To reduce CO₂ and SO₂ emissions, shipping companies have started deploying LNG or methanol dual-fuel ships on liner services. Unlike traditional container ships, these dual-fuel ships can use multiple types of fuels during a voyage, allowing them to comply with emission regulations while reducing operational costs through fuel switching and speed optimization. Given the significant fluctuations in bunker prices across different ports, decisions regarding fuel switching, refueling, and sailing speeds must account for price uncertainty. We develop a distributionally robust chance-constrained programming model based on the Wasserstein uncertainty set to minimize operating costs under this uncertainty. We divide each port-to-port sailing leg into sub-legs, considering regional emission requirements or canal segments. This segmentation enables the optimization of fuel usage proportions, sailing speeds, and refueling strategies for each sub-leg. The model is then reformulated as a tractable mixed-integer second-order conic programming model. We validate the model using real-world data from COSCO Shipping. Numerical experiments demonstrate that the model can identify optimal solutions for real-scale instances within practical computational time. Furthermore, the robust solutions significantly outperform those obtained using the traditional sample average approximation method. Our results suggest that the joint optimization of fuel management and sailing speeds for dual-fuel ships can effectively reduce operating costs without increasing emissions.

Hydrogen-based fuels are potential candidates to help international shipping achieve net-zero greenhouse gas (GHG) emissions by around 2050. This paper quantifies the environmental impacts of liquid hydrogen, liquid ammonia, and methanol used in a Post-Panamax container ship from 2020 to 2050. It considers cargo capacity changes, electricity decarbonization, and hydrogen production transitions under two International Energy Agency scenarios: the Stated Policies Scenario (STEPS) and the Net Zero Emissions by 2050 Scenario (NZE). Results show that, compared to the existing HFO ship, hydrogen-based propulsion systems can decrease cargo weight capacity by 0.3 % to 25 %. In the NZE scenario, hydrogen-based fuels can reduce GHG emissions per tonne-nautical mile by 48 %–65 % compared to heavy fuel oil by 2050. Even with fully renewable hydrogen-based fuels, 18 %–31 % of GHG emissions would still remain. Using hydrogen-based fuels in internal combustion engines requires attention to minimize environmental trade-offs.

Fuel cells have the potential to reduce greenhouse gas (GHG) emissions from deep-sea shipping. To fully understand the environmental impacts of integrating fuel cells into deep-sea ships, this study evaluates the life cycle environmental impacts from 2020 to 2050 for two leading fuel cell systems: liquid hydrogen with proton exchange membrane fuel cells (liquid-H₂ PEMFC) and liquid ammonia with solid oxide fuel cells (liquid-NH₃ SOFC). The study covers various factors, including changes in cargo capacity, operation modes, developments in hydrogen production and electricity decarbonization. We examine two energy scenarios developed by the International Energy Agency: the Stated Policies Scenario (STEPS) and the Net Zero Emissions by 2050 Scenario (NZE). Our findings reveal that, under different ranges and speeds, the liquid-H₂ PEMFC results in a 2% increase to a 10% decrease in cargo weight, while the liquid-NH₃ SOFC leads to a 4%–23% decrease. By 2050, under the NZE scenario, liquid-H₂ PEMFC and liquid-NH₃ SOFC can reduce GHG emissions per tonne-nautical mile by 69%–75% and 65%–71%, respectively, compared to traditional ships. The use of fuel cells also introduces environmental trade-offs. This assessment can help policymakers gain a more comprehensive understanding of the role of fuel cells in reducing GHG emissions in deep-sea shipping and underscores the potential environmental challenges associated with their large-scale deployment in the future.

