A large neighbourhood search heuristic for ship routing and scheduling with split loads

The purpose of this paper is to present and solve a new, important planning problem faced by many shipping companies dealing with the transport of bulk products. These shipping companies are committed to carrying some contract cargoes and will try to derive additional revenue from optional spot cargoes. In most of the literature on ship routing and scheduling problems a cargo cannot be transported by more than one ship. By introducing split loads this restriction is removed and each cargo can be transported by several ships. In this paper we propose a large neighbourhood search heuristic for the ship routing and scheduling problem with split loads. Computational results show that the heuristic provides good solutions to real-life instances within reasonable time. It is also shown that introducing split loads can yield significant improvements.     

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。     

考虑恶劣天气的班轮多阶段重调度方法

受恶劣天气影响的船舶调度是一个非常复杂的优化问题,也是班轮公司重点关注的问题之一。为此,针对某航运网络上的一家班轮公司的所有营运船舶,以获知设计的多阶段重调度机制时段内最新预报的天气信息和这些船舶的实时位置为前提,重点考虑班轮船期表的限制并兼顾港口间航速变化和船舶容量等现实约束,构建了以固定计划期内所有船舶的航运总成本最小为优化目标的非线性数学模型,并设计了嵌入基因修复算子的改进遗传算法用于求解该模型。由此,可以给出集成租船直运、跨航线调船、反挂和货物中转等解决策略的最佳多阶段重调度方案。通过对大、中、小规模的算例进行实验,实验结果表明,可知与传统等待办法相比,多阶段重调度节约了总航运成本的15%以上,验证了所提模型和方案的有效性;与Cplex相比,改进遗传算法的运算效率大大提高,且偏差值均在5%以内,而与蚁群优化（ACO）算法、禁忌搜索（TS）算法、量子差分进化（QDE）算法相比,改进遗传算法能在有效时间内降低10%左右的成本,验证了算法的科学性。所提方法可为班轮公司的实际船舶调度提供参考。     

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 hierarchical expert system for integrated scheduling of ship berthing, discharging and material transport

Abstract: Berthing ships at a port and unloading the stowed materials require a series of scheduling problems: ship berthing, discharging, and material transport. To deal effectively with the scheduling complexity due to mutual interdependence among those problems, this paper proposes a two-level hierarchical architecture for the integrated scheduling of all the activities arising in port. The hierarchical architecture assigns ships to multiple lower level dispatchers, each of which makes its own discharging and material transport schedule independently while satisfying the requirements imposed by the higher level coordinator. If any problem occurs within a dispatcher, the higher level coordinator attempts to resolve the trouble through the coordination of other dispatchers. Based on the hierarchical architecture, a prototype scheduling expert system is developed using G2 for the port scheduling problem at a steelworks. Through the object- oriented interaction of frames, the system is shown to effectively construct integrated schedules from the berth scheduling to the material transport scheduling.     

Collaborative mechanisms for berth allocation

This paper proposes two collaborative mechanisms between container shipping lines and port operators to facilitate port operators to make proper berth allocation decisions. In the first mechanism, assuming no transshipment, a shipping line needs to provide the port operator with the utilities associated with the start operation days of each liner route. The total utilities for all start operation days must be 0. A higher bunker and inventory cost for the shipping line means a lower utility. The port operator compensates the shipping line if its ship is scheduled on a day with negative utility and charges additional fees if the ship is scheduled on a day with positive utility. The second mechanism accounts for the utilities related to the inventory cost of transshipment containers. These two mechanisms ensure that shipping lines have no incentive to overstate or undervalue the utilities. The utilities estimated by shipping lines are much more accurate than those estimated by port operators. Hence, models for the tactical berth allocation problem incorporating the utilities provided by shipping lines lead to more efficient and equitable berth allocation plans. The utilities provided by shipping lines can also guide the decisions on operational berth allocation.     

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。     

基于遗传算法的不定期船舶调度优化模型研究

针对不定期船舶调度中客户需求信息的动态变化、船舶类型的多样化以及船舶航线的不定性,以最小化航运成本为目标函数,建立了带滚动窗口的不定期多目标船舶调度优化模型（SRPRW）,并制定了基于SRPRW模型的实时优化策略以实现需求信息变化时能及时快速地调整船舶调度航线.SRPRW模型求解时,在遗传算法（GA）中引入模拟退火机制以防止SRPRW模型的调度结果陷入局部最优解,同时设计了一种自适应交叉算子和自适应变异算子,以提高模型求解的收敛速度.实验结果表明该模型能快速地制定出船舶调度路线,及时响应客户的动态需求.     

