考虑运营稳定性的内支线班轮航线网络规划模型

货主选择承运航线的影响因素,既包括挂靠港口的计划到港时间与单箱运价,还包括反映班轮运营稳定性的甩箱率与准班率。对此,文章将挂靠港口的航行与在港时间不确定引入研究,并对挂靠港口间的不确定性建立联系,基于航次仿真来计算各挂靠港的到港时间分布、船舶的航次最大载箱量分布。以班轮航线的甩箱率与准班率限制、内支线最大船型与最长往返时间为约束,在优化内支线航线网络结构的同时,计算航线适配船型、班期密度及挂靠港计划到港时间。针对所构建的带不确定参数的NP难问题,文章设计了基于模拟仿真的智能优化算法,通过方案仿真技术来处理输入模型的众分布函数,借助智能优化原理从大范围解空间内寻找满意方案。文末对船舶航次仿真与网络规划模型的有效性进行了验证,算例分析表明:内支线班轮航线网络的货主选择比例达64%,且不论货主更偏好运输时间或价格,航线方案皆能贴近货主偏好。     

基于遗传算法与贪婪策略的多港口集装箱配载研究

在物流运输行业中,集装箱运输已经成为我国长江沿岸各大港口的主要运输业务。集装箱的处理流程,尤其是集装箱的配载过程直接影响着班轮的运输效率,配载方案的制定对班轮运输起着至关重要的作用。本文针对多港口集装箱船的配载情况,利用CPLEX对该线性规划问题进行求解,并设计遗传算法和贪婪算法对长江沿岸多港口集装箱船配载情形进行对比。通过仿真实验,在小规模时遗传算法与CPLEX求解的精确解相同,验证了遗传算法的有效性。并且在大规模运输情形下,遗传算法得出的结果明显优于贪婪策略,进一步说明了遗传算法是行之有效的。得出的解决方案降低了班轮公司的运输成本,提高了港口的工作效率,对我国长江沿岸港口集装箱配载计划的制定具有一定的指导作用。     

基于仿真优化的自动化集装箱码头出口箱箱位分配

在集装箱码头堆场中，出口集装箱的箱位分配直接影响集装箱的装船效率以及船舶的在港停留时间。研究主要探讨自动化集装箱码头出口集装箱的箱位分配问题，并将船舶靠泊随机性和出口集装箱集港顺序综合考虑到整数规划模型中。针对模型的特点，设计了基于仿真优化思想的启发式算法求得集装箱贝位分配量，并提出“长途箱压短途箱”的贝内具体落位策略。多组实验结果表明，提出的基于仿真的遗传算法能够有效解决自动化集装箱码头出口箱的箱位分配问题。通过多组算例对比实验发现贝位平均翻箱次数和箱区间作业不平衡度随集装箱数量增加而增加，且“长途箱压短途箱”堆存策略能有效减少未来取箱的翻箱次数。研究结果为智能港口、全自动化集装箱码头的运营提供了思路和方向，有助于实现集装箱码头各子系统一体化效率的提升。     

考虑动态加权A星算法的预翻箱作业优化

为改善堆场作业效率，提出考虑堆场翻箱位规则的集装箱预翻箱作业优化模型并使用动态加权A星算法进行求解，进而确定满足作业规则限制的最优移动序列。基于集装箱装船过程的预翻箱作业，将预翻箱作业过程的贝位布局抽象为A星算法的状态节点，以错误堆叠箱和翻箱迭代深度计算总成本函数，引入动态加权因子控制翻箱迭代方向，提出新颖的搜索分支规则选择有效分支进行搜索。通过上海港码头和相关文献的算例进行实验，结果表明，设计的动态加权启发式函数能够有效改善寻优过程中跳出局部最优解的能力，验证了考虑堆场运作规则-翻箱位规则的算法有效性和稳定性。研究成果能够普遍适用于作业工艺和贝位布局不同的集装箱码头预翻箱作业，能够为堆场运作优化提供决策参考。     

预倒出口箱区的多场桥调度优化

在集装箱出口箱区堆场的实际作业中,常将待提箱提前翻倒至一空闲箱区,使其装船前以船舶配载图的倒序堆垛,以提高装船效率。为提高初始出口箱区的预翻作业效率,针对该箱区的多场桥调度优化问题进行研究。以实施预翻作业的某一出口箱区为研究对象,在船舶配载图已知的前提下,考虑作业场桥间保持安全距离且不可跨越的条件,兼顾满足经验翻箱规则等现实约束,侧重作业过程中实时翻箱,构建了以场桥作业总行走时间最小为优化目标的线性规划模型,并设计了分支定价算法。在算例实验中,通过与非实时预翻箱方案、FCFS方案以及下界进行对比,验证了模型及算法的有效性,可为集装箱码头出口箱堆场的场桥调度提供参考。     