Maritime shipping plays a vital role in global trade, involving a multitude of actors such as shipping lines, ports, and diverse logistics providers. Collaborative operations planning among those actors is imperative to advance overall efficiency and comply with increasingly strict decarbonization policies. Recent events such as the COVID-19 pandemic and the Red Sea crisis have further highlighted this need for collaboration and have led the sector to create new forms of collaboration to enhance its resilience. Although collaborative transportation strategies have been suggested for decades, the related literature on maritime transport remains fragmented, lacking a comprehensive literature review of related research as is available for other transportation modes. In this work, we present a systematic survey on collaboration within the maritime and port transportation sector, taking a critical look at the challenges these collaborative systems face and mapping ways in which Operations Research (OR) methods are used to address them. Building on the two main forms of vertical and horizontal collaborations, we distinguish the involved stakeholders, analyze collaboration approaches, classify decision support and OR approaches, and discuss practical applications, leading to a research agenda that outlines specific challenges for future research. In this way, we connect the fragmented problems and approaches to a roadmap for future collaborative maritime and port transportation systems. This survey helps maritime researchers and practitioners find the right methods for their challenges and gain insight into directions for future collaboration, catalyzing both further research in academia and industrial implementations. This survey further facilitates advanced collaboration in maritime transportation systems, showing pathways towards visions of large-scale collaboration such as the Physical Internet.

Metro networks face operational challenges due to increasing ridership and system growth, particularly in managing delay propagation. Epidemiology models have recently been an interesting method in transportation research for studying delays. This study, therefore, aims to investigate if the Susceptible-infectious-susceptible (SIS) model is suitable to help model delay propagation in a metro network through its ability to reproduce the vulnerability of metro stations for specific instances. Using data from the Washington Metro Network, two groups of delay propagation instances were selected and used for model training and testing using a differential evolution algorithm. The results indicate that the vulnerability values as calculated from the reallife data do not follow the expected trend. Still, our model can capture this variation with good vulnerability estimation accuracy for both groups. Also, the predicted vulnerability values for the first group are more accurate than for the second group. However, limitations such as underestimation and overestimation of station vulnerabilities, and sensitivity to training data were observed. These challenges stemmed from the dynamics between specific parameters and the lack of additional factors.

Recent supply chain disruptions and crisis response policies (e.g., the COVID-19 pandemic and the Red Sea crisis) have highlighted the role of container terminals as crucial and scarce resources in the global economy. To tackle these challenges, the industry increasingly aims for advanced operational collaboration among multiple stakeholders, as demonstrated by the ambitions of the recently founded Gemini alliance. Nonetheless, collaborative planning models often disregard the requirements and incentives of stakeholders or simply solve idealized small instances. Motivated by the above, we design novel and effective collaboration mechanisms among terminal operators that share the resources (berths and quay cranes). We first define the collaborative berth allocation problem and propose a mixed integer linear programming (MILP) model to minimize the total cost of all terminals, referred to as the coalitional costs. We adopt the core and the nucleolus concepts from cooperative game theory to allocate the coalitional costs such that stakeholders have stable incentives to collaborate. To obtain solutions for realistic instance sizes, we propose two exact row-generation-based core and nucleolus algorithms that are versatile and can be used for various combinatorial optimization problems. To the best of our knowledge, the proposed row-generation approach for the nucleolus is the first of its kind for combinatorial optimization problems. Extensive experiments demonstrate that the collaborative berth allocation approach achieves up to 28.44% of cost savings, increasing the solution space in disruptive situations, while the proposed core and nucleolus solutions guarantee the collaboration incentives for individual terminals.

The application of automated ground vehicles (AGVs) is well-established in closed environments such as port terminals, while their operation in open areas remains challenging. In this work, we set out to overcome this limitation by introducing platooning as a transfer mode in heterogeneous vehicle networks. We propose a collaborative transportation framework where different transportation companies use a shared platform for delivery tasks. To support decarbonization efforts in port hinterland transport, we consider fleets comprising electric AGVs (E-AGVs) and conventional trucks. These E-AGVs need to visit charging stations, modeled as battery swap stations (BSS), and join platoons to travel within the linking road segment. Each carrier has contracts with certain BSSs and shares these stations through the platform as part of the transportation plan. The platform functions as a demand and resource pooling mechanism, further offering platooning and infrastructure-sharing services. We model the interaction between the platform and carriers as a two-level constrained Stackelberg competition. An efficient solution algorithm, incorporating problem-specific heuristics and an adaptive large neighborhood search with dedicated destroy, repair, and intensification operators, is proposed. Extensive numerical experiments demonstrate the algorithm's performance on both existing and new benchmark instances. Our results highlight the platform's potential to streamline port-hinterland logistics, with E-AGV platoons significantly reducing costs and emissions.

