An enriched model for the integrated berth allocation and quay crane assignment problem

Given the increasing pressure to improve the efficiency of container terminals, a lot of research efforts have been devoted to optimizing container terminal operations. Most papers deal with either the berth allocation problem (BAP) or the (quay) crane assignment problem (CAP). In the literature on the BAP, handling times are often simplified to be berth dependent or proportional to vessel size, so the CAP can be ignored when scheduling vessels. This is unsatisfactory for real-life applications because the handling time primarily depends on the number of containers to be handled and the number of cranes deployed. Only a limited number of papers deals with the combination of berth allocation and crane assignment. In these papers however, authors often have resorted to algorithmic simplifications that limit the practical use of the models. This paper presents a MILP model for the integrated BAP–CAP taking into account vessel priorities, preferred berthing locations and handling time considerations. The model is used in a hybrid heuristic solution procedure that is validated on real-life data illustrating the potential to support operational and tactical decision-making.     

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.     

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 new continuous berth allocation and quay crane assignment model in container terminal

Over the past decades, Chinese ports throughput grew rapidly, and more and more concerns were shown on the operational efficiency and effectiveness. Many studies have been made for scheduling berth and quay cranes, which are the critical resources in container terminals. In this paper, a two-phase model for berth allocation and quay crane assignment is proposed. In the first phase, according to the relationships of time and space between vessels, a new continuous berth allocation model is established, in which not only the common restricts but the coverage area of quay crane are considered. Then in the quay crane assignment phase, a multi-objective programming model is proposed, in which the first objective is to minimize the range of maximum and minimum quay cranes used for resources saving, and the second one is to minimize the movements of quay cranes so as to improve the efficiency. A particle swarm optimization algorithm for BAP was developed. The results of numerical experiments show that the proposed approach can improve the essential operations in container terminal.     

An optimization approach for coupling problem of berth allocation and quay crane assignment in container terminal

This paper addresses an effective approach to solve the issue of berth allocation and quay crane assignment in a multi-user container terminal. First of all, the studied coupling problem is formulated with the interactions between berth allocation and quay crane assignment considered. Then, an evolutionary algorithm with nested loops was developed to obtain optimal solutions. The algorithm is well structured, where two inner loops are used to solve sub-problems of berth allocation and quay crane assignment respectively; an outer loop is then utilized to find an approximate solution based on the results of the two inner loops. The results of numerical experiments show that the proposed approach can improve the essential operations in container terminals.     

基于作业链的泊位与岸桥协同调度研究

针对泊位与岸桥协同调度问题,引入"链式优化"思路,用作业链的方法分析集装箱装卸作业过程,首先将泊位计划作为开始链单元,采用资源节点优化策略进行分析,以最小化船舶在港总成本为目标建立模型;然后将岸桥卸船作业作为结束链单元,采用任务节点优化策略进行分析,以最小化岸桥最大完工时间为目标建立模型.考虑到作业链的整体性能,设计嵌...     

Multi-objective hybrid genetic algorithm for quay crane dynamic assignment in berth allocation planning

The development of containers?? transportation has maintained a high momentum since 1961, especially, the containers?? traffic growth reached 10?C11% in recent years. Container terminals play an important role in the transportation chain, in order to response rapidly for the requirement of modern logistics, better resource allocation, lower cost, and higher operation efficiency are needed. In this paper, we introduce the quay crane dynamic assignment (QCDA) in berth allocation planning problem (BAP) and formulate a multi-objective mathematical model considering each berth for container ship with QCDA and number of Quay Crane??s Move. In order to solve this QCDA in BAP problem, we propose a multi-objective hybrid Genetic Algorithm approach with a priority-based encoding method. To demonstrate the effectiveness of proposed mohGA approach, numerical experiment is carried out and the best solution to the problem is obtained.     

An adaptive large neighborhood search for the discrete and continuous Berth allocation problem

The Berth Allocation Problem (BAP) consists of assigning ships to berthing positions along a quay in a port. The choice of where and when the ships should move is the main decision to be made in this problem. Considering the berthing positions, there are restrictions related to the water depth and the size of the ships among others. There are also restrictions related to the berthing time of the ships which are modeled as time windows. In this work the ships are represented as rectangles to be placed into a space ×time area, avoiding overlaps and satisfying time window constraints. We consider discrete and continuous models for the BAP and we propose an Adaptive Large Neighborhood Search heuristic to solve the problem. Computational experiments indicate that the proposed algorithm is capable of generating high-quality solutions and outperforms competing algorithms for the same problem. In most cases the improvements are statistically significant.     

