基于改进遗传算法的泊位分配问题

泊位是船舶进出港调度中的重要组成部分。集装箱港口的泊位分配问题（BAP）是为到达集装箱港口的船舶安排最佳停靠位置和时间。考虑船舶动态到达的情况以及码头装卸效率对船舶在港作业时间的影响。以船舶的总在港时间最短为目标,包括船舶的等待时间和作业时间,建立了连续型泊位分配问题的求解策略。根据“先来先服务”的原则,构建了混合整数规划模型,并采用改进的遗传算法对其进行求解。经过算例结果的验证,该模型和算法被证明具有正确性和有效性,可以找到更符合实际情况的泊位分配策略。     

基于蚁群算法的泊位调度优化与仿真

在全球贸易经济聚焦在中国的同时,港口的吞吐能力成为目前港口业的主要矛盾。提高泊位这个环节的运作能力,减少船舶在港时间,增加港口的吞吐能力成为主要研究对象。本文采取仿真模型与优化算法相结合的研究方法,把泊位调度问题转化为旅行商问题,建立了一个泊位岸桥协调调度,通过蚁群算法建立数学模型,使船舶在港时间最短为目标建立函数,求得最佳调度方案。用ProModel建立船舶到港停泊及离港仿真模型。验证泊位调度优化的有效性,以便指导港口实际的泊位调度。     

集装箱港口集群下多港口多泊位联合调度方法

目前对港口和泊位的调度研究尚停留在单港口多泊位,而在集装箱港口集群条件下对多港口多泊位实行船舶的联合调度可以充分实现港口资源的优化配置。为了充分利用港口资源,实现船舶在港时间最短,且服从船公司运输成本最低的目的,建立了集装箱港口集群下多港口多泊位联合调度的多目标非线性决策模型,并按照模型决策空间所具有的特殊条件,设计了改进的遗传启发式算法,结果表明船舶靠泊成本大幅降低,港口利用率大幅提高。通过大量真实和随机算例验证了算法的有效性和稳定性,证明了模型和算法实用有效。     

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。     

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。     

桥吊迁移的港口连续泊位分配问题在线算法

集装箱码头资源的高效利用已被研究多年,而多数是在预知所有船舶作业的相关信息(到港时间、船舶尺寸等)的离线情况下建模与计算.现实中,却因一些突发因素(如恶劣天气、设备故障等)使预知信息不可靠,以至原调度方案不可行,从而降低港口作业效率及资源浪费.故在桥吊可迁移的连续泊位分配模式下,首次结合在线算法思想,提出泊位与桥吊调度的模型,并设计相应的在线调度算法.利用平滑分析方法给出算法的平滑竞争比,实验证实算法可行性.     

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。     

低碳经济下的港口泊位分配模型及其算法实现

航运公司正在进行前所未有的努力以减少船舶的燃油消耗量及碳排放量,而港口所制定的泊位分配计划对于船舶的油耗量和碳排放量有着直接的影响。由于船舶的到港时间是港方制定泊位分配计划的关键参数,因此将船舶到港时间作为决策变量引入传统的泊位分配（BAP）模型中,设计了港口与船方协调调度的新的泊位分配策略--VAT（Variable Arrival Time）策略,同时将船舶油耗和碳排放量融入BAP 模型的目标函数中,建立了船舶油耗量最小和船舶离港延迟时间最短的双目标优化模型。采用多目标遗传算法对该模型进行求解,并用仿真算例验证了该策略的有效性。计算结果表明,VAT策略可以大大削减航运公司的燃油消耗和船舶的碳排放,同时可以提高港口的服务水平,缩短船舶在港等待时间。     

基于量子遗传混合算法的泊位联合调度

为了提高集装箱港口服务效率,减少船舶服务的拖期费用,针对港口硬件（泊位、拖轮、岸桥）既定条件下的拖轮-泊位联合调度问题,新建了以最小化总体船舶在港时间和总拖期时间为目标的数学模型,设计了一种混合算法进行求解。首先,分析确定了将量子遗传算法（QGA）和禁忌搜索（TS）算法进行串行混合的策略;然后,依据该联合调度问题特点,在解决算法实施中的关键技术问题（染色体结构设计和测量、遗传操作、种群更新等）的同时,采用了动态量子旋转门更新机制;最后,用生产实例验证了算法的可行性及有效性。算法实验结果表明,与人工调度结果相比,混合算法的总体船舶在港时间和总拖期时间分别减少了24%和42.7%;与遗传算法结果相比,分别减少了10.9%和22.5%。所提模型及算法不仅能为港口船舶的入泊、离泊和装卸作业环节提供优化作业方案,而且能增强港口竞争力。     

重庆港骨干集装箱码头联动调度的动态模拟

多港口联合调度在我国还处于研究上的空白状态。考虑到港离港时间、满空箱量、泊位吨数等方面的因素,设计了重庆港多港口集装箱码头联动调度的原则和策略,建立了联动调度的数学模型,定义了目标函数和相应的约束条件,并实现了调度计划的动态模拟和仿真测试。     

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.     

The berth allocation problem with mobile quay walls: problem definition,solution procedures,and extensions

The berth allocation problem (BAP), which defines a processing interval and a berth at the quay wall for each ship to be (un-)loaded, is an essential decision problem for efficiently operating a container port. In this paper, we integrate mobile quay walls into the BAP. Mobile quay walls are huge propelled floating platforms, which encase ships moored at the immobile quay and provide additional quay cranes for accelerating container processing. Furthermore, additional ships can be processed at the seaside of the platform, so that scarce berthing space at a terminal is enlarged. We formalize the BAP with mobile quay walls and provide suitable solution procedures.     

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.     

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.     

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...     

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.     

Agent based simulation architecture for evaluating operational policies in transshipping containers

An agent based simulator for evaluating operational policies in the transshipment of containers in a container terminal is described. The simulation tool, called SimPort, is a decentralized approach to simulating managers and entities in a container terminal. Real data from two container terminals are used as input for evaluating eight transshipment policies. The policies concern the sequencing of ships, berth allocation, and stacking rule. They are evaluated with respect to a number of aspects, such as, turn-around time for ships and traveled distance of straddle carriers. The simulation results indicate that a good choice in yard stacking and berthing position policies can lead to faster ship turn-around times. For instance, in the terminal studied the Overall-Time-Shortening policy offers fast turn-around times when combined with a Shortest-Job-First sequencing of arriving ships.     

Robust berth scheduling at marine container terminals via hierarchical optimization

In this paper, we present a mathematical model and a solution approach for the discrete berth scheduling problem, where vessel arrival and handling times are not known with certainty. The proposed model provides a robust berth schedule by minimizing the average and the range of the total service times required for serving all vessels at a marine container terminal. Particularly, a bi-objective optimization problem is formulated such that each of the two objective functions contains another optimization problem in its definition. A heuristic algorithm is proposed to solve the resulting robust berth scheduling problem. Simulation is utilized to evaluate the proposed berth scheduling policy as well as to compare it to three vessel service policies usually adopted in practice for scheduling under uncertainty.