数量、岸边箱占比、轨道吊数量和集卡数量对轨道吊完工时间和集卡完工时间的影响,发现同等集装箱数量下岸边箱占比提高时,应通过增加轨道吊数量来有效缩短完工时间。     

A genetic algorithm to solve the storage space allocation problem in a container terminal

In this paper, an efficient genetic algorithm (GA) is presented to solve an extended storage space allocation problem (SSAP) in a container terminal. The SSAP is defined as the temporary allocation of the inbound/outbound containers to the storage blocks at each time period with aim of balancing the workload between blocks in order to minimize the storage/retrieval times of containers. An extended version of a SSAP proposed in the literature is considered in this paper in which the type of container affects on making the decision on the allocation of containers to the blocks. In real-world cases, there are different types (as well as different sizes) of containers consisting of several different goods such as regular, empty and refrigerated containers. The extended SSAP is solved by an efficient GA for real-sized instances. Because of existing the several equality constraints in the extended model, the implementation of the GA in order to quick and facilitate achieve to the feasible solutions is one of the outstanding advantages of this paper. The performance of the extended model and proposed GA is verified by a number of numerical examples.     

Workload-based yard-planning system in container terminals

Traditionally, the yard-planning problem has been considered to be the assignment of yard spaces to arriving vessels in practices of container terminals. This study proposes an integrated decision-making framework for the yard-planning that simultaneously considers various resources such as storage space, yard cranes, and traffic area in container terminals for planning. The decision-making framework in this study is based on a mathematical model, which supports intelligent yard-planning activities considering work-loads on various related resources. Further, it is shown that the decision-making framework for the yard-planning can be extended to that for simultaneous decisions on yard plans and handling capacity of yard cranes per block. A heuristic algorithm is also proposed in order to reduce the computational time for planning. Numerical experiments are conducted to validate the models and algorithms proposed in this study.     

An optimization model for the container pre-marshalling problem

In most container yards around the world, containers are stacked high to utilize yard space more efficiently. In these yards, one major factor that affects their operational efficiency is the need to re-shuffle containers when accessing a container that is buried beneath other containers. One way to achieve higher loading efficiency is to pre-marshal the containers in such a way that it fits the loading sequence. In this research, we present a mathematical model for the container pre-marshalling problem. With respect to a given yard layout and a given sequence that containers are loaded onto a ship, the model yields a plan to re-position the export containers within the yard, so that no extra re-handles will be needed during the loading operation. The optimization goal is to minimize the number of container movements during pre-marshalling. The resulting model is an integer programming model composed of a multi-commodity flow problem and a set of side constraints. Several possible variations of the model as well as a solution heuristic are also discussed. Computation results are provided.     

基于网络模型的集装箱预翻箱问题研究

集装箱翻箱问题涉及集装箱装卸工作的顺畅与否。以此为研究对象,首先建立了基于网络模型的集装箱翻箱模型,并用算例进行了计算试验。在此基础上,对约束进行了改正,建立了改正的集装箱预翻箱模型,并进行了计算验证,证明了其在计算时间上的压缩。根据实际的单贝集装箱堆存情况,进行了实际堆存状态翻箱的计算试验,取得了阶段性成果。对单贝集装箱的堆存数与翻箱次数的关系进行了研究,发现在堆存数达到17时有较好的堆场利用率和较少的翻箱次数。     

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.     

考虑时间窗的集装箱港口场桥全局调度优化

合理高效的场桥调度计划有助于减少场桥与集卡相互等待的时间从而提高港口的运营效率,考虑到在实际操作中会出现多箱区多场桥同时工作、互相冲突等情况,建立了以场桥移动成本和延误成本最小化为目标的数学模型,利用计划时间段和时间窗的概念对场桥作业进行约束,通过遗传算法编码进行求解,并将计算结果与实际操作及其他算法的优化结果相对比,进而验证该模型和算法的有效性和稳定性。     

基于突破性局部搜索的集装箱班列同步转运作业调度优化

铁路集装箱中心站作为内陆腹地运输网络中的重要节点.转运作为主要的站内作业活动,对其开展调度研究,能够有效缩短不同运输方式之间的转运周期,从而保证铁路集装箱多式联运的整体运作效率.为了快速制定堆场作业计划,现有铁路集装箱站场转运作业均要求班列间的集装箱转运须经堆场方可实现.在该模式下,必然发生两次装卸和一次暂存作业.为了避免暂存中转作业,采用集装箱班列同步转运作业模式构建了以同步转运集装箱箱数最大化为目标的数学规划模型,提出突破性局部搜索框架实现对大规模问题的求解.最后通过算例分析验证了所提出的启发式算法效率.