Multi-objective bacterial colony optimization algorithm for integrated container terminal scheduling problem

Natural Computing - This paper proposes a multi-objective integrated container terminal scheduling problem considering three key components: berth allocation, quay cranes assignment and containers...     

基于船时效率的岸桥配置优化

在集装箱码头系统中,对船舶进行有效的岸桥配置有助于缓解岸边资源紧张的现状,提高码头的运营效率。针对连续泊位下动态到港船舶的泊位分配和岸桥配置的集成优化问题,对船舶的岸桥配置进行基于船时效率的动态调整,以最小化包括船舶延迟靠泊成本、偏离偏好泊位成本、延迟离港成本和岸桥重新配置成本在内的总成本为目标建立模型,并根据基于船时效率的岸桥配置的调整规则设计了启发式算法,结合遗传算法（GA）对问题进行求解。最终通过算例分析,验证了提出的模型和算法在解决实际港口中泊位分配和岸桥配置问题上的有效性,并通过与未考虑岸桥配置进一步调整的传统GA计算的结果进行比较,证实了提出算法的优化效果。     

Heuristics for solving continuous berth allocation problem considering periodic balancing utilization of cranes

This study investigates a berth allocation problem considering the periodic balancing utilization of quay cranes in container terminals. The proposed model considers that the quay cranes allocated to a work shift should be fully used and other real-world considerations, such as the continuous quay line, the penalties for early arrivals and departure delays. To solve the model, several heuristics are developed: the model for large problems is decomposed into sub-models that are solved by rolling-horizon heuristics; neighborhood search heuristics are used for optimizing a berthing order of vessels; parallel computing is used to improve the algorithmic performance. The method performs well when applied to real-world large-scale instances with promising computation time that is linearly related to the number of vessels.     

桥吊可动态分配的连续泊位分配问题算法

研究在允许桥吊动态分配的情况下集装箱码头的连续泊位动态分配问题,并建立以船舶在港时间最小为目标的动态泊位分配模型;然后基于兄弟-儿子方法对船舶的位置进行调整以规划桥吊使桥吊不交叉。在相同算例下,比现有方法得到的船舶在港时间更少,从而验证了模型及算法的有效性。     

改进Memetic算法求解集装箱码头泊位岸桥调度问题

针对集装箱码头泊位岸桥调度这一NP难题,提出了一种改进的Memetic算法。算法中采用三层染色体结构表示个体,通过改进顺序交叉算子和基于领域搜索的变异算子以避免个体超出可行域,在交叉和变异后采用改进的模拟退火策略进行局部搜索。试验算例表明该算法收敛速度较快,且能获得较好的满意解。     

基于蚁群算法的泊位调度问题

在集装箱港口的运作中,泊位调试系统是制约集装箱港口降低船舶在港时间和运营成本的主要瓶颈之一。泊位调度的目标就是确定集装箱港口船舶的停靠泊位和停泊时间。将码头看成离散泊位的集合,以船舶的在港时间最短为目标,应用蚁群算法对该问题进行优经研究,在满足各种约束条件的基础上,充分的利用好码头资源。利用蚁群算法的正反馈和并行搜索特点提高解的质量2和稳定性,通过对某集装箱码头的案例分析,说明该算法的有效性和实用性。     

潮汐影响下泊位和岸桥双目标联合调度仿真

针对集装箱船舶大型化导致的港口航道现有水深无法满足大型船舶安全吃水深度,需要借助潮水上涨进出航道的现状,研究了潮汐影响下连续型泊位和动态岸桥联合调度问题。建立了以最小化船舶周转时间和岸桥在船舶间移动次数的双目标混合整数规划模型。基于问题特点,设计了Epsilon约束精确算法和带精英策略的快速非支配排序遗传算法(NSGA-Ⅱ)分别求解小规模和大规模算例的Pareto最优解集,所得结果验证了模型和算法的正确性与有效性。通过潮汐周期灵敏度分析评估了潮汐周期长度对岸桥工作效率和港口服务质量的影响。仿真结果表明,建立的优化模型能够帮助港口企业有效降低潮汐对生产作业的影响,同时提供一组高效的Pareto最优泊位岸桥调度方案提高工作效率和经济效益。     

