Search Results (9)

Background: Schedule reliability in container liner services is essential for the efficiency of maritime and inland transport, terminal operations, and the overall supply chain. Disruptions to vessel schedules can trigger a series of disruptions at other points, generating additional operational costs for carriers, terminal operators, inland transport providers, and ultimately, for importers, exporters, and end consumers. Methods: The research paper combines literature reviews and shipping company data. A qualitative analysis contains specific causes of vessel delays and corrective actions used to realign schedules with the pro forma plan. The analysis was expanded to include transport of cargo in containers from origin to the final inland destination. Results: Disruption factors are identified and classified by their place of occurrence: (1) inland transport, (2) anchorage, (3) ports, and (4) navigation between ports. The research produced several new disruptive factors previously not identified and published. It has been confirmed that port congestion acts as the principal cause of delay in liner service. Conclusions: The findings indicate that while the number and complexity of disruptive factors are increasing due to global and regional dynamics, the range of recovery measures remains narrow. A deeper understanding of these causes enables more effective prevention, aiming to minimize supply chain disruptions and costs and increase the reliability of door-to-door container transport. Full article

Effective route and speed decision-making can significantly reduce vessel operating costs and emissions. However, existing optimization methods developed for conventional fuel-powered vessels are inadequate for application to methanol dual-fuel ships, which represent a new energy vessel type. To address this gap, this study investigates the operational characteristics of methanol dual-fuel liners and develops a mixed-integer nonlinear programming (MINLP) model aimed at minimizing operating costs. Furthermore, an improved genetic algorithm (GA) integrated with the Nonlinear Programming Branch-and-Bound (NLP-BB) method is proposed to solve the model. The case study results demonstrate that the proposed approach can reduce operating costs by more than 15% compared to conventional route and speed strategies while also effectively decreasing emissions of CO₂, NOx, SOx, PM, and CO. Additionally, comparative experiments reveal that the designed algorithm outperforms both the GA and the Linear Interactive and General Optimizer (LINGO) solver for identifying optimal route and speed solutions. This research provides critical insights into the operational dynamics of methanol dual-fuel vessels, demonstrating that traditional route and speed optimization strategies for conventional fuel vessels are not directly applicable. This study provides critical insights into the optimization of voyage decision-making for methanol dual-fuel vessels, demonstrating that traditional route and speed optimization strategies designed for conventional fuel vessels are not directly applicable. It further elucidates the impact of methanol fuel tank capacity on voyage planning, revealing that larger tank capacities offer greater operational flexibility and improved economic performance. These findings provide valuable guidance for shipping companies in strategically planning methanol dual-fuel operations, enhancing economic efficiency while reducing vessel emissions. Full article

In response to climate change caused by shipping, the maritime emission trading system (METS) is used to reduce ship carbon emissions, and the METS also imposes additional costs on shipping carriers through emission permit trading. This paper focuses on the deployment of liquefied natural gas-powered (LNG-powered) ships for shipping alliances to comply with the METS. From the perspective of a liner alliance, we investigate how to determine the deployment of LNG-powered ships and how ship emissions will be affected. To investigate these problems, we propose an LNG-powered fleet deployment problem, which integrates slot co-chartering and emission permit trading, to determine the fleet deployment of LNG-powered and oil-powered ships, ship speeds and container shipment. To formulate our proposed problem, we develop a mixed-integer linear programming model, which can be solved effectively by CPLEX. Numerical experiments are provided to assess the effectiveness of our proposed model. Full article

Maritime regulators are closely monitoring the progression of green shipping, and liner companies are seeking strategies to meet tough ship emission rules. To reduce the operating cost while conforming to the increasingly strict environmental regulations, the study first constructs a mixed-integer nonlinear optimization model. Subsequently, the nonlinear parts in the objective function and constraints are transformed into linear forms. Thereafter, the model is applied to the Asia–Europe route of the CMA CGM Shipping Company to find the planned speeds and bunkering strategies for container liners sailing in expanded emission control areas (ECAs) that will be implemented in the future. Finally, a sensitivity analysis is performed to examine the influence of bunker tank capacity and fuel price difference on the operating cost, carbon dioxide emission, bunkering strategy and planned sailing speed. The study contributes to determining the optimal tank capacity and developing bunkering strategies at different fuel price differences. With stricter policies, operators must strategically choose refueling ports, adjust refueling amounts, and optimize planned sailing speeds based on ship and route data. The proposed approach provides a solution to the contradiction between compliance with environmental regulations and cost-effectiveness of shipping companies and is of great significance for promoting the sustainable development of the waterway transportation industry. Full article

Maritime transport is crucial for global trade, as over 80% of goods are transported by sea. Recent conflicts have exposed the vulnerability of shipping routes to disruptions. Therefore, devising an optimal plan for naval escort operations is critical to ensure that ships are safely escorted. This study addresses the naval escort operation problem by constructing a mixed-integer programming model that integrates escort scheduling of the warship with the speed optimization of liner ships, aiming to minimize overall cargo delay and fuel consumption costs while ensuring the protection of all ships. The results indicate that as the number of container ships increases, ships wait longer before departure with the warship, leading to a higher average delay cost per ship. For instances with a single ship type, ships have similar sailing speeds on different legs. The proposed model balances cargo delivery timeliness with carbon emission reduction, enhancing economic viability and environmental sustainability in crisis-prone maritime scenarios. Future research should explore real-time data integration and adaptive strategies to improve naval escort operations’ robustness and responsiveness. Full article

Over the past several years, port congestion has become a severe problem, as ships are often not able to reach a series of ports based on the designed schedule, which induces changes in the schedules associated with port operations. Moreover, customers can not receive their cargo in a timely manner because of port congestion. This is not only an internal problem within the shipping industry but also calls for collaboration between shipping lines and their upstream or downstream members in the maritime supply chain, including shippers and port operators. This study concentrates on the tactical planning problem for optimizing ship schedules to determine the number of ships, the projected maximum speed, and the ship service schedule, which is set for a company on a certain route. We develop a novel multi-objective programming model for the green vessel scheduling problem under port congestion, and queuing theory is used to calculate the uncertain queuing times at ports. The ultimate goal of developing this model is to maximize cost efficiency, service reliability, and environmental benefits. A multi-objective grey wolf optimizer algorithm is introduced for solving this problem, which shows some computational advantages compared to the NSGA-II algorithm commonly used at the most advanced level. Experimental results verify the application of the model and confirm that more congested periods induce more service unreliability issues rather than additional costs and emissions generated. To this end, the proposed methodology would allow designing better liner shipping schedules to alleviate port congestion and provide sustainable shipping services. Full article

In the shipping network optimization, the feeder liner companies not only need to decrease the operation cost by comprehensively optimizing the route, schedule, and fleet but also try to increase the operation income by attracting more shippers, with multimodal transport-path selection considered. Therefore, this paper proposes the integrated planning of feeder route selection, schedule design, and fleet allocation with shippers’ transport-path selection considered. We utilize the nested Logit model to analyze the shipper selection behavior for the multimodal transport path and then formulate a mixed-integer nonlinear programming model of integrated optimization for route, schedule, and fleet. To solve our nonlinear model, we propose a particle swarm optimization (PSO) framework embedded with CPLEX solver by combining the constraint relaxations and the linearization techniques with the heuristic rules. Based on the multimodal transport system in Northern China, computational experiments were conducted to verify the effectiveness of our model and algorithm. The sensitivity analyses of model parameters show that with the increase in ship rent, feeder liner companies should reduce the ship capacity or try to increase the number of port call. With the increase in fuel price, feeder liner companies should reduce the ship speed under the constraints of the arrival-time window and schedule interval. If shippers’ time utility coefficient is high, feeder liner companies should shorten the sailing time of ships by decreasing the number of port calls or shorten the at-port time of ships by increasing the service frequency. If the multimodal transport paths are cost-advantaged or if the waterway transport time has a small impact on multimodal transport time, feeder liner companies should weigh the increase in income and cost when using the measures of time saving. These management insights provide decision support for the operation practice of liner shipping network optimization. Full article

In recent years, liner-shipping companies have faced a traditional trade-off between cost and emission (CO₂ and SO_X) reduction. This study considers this element to construct a liner-shipping network design model which includes a package-cargo transport plan, route allocation, and route design. The objective is to maximize profit by selecting the ports to be visited, the sequence of port visits, the cargo flows between ports, and the number/operating speeds of vessels. In addition, emission control areas (ECAs) exist in the liner network. With reference to the idea of the column generation algorithm, this study proposed a heuristic algorithm based on empirical data through a real case calculation and selected the optimal scheme, which is in-line with both economic and environmental benefits. The results show that the model and optimization method are feasible and provide an effective solution for the liner network design of shipping companies, while also considering environmental factors. In addition, the effects of the number of ECAs, inter-port origin-destination (OD) demand, freight rate, fuel price, and carbon prices on the design of transport networks are discussed to provide a reference for the operation of shipping companies and government decision-making. Full article

Liner shipping is a vital component of the world trade. Liner shipping companies usually operate fixed routes and announce their schedules. However, disruptions in sea and/or at ports affect the planned vessel schedules. Moreover, some liner shipping routes pass through the areas, designated by the International Maritime Organization (IMO) as emission control areas (ECAs). IMO imposes restrictions on the type of fuel that can be used by vessels within ECAs. The vessel schedule recovery problem becomes more complex when disruptions occur at such liner shipping routes, as liner shipping companies must comply with the IMO regulations. This study presents a novel mixed-integer nonlinear mathematical model for the green vessel schedule recovery problem, which considers two recovery strategies, including vessel sailing speed adjustment and port skipping. The objective aims to minimize the total profit loss, endured by a given liner shipping company due to disruptions in the planned operations. The nonlinear model is linearized and solved using CPLEX. A number of computational experiments are conducted for the liner shipping route, passing through ECAs. Important managerial insights reveal that the proposed methodology can assist liner shipping companies with efficient vessel schedule recovery, minimize the monetary losses due to disruptions in vessel schedules, and improve energy efficiency as well as environmental sustainability. Full article