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。     

An integrated optimization method for tactical-level planning in liner shipping with heterogeneous ship fleet and environmental considerations

The maritime transportation flows and container demand have been increasing over time, although the COVID-19 pandemic may slow down this trend for some time. One of the common strategies adopted by shipping lines to efficiently serve the existing customers is the deployment of large ships. The current practice in the liner shipping industry is to deploy a combination of ships of different types with different carrying capacities (i.e., heterogeneous fleet), especially at the routes with a significant demand. However, heterogeneous fleets of ships have been investigated by a very few studies addressing the tactical liner shipping decisions (i.e., determination of service frequency, ship fleet deployment, optimization of ship sailing speed, and design of ship schedules). Moreover, limited research efforts have been carried out to simultaneously capture all the major tactical liner shipping decisions using a single solution methodology. Therefore, this study proposes an integrated optimization model that addresses all the major tactical liner shipping decisions and allows the deployment of a heterogeneous ship fleet at each route, considering emissions generated throughout liner shipping operations. The model’s objective maximizes the total turnaround profit generated from liner shipping operations. A decomposition-based heuristic algorithm is presented in this study to solve the model proposed and efficiently tackle large-size problem instances. Numerical experiments, carried out for a number of real-world liner shipping routes, demonstrate the effectiveness of the proposed methodology. A set of managerial insights, obtained from the proposed methodology, are also provided.     

A maritime inventory routing problem with stochastic sailing and port times

This paper describes a stochastic short sea shipping problem where a company is responsible for both the distribution of oil products between islands and the inventory management of those products at consumption storage tanks located at ports. In general, ship routing and scheduling is associated with uncertainty in weather conditions and unpredictable waiting times at ports. In this work, both sailing times and port times are considered to be stochastic parameters. A two-stage stochastic programming model with recourse is presented where the first stage consists of routing, loading and unloading decisions, and the second stage consists of scheduling and inventory decisions. The model is solved using a decomposition approach similar to an L-shaped algorithm where optimality cuts are added dynamically, and this solution process is embedded within the sample average approximation method. A computational study based on real-world instances is presented.     

Multibody simulation tool for the calculation of lashing loads on RoRo ships

Approximately 80% of the international transport of goods is carried on by means of ships. A large portion of the transport capacity is represented by Roll-on-Roll-off (RoRo) ships. Especially in Europe this is a relevant potential for the RoRo segment. Consequently, the design and construction of RoRo ships plays an increasing role for German shipyards and their suppliers. In order to make the loading and unloading procedure of trailer economically more competitive, ship owners would like to improve the lashing of trailers on the ship. On the basis of a multibody system formalism, a software tool has been developed which allows for an optimization of the loading of trailers on RoRo ships. Commemorative Contribution.     

Optimization-based decision support system for crew scheduling in the cruise industry

Crew members on cruise ships are hired in a global labor market, and a major cost for cruise lines is moving crew members from their home cities to the cruise ship’s departure port. Complicating the crew scheduling problem is the uncertainty due to no-shows, terminations, and other reasons for crew to terminate their contract prematurely. To address this problem, this paper describes a scheduling system that implements a two-stage planning process that first determines overbooking levels for the number of crew to offer contracts to, and then second, a goal integer programming formulation to minimize the movement cost of assigning crew to ships while maintaining adequate crew levels and a desired crew region composition. We solve actual-sized problems characteristic of the cruise industry in a reasonably short amount of time. Experiments comparing the actual crew movement costs to the system’s projected crew movement costs show that the scheduling system can consistently reduce the movement costs in the range of 9–23%, better maintain desired crew levels, and better maintain desired crew region composition.     

Disruption management-based coordinated scheduling for vessels and ship loaders in bulk ports

For ensuring the orderly operation of the port, it is vital to coordinately schedule available ship loaders and vessels that plan to enter and exit the port when ship loaders are unable to work due to faults. Therefore, this paper studies the coordination between vessels and ship loaders scheduling problem affected by failed ship loaders (VSLPB), and proposes a novel disruption management-based method to address this problem. An innovative optimization model is developed to reduce the generalized cost with the constraints of disruption management strategies (DMS), aiming to minimize the impact of failed ship loaders on the coordinated scheduling and the bulk cargo handling efficiency. For solving the VSLPB, an effective two-stage row generation (TSRG) algorithm is developed. In the first stage, the disruption conditions in the model are released to find the available ship loaders and berths for vessels affected by the failure factors. In the second stage, the optimal strategy is sought among multiple DMS to minimize the objective function value. Using the proposed method in Huanghua Coal Port as a case study, the results show that our method can effectively solve the impact of ship loader failure on the efficiency of bulk cargo handling and the efficiency of vessels entering and leaving the port. These further highlights the importance of implementing DMS, and show that the proposed method can provide an efficient and reliable solution for port production and operation to deal with disruption problems. Furthermore, the proposed method in this paper can help improve the ability of the port to resist uncertain factors, thus improving the ability of the entire supply chain to resist risks.     