不确定环境下集装箱码头泊位与岸桥联合调度

在集装箱码头操作系统中,有效的泊位岸桥调度计划有助于提高码头的运营效率和客户满意度。针对船舶到港时间和装卸作业时间随机的泊位岸桥联合调度问题,综合考虑了连续泊位下船舶偏离偏好泊位产生的惩罚时间,并通过添加延缓时间的方法来吸收不确定性因素带来的影响。为了体现调度计划的鲁棒性,将延缓时间添加在目标函数中,建立了以船舶在港总时间、偏离偏好泊位的惩罚时间、客户满意度和延缓时间之和最小化为目标的混合整数规划模型,提出一种自改变遗传算法和启发式靠泊相结合的改进遗传算法对模型进行求解;通过算例分析,证明了提出的改进遗传算法在计算不确定环境下的泊位岸桥联合调度问题的有效性。     

Biased random key genetic algorithm for the Tactical Berth Allocation Problem

The Tactical Berth Allocation Problem (TBAP) aims to allocate incoming ships to berthing positions and assign quay crane profiles to them (i.e. number of quay cranes per time step). The goals of the TBAP are both the minimization of the housekeeping costs derived from the transshipment container flows between ships, and the maximization of the total value of the quay crane profiles assigned to the ships. In order to obtain good quality solutions with considerably short computational effort, this paper proposes a biased random key genetic algorithm for solving this problem. The computational experiments and the comparison with other solutions approaches presented in the related literature for tackling the TBAP show that the proposed algorithm is applicable to efficiently solve this difficult and essential container terminal problem. The problem instances used in this paper are composed of both, those reported in the literature and a new benchmark suite proposed in this work for taking into consideration other realistic scenarios.     

Coordinated optimized scheduling of locks and transshipment in inland waterway transportation using binary NSGA‐II

Traffic congestion in inland waterways caused by insufficient throughput capacity of locks has become a compelling problem in developed inland shipping countries. In order to avoid excessive time wasted in waiting for lock service, it is suggested that some types of cargoes should be unloaded at the quays and transported by road/train to their destinations, which is called water–land transshipment. By this means, the ships are divided into two groups that either pass the lock or are transshipped at the quays, engendering the lock and water–land transshipment co‐scheduling (LWTC) problem. This paper focuses on the LWTC, where the roll‐on roll‐off ships, passenger ships, and general cargo ships that can be transshipped and other ships that can only pass the lock are considered in a lock‐quay co‐scheduling system. The LWTC problem is decomposed into an outer‐layer main 0‐1 optimization problem and two inner‐layer subproblems: lock scheduling and berth allocation. A multiobjective optimization model is proposed for the LWTC problem based on its two‐layer structure. To solve the LWTC problem, a hybrid heuristic method is proposed, where a modified binary nondominated sorting genetic algorithm II is proposed to solve the main problem, and the two subproblems are solved by specific heuristics. The proposed model and hybrid method are tested on instances extracted from historical data of traffic at Three Gorges Dam, the results of which demonstrate the feasibility of the model and the superiority of the proposed hybrid heuristic method over other comparisons.     

Integrated intelligent techniques for remarshaling and berthing in maritime terminals

Maritime container terminals are facilities where cargo containers are transshipped between ships or between ships and land vehicles (trucks or trains). These terminals involve a large number of complex and combinatorial problems. Two important problems are the container stacking problem and the berth allocation problem. Both problems are generally managed and solved independently but there exist a relationship that must be taken into account to optimize the whole process. The terminal operator normally demands all containers bound for an incoming vessel to be ready in the terminal before its arrival. Similarly, customers (i.e., vessel owners) expect prompt berthing of their vessels upon arrival. This is particularly important for vessels from priority customers who may have been guaranteed berth-on-arrival service in their contract with the terminal operator. To this end, both problems must be interrelated.In this paper, a set of artificial intelligence based-techniques for solving both problems is presented. We develop a planning technique for solving the container stacking problem and a set of optimized allocation algorithms for solving the berth allocation problem independently. Finally we have developed an architecture to solve both problems in an integrated way. Thus, an algorithm for solving the berth allocation problem generates an optimized order of vessels to be served meanwhile our container stacking problem heuristics calculate the minimum number of reshuffles needed to allocate the containers in the appropriate place for the obtained ordering of vessels. Thus combined optimal solutions can be calculated and the terminal operator could decide which solution is more appropriate in each case. These techniques will minimize disruptions and facilitate planning in container terminals.     

考虑泊位偏好和岸桥移动的泊位岸桥联合调度

为了更高效地利用码头资源,同时考虑泊位资源和岸桥资源,建立了考虑泊位偏好和岸桥移动频数的泊位岸桥联合调度两阶段模型.第一阶段模型采用船舶到港时间可变的到港策略,建立了以船舶等待成本、泊位偏离成本、延迟离港成本之和最小为目标的混合整数规划模型.第二阶段模型考虑了岸桥的干扰约束,建立了以岸桥移动频数最小为目标的整数规划模型...