考虑CO2排放的中国港口动态网络效率研究

港口是一个多时期多流程组成的动态复杂网络系统。本文以2013~2016年中国10个主要沿海港口为研究对象,综合考虑经济、运营、环境等多种因素,将港口分为生产运营阶段和盈利转化阶段,并运用动态网络SBM模型进行港口效率评价,同时考虑了港口内部结构和跨期活动因素,通过结转指标将不同时期的港口结构连接成一个动态的整体系统,构建了动态的考虑港口相邻阶段间相互活动和相邻时期间跨期活动的非线性目标规划效率模型。不仅反映了港口效率在时间序列中的动态变化,而且揭示了港口内部的薄弱环节,完善了现有港口效率评价中忽略跨期活动和内部结构影响的不足。实证结果表明:中国港口的生产运营阶段效率普遍高于盈利转化阶段效率,动态网络综合效率低下与盈利转化阶段效率低有较大的关系,港口碳排放量仍然有很大的减排空间,动态网络SBM模型比动态SBM模型区分力更强,更适于港口效率评价。     

兼顾成本与时间保证率的泊位及岸桥协同调度优化

针对集装箱班轮根据船期表按计划到离港的运行规律以及港口企业追求低运营成本的需求,本文以集装箱班轮按计划离港保证率最大和码头作业成本最低为目标,构建了泊位及岸桥协同调度多目标优化模型;设计了叠加式局部搜索算法,将其嵌入到带精英策略的非支配排序遗传算法中,经过相互交叉反馈运算,得到Pareto非劣解;采用“性价比”的概念和量化方法,选择出对港口和船公司的利益偏向最小的实施方案,解决了在Pareto解集中寻优的问题。最后,以大连港集装箱码头的生产实际为例,验证了上述优化模型及算法的合理性和有效性。     

集装箱码头泊位计划的鲁棒优化模型

针对集装箱码头作业中的不确定性因素,构建泊位计划的鲁棒优化模型与算法,目的是降低不确定性因素对集装箱码头作业系统的影响。首先,提出泊位计划鲁棒性度量指标,利用算例对各指标的效果进行分析。在此基础上,设计泊位计划鲁棒优化的两阶段优化算法。算法的第一阶段不考虑泊位计划的鲁棒性,以船舶总延误时间最小为目标;算法的第二阶段以所选择的鲁棒性指标最大为目标,以第一阶段获得的船舶总延误时间为约束条件,获得鲁棒调度方案。最后,研究作业资源(装卸桥数量)的变化对泊位计划鲁棒性的影响。算例分析表明,权重松弛量是有效的度量泊位计划鲁棒性的指标,两阶段算法可以有效解决泊位计划鲁棒优化问题。     

关于合理确定集装箱码头装船顺序的算法

如何在给出箱区图和配载图的情况下,根据不同的装船方式,运用计算机程序确定合理的发箱和装箱顺序?本文主要依据实际数据,针对两种在码头实际装船作业中应用性较强的装船方式进行了研究,分别对其做了建立数学矩阵模型,制定合理算法,编写M ATLAB程序等工作,并论证了它们在实际操作中的可行性.本文的研究结果会在集装箱码头的实际装船作业中具有很强的应用性.     

基于贪婪禁忌搜索算法的垂岸式堆场出口箱装船翻箱研究

基于垂岸式自动化集装箱码头不同装船周期出口集装箱堆场多贝位混合堆存、场桥大车在贝位间频繁移动取箱装船特点,考虑装船发箱时场桥移动等操作时间及翻箱取箱次数对出口箱装船效率和连续性影响,建立多贝位出口箱装船堆场翻箱模型,提出两阶段贪婪禁忌搜索算法,将翻箱规则嵌入算法中,有效限制算法时间和解空间增长速度。通过算例,将提出的翻箱规则与现有常见翻箱规则进行对比,验证模型及算法的有效性与实用性。结果表明,提出的模型和算法可以在合理的求解时间内输出较优的翻箱方案,减少装船时场桥发箱作业时间,提高装船作业效率。     

Short-term liner ship fleet planning with container transshipment and uncertain container shipment demand