考虑港口拥堵因素的沿海海运网络优化

沿海运输权制度是沿海运输是否保留给本国船舶或者向外国籍船舶开放的制度。港口拥堵主要发生在海运网络的枢纽港上。通过考虑沿海运输权和港口拥堵对轴辐式海运网络加以优化,以海运网络内货物运输总成本最小化为目标函数,构建一个小规模整数规划模型。从欧亚航线覆盖的主要地区中选取10个港口组成海运网络进行算例分析,并使用CPLEX软件进行求解,研究结果表明,通过考虑枢纽港间货物运输的规模经济效应和产生的拥堵成本,可合理地确定枢纽港的位置;若允许沿海捎带,外资航运企业将会改变货物中转的港口。     

Model and heuristic for berth allocation in tidal bulk ports with stock level constraints

We consider the problem of allocating berth positions for vessels in tidal bulk port terminals. A berth is defined as a specific location alongside a quay where a ship loader is available for loading or unloading vessels, accommodating only one vessel at a time. In tidal ports, draft conditions depend on high tide conditions, since available depth at low tide is not adequate for the movement of ships. Some port terminals are associated with important transnational enterprises which maintain strong control over the stock level of their goods. Since the stock level sometimes depends on a continuous process of consumption or production of minerals, the decision to load or unload vessels must consider the amount of the bulk cargo stored in the port yards. Therefore, a basic criterion for decision making is to give priority to the vessels related to the most critical mineral stock level. A second basic criterion is to decide what sequence of vessels reduces the overall demurrage within a given planning horizon. This paper presents an integer linear programming model based on the transportation problem to represent the Berth Allocation Problem in Tidal Bulk ports with Stock level conditions (BAPTBS). Problem instances are solved by a commercial solver and by a Simulated Annealing-based algorithm (SA). The SA employs a problem-specific heuristic, becoming a valid alternative for finding out good solutions for difficult instances.     

Intelligent decision support for effectively evaluating and selecting ships under uncertainty in marine transportation

This paper presents an intelligent decision support system for evaluating and selecting specific ships under uncertainty. A task-oriented procedure is developed for determining the relative importance of the evaluation and selection criteria with respect to a specific shipping task. A fuzzy multicriteria analysis algorithm is developed for determining the overall performance of each ship across all the selection criteria and their associated sub-criteria. An intelligent decision support system capable of integrating the developments above is proposed for facilitating the ship evaluation and selection process. An example is presented to demonstrate the effectiveness of the proposed intelligent decision support system.     

智能船舶综合能源系统及其分布式优化调度方法

船舶航运污染是阻碍海洋经济发展、海洋强国建设的瓶颈问题. 智能船舶为航运业绿色环保发展提供了重要手段. 为进一步开发船载新能源, 提升能源综合利用效率, 降低船舶航运污染排放, 本文构建以能量优化调度系统为核心、以能源转换中心为枢纽的智能船舶综合能源系统; 考虑其特有的动力系统负荷需求、航行低污染排放量标准以及电?热多能流耦合供能特性, 建立智能船舶综合能源系统能量优化调度目标函数及相关约束条件; 并基于宽度学习、带有广义噪声的多智能体分布式优化相关理论, 提出可快速准确地预测全航程各时段负荷需求、可容纳复杂干扰的分布式优化调度方法, 实现高效的智能船舶综合能源系统能量优化调度, 保障智能船舶经济、可靠、稳定航行. 仿真分析验证了所提出智能船舶综合能源系统分布式优化调度方法的有效性.     

Collaborative optimization for loading operation planning and vessel traffic scheduling in dry bulk ports

While loading operation planning and vessel traffic scheduling are still deemed as two independent operations in practice, it has been realised that their collaborative optimization and coordination can improve port operation efficiency. It is because that two separate operations often result in vessels spending more waiting time when passing through channels and/or longer loading time at berth, and hence seriously affect the productivity and efficiency of ports. It is even worse in the case where multi-harbor basins share a restricted channel. Therefore, this paper aims to address the collaborative optimization of loading operation planning and vessel traffic scheduling (COLOPVTS) and to generate the optimal traffic scheduling scheme and loading operation plan for each vessel synchronously. Through analyzing the process of vessels entering and leaving dry bulk export ports, a multi-objective mathematical model of COLOPVTS is proposed. Due to the complexity of the model, a heuristic algorithm combining the Variable Neighborhood Search (VNS) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to solve the model. Finally, the computational results on the practical data of Phase I and Phase II terminals in Huanghua coal port are analysed to verify the rationality and effectiveness of the proposed model and algorithm.     

An artificial neural network based decision support system for energy efficient ship operations

Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN-based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data – ‘Noon Data’ – which provides information on a ship’s daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects.