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.     

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。     

Slot co-chartering and capacity deployment optimization of liner alliances in containerized maritime logistics industry

With the growing traffic of containerized shipping worldwide, container liners have seen increasing cooperation. Slot co-chartering has drawn wide attention as a way of cooperation within liner alliances. However, liners face the issue of finding proper measures to optimize alliance operations. In response to this, this paper comprehensively considers shipping capacity dispatch for containerized shipping companies within liner alliances. First, we explained the general rules of liner shipping dispatch and analyzed the interfering factors. Second, we created a liner shipping capacity dispatch model without consideration of slot co-chartering costs and a liner shipping capacity dispatch model with consideration of slot co-chartering costs and proposed a new column generation algorithm to solve both problems. Third, we tested our algorithm in a real case of a large Chinese containerized shipping company, which belongs to Ocean Alliance, and an optimal ship dispatch strategy on relevant routes and a decision on optimal slot chartering can be reached. Computational results indicate that the proposed column generation algorithm exhibits effective and efficient performance for large-scale slot co-chartering in liner shipping.     

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.     

Optimal fleet design in a ship routing problem

The problem of deciding an optimal fleet (the type of ships and the number of each type) in a real liner shipping problem is considered. The liner shipping problem is a multi-trip vehicle routing problem, and consists of deciding weekly routes for the selected ships. A solution method consisting of three phases is presented. In phase 1, all feasible single routes are generated for the largest ship available. Some of these routes will use only a small portion of the ship's capacity and can be performed by smaller ships at less cost. This fact is used when calculating the cost of each route. In phase 2, the single routes generated in phase 1 are combined into multiple routes. By solving a set partitioning problem (phase 3), where the columns are the routes generated in phases 1 and 2, we find both the optimal fleet and the coherent routes for the fleet.     

考虑潮汐影响的班轮多船型船舶调度

针对班轮企业由于提前公布船期表，但受货运需求的波动和潮汐的影响引起的多船型船舶调度问题进行研究。首先系统分析了一家班轮企业近洋运输航线结构；然后考虑大型船舶需乘潮进出港口，以及适当条件下允许租船的实际情况，兼顾班轮船期表的限制，构建了以运输总成本最小为目标的班轮多船型船舶调度非线性规划模型；最后考虑模型的特点，设计了嵌入基因修复的改进遗传算法（IGA）用于模型求解。实验结果表明，与传统的经验调度方案相比，得到的船舶调度方案在船舶利用率上能提高25%~35%；中规模算例下与CPLEX相比，IGA的CPU处理时间平均降低77%；中、大规模算例下与蚁群算法相比，IGA计算的运输费用平均降低15%。实验结果验证了所提模型和算法的有效性，可为班轮企业船舶调度提供参考。     

Liner shipping network design with deadlines

It is crucial for a liner shipping company to design its container shipping network. Given a set of port-to-port container shipment demands with delivery deadlines, the liner shipping company aims to design itineraries of portcalls, deploy ships on these itineraries and determine how to transport containers with the deployed ships in order to maximize its total profit. In this paper we first demonstrate NP-hardness of this problem and subsequently formulate it as a mixed-integer non-linear non-convex programming model. A column generation based heuristic method is proposed for solving this problem. Numerical experiments for container shipping on the Asia–Europe trade lane show that the proposed solution algorithm is efficient to find good quality solutions.     

An effective heuristic for solving a combined cargo and inventory routing problem in tramp shipping

In this paper a vendor managed inventory (VMI) service in tramp shipping is considered. VMI takes advantage of introducing flexibility in delivery time and cargo quantities by transferring inventory management and ordering responsibilities to the vendor which in this case is a shipping company. A two-phase heuristic is proposed to determine routes and schedules for the shipping company. The heuristic first converts inventories into cargoes, thus turning the problem into a classic ship routing and scheduling problem. It then uses adaptive large neighborhood search to solve the resulting cargo routing and scheduling problem. The heuristic iteratively changes the cargoes generated to handle the customer’s inventories, based on the information obtained from an initial solution. Computational results are presented, discussed and compared with exact solutions on large realistic instances. The results reveal the potential savings from converting traditional contracts of affreightment to an integrated VMI service. The factors that influence the benefits obtainable through VMI are also analyzed.     

Bilateral slot exchange and co-allocation for liner alliance carriers of containerized maritime logistics

With the maritime logistics technology evolving toward standardization and modernization, container liner shipping has become the most valued mode of transportation in the shipping industry. Liner carriers share resources in the alliance-based mode to achieve synergy. Specifically, slot exchange is a key and effective alliance-based means to enhance competitiveness and operational effectiveness. To this end, this article looks at how to optimize slot capacity allocation within a container liner alliance under the slot exchange mode in the containerized maritime logistics industry. First, we explained the slot exchange concept and its impact on the shipping business. Second, we identified the key factors that may affect slot allocation decisions. Then we established an optimal multi-objective slot exchange allocation model for liner alliances and solved the problem with the NSGA-II algorithm. Finally, taking the cooperation of two carriers on two trans-Pacific routes as an example, we used the model to establish the optimal slot exchange strategy for each carrier on the shipping alliance's routes to verify the practical application value of this model. The results showed that the model proposed in this article can help develop an optimal slot allocation plan, providing a scientific and effective tool for container liner alliances to formulate slot exchange and allocation strategies.     

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。     

Understanding Port Choice Behavior—A Network Perspective

A novel Network-based Integrated Choice Evaluation (NICE) model is developed to enhance the multinomial logit preference (MNL) model that is widely employed in the existing port choice literature. The NICE model integrates the element of port service network with observational port attributes to identify important quality characteristics on which liner shipping companies base their port choices. An empirical study of the proposed model is conducted through the service schedules of three established liner shipping companies. Results show that port efficiency and scale economies are the more important dimensions influencing liner shipping companies’ selection of major Asian ports. Nevertheless, it is important for a competitive port to balance its efforts among all the dimensions.     

Decision support and data visualization for liner shipping fleet repositioning

Liner carriers move vessels between routes in their networks several times a year in a process called fleet repositioning. There are currently no decision support systems to allow repositioning coordinators to take advantage of recent algorithmic advances in creating repositioning plans. Furthermore, no study has addressed how to visualize repositioning plans and liner shipping services in an accessible manner. Displaying information such as cargo flows and interactions between vessels is a complex task due to the overlap of container demands and long time scales. To this end, we propose a web-based decision support system designed specifically for liner shipping fleet repositioning that integrates an extended version of a state-of-the-art simulated annealing solution approach. Our system supports users in evaluating different strategic settings and scenarios, allowing liner carriers to save money through better fleet utilization and cargo throughput, as well as reduce their environmental impact by using less fuel.     

A study on bunker fuel management for the shipping liner services

In this paper, we consider a bunker fuel management strategy study for a single shipping liner service. The bunker fuel management strategy includes three components: bunkering ports selection (where to bunker), bunkering amounts determination (how much to bunker) and ship speeds adjustment (how to adjust the ship speeds along the service route). As these three components are interrelated, it is necessary to optimize them jointly in order to obtain an optimal bunker fuel management strategy for a single shipping liner service. As an appropriate model representing the relationship between bunker fuel consumption rate and ship speed is important in the bunker fuel management strategy, we first study in detail this empirical relationship. We find that the relationship can be different for different sizes of containerships and provide an empirical model to express this relationship for different sizes of containerships based on real data obtained from a shipping company. We further highlight the importance of using the appropriate consumption rate model in the bunker fuel management strategy as using a wrong or aggregated model can result in inferior or suboptimal strategies. We then develop a planning level model to determine the optimal bunker fuel management strategy, i.e. optimal bunkering ports, bunkering amounts and ship speeds, so as to minimize total bunker fuel related cost for a single shipping liner service. Based on the optimization model, we study the effects of port arrival time windows, bunker fuel prices, ship bunker fuel capacity and skipping port options on the bunker fuel management strategy of a single shipping liner service. We finally provide some insights obtained from two case studies.     

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.     

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。     

智能航运数据处理的分布式弹性计算技术研究与应用

船联网项目涉及到全国范围的内河航运数据,需要连通所有省级航运数据中心并接入数据,因此需要具备分布式、可线性扩展的并行计算能力。针对船联网项目中智能航运数据处理场景,参考国内外成熟的理论基础,提出了一种两级分布式弹性计算技术,介绍了其计算模型和故障处理机制,并描述了该计算框架的应用场景。这种计算技术可以完成海量航运数据的处理任务,满足上层航运信息服务的需求,并适应今后不断增长的数据量和计算规模。     

Routing of barge container ships by mixed-integer programming heuristics

We investigate the optimization of transport routes of barge container ships with the objective to maximize the profit of a shipping company. This problem consists of determining the upstream and downstream calling sequence and the number of loaded and empty containers transported between any two ports. We present a mixed integer linear programming (MILP) formulation for this problem. The problem is tackled by the commercial CPLEX MIP solver and improved variants of the existing MIP heuristics: Local Branching, Variable Neighborhood Branching and Variable Neighborhood Decomposition Search. It appears that our implementation of Variable Neighborhood Branching outperforms CPLEX MIP solver both regarding the solution quality and the computational time. All other studied heuristics provide results competitive with CPLEX MIP solver within a significantly shorter amount of time. Moreover, we present a detailed case study transportation analysis which illustrates how the proposed approach can be used by managers of barge shipping companies to make appropriate decisions and solve real life problems.     

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.     

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.     

基于主—从两级遗传算法的港口散货物流铲车调度

港口散货物流中,在考虑铲车能力约束条件下合理的铲车调度将提高整体的运作效率,并增强顾客满意度。通过分析货位上待服务的货车与有能力约束的铲车之间的关系,提出了利用主—从级遗传算法解决该类有能力约束的铲车调度问题。首先,建立了以缩短顾客停留时间为目标的数学模型。其次,设计了主、从递阶型式的两级遗传算法。算法中,主级求解铲车到货位的分配问题,从级求解主级所分配铲车能力约束下的货车服务次序及服务时间。另外,为避免非可行解产生,在主从级遗传算法的解码中均设计了启发式规则和惩罚函数。最后,通过一个应用实例的对比实验分析验证了此算法的有效性,并将最优解通过甘特图的形式展现出来。