This paper proposes a short-term liner ship fleet planning problem by taking into account container transshipment and uncertain container shipment demand. Given a liner shipping service network comprising a number of ship routes, the problem is to determine the numbers and types of ships required in the fleet and assign each of these ships to a particular ship route to maximize the expected value of the total profit over a short-term planning horizon. These decisions have to be made prior to knowing the exact container shipment demand, which is affected by some unpredictable and uncontrollable factors. This paper thus formulates this realistic short-term planning problem as a two-stage stochastic integer programming model. A solution algorithm, integrating the sample average approximation with a dual decomposition and Lagrangian relaxation approach, is then proposed. Finally, a numerical example is used to evaluate the performance of the proposed model and solution algorithm.     

Stowage planning in maritime container transportation

We consider a stowage-planning problem of arranging containers on a container ship in the maritime transportation system. Since containers are accessible only from the top of the stack, temporary unloading and reloading of containers, called shifting, is unavoidable if a container required to be unloaded at the current port is stacked under containers to be unloaded at later ports on the route of the ship. The objective of the stowage planning problem is to minimize the time required for shifting and crane movements on a tour of a container ship while maintaining the stability of the ship. For the problem, we develop a heuristic solution method in which the problem is divided into two subproblems, one for assigning container groups into the holds and one for determining a loading pattern of containers assigned to each hold. The former subproblem is solved by a greedy heuristic based on the transportation simplex method, while the latter is solved by a tree search method. These two subproblems are solved iteratively using information obtained from solutions of each other. To see the performance of the suggested algorithm, computational tests are performed on problem instances generated based on information obtained from an ocean container liner. Results show that the suggested algorithm works better than existing algorithms.     

Routing,ship size,and sailing frequency decision-making for a maritime hub-and-spoke container network

This study formulates a two-objective model to determine the optimal liner routing, ship size, and sailing frequency for container carriers by minimizing shipping costs and inventory costs. First, shipping and inventory cost functions are formulated using an analytical method. Then, based on a trade-off between shipping costs and inventory costs, Pareto optimal solutions of the two-objective model are determined. Not only can the optimal ship size and sailing frequency be determined for any route, but also the routing decision on whether to route containers through a hub or directly to their destination can be made in objective value space. Finally, the theoretical findings are applied to a case study, with highly reasonable results. The results show that the optimal routing, ship size, and sailing frequency with respect to each level of inventory costs and shipping costs can be determined using the proposed model. The optimal routing decision tends to be shipping the cargo through a hub as the hub charge is decreased or its efficiency improved. In addition, the proposed model not only provides a tool to analyze the trade-off between shipping costs and inventory costs, but it also provides flexibility on the decision-making for container carriers.     

Liner ship fleet deployment with week-dependent container shipment demand

This paper addresses a practical liner ship fleet deployment problem with week-dependent container shipment demand and transit time constraint, namely, maximum allowable transit time in container routing between a pair of ports. It first uses the space–time network approach to generate practical container routes subject to the transit time constraints. This paper proceeds to formulate the fleet deployment problem based on the practical container routes generated. In view of the intractability of the formulation, two relaxation models providing lower bounds are built: one requires known container shipment demand at the fleet deployment stage, and the other assumes constant container shipment demand over the planning horizon. An efficient global optimization algorithm is subsequently proposed. Extensive numerical experiments on the shipping data of a global liner shipping company demonstrate the applicability of the proposed model and algorithm.     

A Genetic Algorithm with a Compact Solution Encoding for the Container Ship Stowage Problem

The purpose of this study is to develop an efficient heuristic for solving the stowage problem. Containers on board a container ship are stacked one on top of the other in columns, and can only be unloaded from the top of the column. A key objective of stowage planning is to minimize the number of container movements. A genetic algorithm technique is used for solving the problem. A compact and efficient encoding of solutions is developed, which reduces significantly the search space. The efficiency of the suggested encoding is demonstrated through an extensive set of simulation runs and its flexibility is demonstrated by successful incorporation of ship stability constraints.     

A network flow model for the optimal allocation of both foldable and standard containers

This paper considers a multi-port and multi-period container planning problem of shipping companies that use both standard and foldable containers. A company must decide which number of empty containers of each type to purchase and reposition at each port within a defined period to minimize the total purchasing, repositioning, and storage costs.We develop a model to optimally allocate both foldable and standard containers. We show that a single commodity minimum cost network flow algorithm solves the problem by proving that a two commodity flow problem can be modeled as a single commodity flow problem.