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。     

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。     

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。     

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。     

单向航道下连续泊位分配与船舶调度集成优化

为改善单向航道连续泊位港口的运营效率,研究泊位分配与船舶进出港调度集成优化.考虑潮汐、进出港时段交替与偏好泊位的影响,建立0-1整数线性规划模型,以船舶偏离偏好泊位成本和滞期成本为优化目标,确定各艘船舶的靠泊位置与进出港时刻.针对问题情境和其特有的约束条件,将原数学模型通过Dantzig-Wolfe分解方法分成主问题模...     

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。     

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.     

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...     

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.     

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.     

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.     

基于粒子群算法的供油船调度

供油船调度是港口作业船舶调度中不可或缺的一个环节。根据港口的实际情况,以分析供油船的工作过程和特点为基础,本文提出了一种针对供油船的编码方式,并且将粒子群算法应用到供油船调度系统中。经过多次优化运行,证明优化的结果好于仿真。相比传统的经验调度,采用模型和算法的调度方式能够提高供油船作业的效率。     

Berth allocation planning in Seville inland port by simulation and optimisation

We study the problems associated with allocating berths for containerships in the port of Seville. It is the only inland port in Spain and it is located on the Guadalquivir River. This paper addresses the berth allocation planning problems using simulation and optimisation with Arena software. We propose a mathematical model and develop a heuristic procedure based on genetic algorithm to solve non-linear problems. Allocation planning aims to minimise the total service time for each ship and considers a first-come-first-served allocation strategy. We conduct a large amount of computational experiments which show that the proposed model improves the current berth management strategy.