Port terminals, especially their reefer container yards, face surging power demands. Efficient reefer charging is critical for port sustainability and efficiency, as it helps reduce peak energy loads and total energy consumption. This requires consideration of reefer characteristics, temperature control requirements, time-variant energy prices, port power distribution network and environmental factors such as ambient temperature and sunlight. Optimising the charging power and internal temperature of reefers is therefore essential. This study introduces mathematical models to optimise two efficient charging schemes for reefers: flexible power charging and on/off power charging. Internet-of-Things (IoT) technologies can enable tailored optimisation strategies for reefer charging by facilitating information sharing among reefers and the charging planning system. This study also proposes a cyber-physical system for IoT that allows these charging schemes to be implemented. Using data from existing ports, the results demonstrate that the optimised reefer charging plan significantly reduces energy costs and alleviates peak energy consumption, consistently outperforming the baseline policy. In the optimised plan, charging periods are slightly adjusted based on energy price in each period as part of a demand response strategy, and intermittent charging is actively used for peak energy shaving. The study also quantifies the positive impact of roof shade installation. Findings provide actionable insights for refrigerated goods.

Rising energy expenses, the shift towards renewable sources, and grid congestion considerably affect the operations of container terminals. To tackle these challenges, it is necessary to implement energy-aware integrated operational planning which considers related uncertainties. This work proposes a two-stage stochastic mixed integer programming model to optimize container terminal operations planning and demand-responsive energy management. To this end, energy consumption is shifted whenever operationally possible and economically beneficial. We solve the proposed model by developing a dedicated progressive hedging algorithm. Operations considered in this model include vessel scheduling at berths, temperature control of refrigerated containers, and allocation of handling capacity of quay cranes, yard cranes, and automated guided vehicles to serve each vessel. Various scenarios for vessel arrival times and electricity prices are explored representing the uncertainty of energy demand and supply, respectively, based on a case study of the Altenwerder container terminal in Hamburg. Our results suggest potential cost savings of 5.9 per cent on average with a single energy price based on a long-term contract and 13.2 per cent when applying varying real-time electricity prices based on wholesale market rates. These findings underscore the substantial potential of demand response strategies for (electrified) container terminal operations.

Airport buses play a crucial role in addressing the last-mile problem of air travel, especially in cities and countries lacking inner-city rail transit systems. Nevertheless, airport buses are currently witnessing a decline in ridership due to drawbacks such as long departure intervals, inflexible stops, and considerable distances between stops. Consequently, delivering high-quality airport bus services has become a pressing concern for public transport operators. Motivated by new flexible buses and ride-sharing services, this paper explores a flexible airport bus service that integrates ride-sharing services for passengers traveling from bus stops to their destinations. This problem entails integrated decisions involving bus stop selection, passenger assignment to drop-off bus stops, as well as bus and ride-sharing routing. Accordingly, this problem presents more challenges in decision-making than traditional flexible bus or ride-sharing routing problems. We first develop an arc-based mixed-integer linear programming model. Subsequently, we design a double decomposition math-heuristic algorithm that builds upon logic-based Benders decomposition and column generation algorithms to obtain a near-optimal solution within practical computation time limits for practical-scale instances. Additionally, we implement an adaptive large neighborhood search algorithm to evaluate the solution quality of this math-heuristic algorithm and to solve large-scale instances. To validate the effectiveness of both the model and the algorithms, we conduct numerical experiments using instances derived from Shenzhen airport bus lines. The experimental results demonstrate that the flexible service mode offers significant advantages in reducing both passenger ride time and vehicle mileage over traditional airport bus or taxi modes.

The burden of first-mile connection to public transit stations is a key barrier that discourages riders from taking public transportation. Public transit agencies typically operate a modest fleet of vehicles to provide first-mile services due to the high operating costs, thus failing to adequately meet the first-mile travel demands, especially during peak hours. At the same time, private cars are underutilized and have a lot of idle time. With the emergence of self-driving vehicles, new opportunities for addressing the current dilemma arise, such as integrating idle private self-driving vehicles to provide first-mile services, which is beneficial for public transportation agencies to provide high-quality services at low costs. This study investigates the first-mile ridesharing problem in which public transit agencies utilize idle privately-owned autonomous vehicles to dynamically inflate their fleet. This problem is more challenging in decision-making than conventional first-mile problems, as it involves decisions on heterogeneous fleet scheduling, vehicle routing, and time scheduling, all while taking into account the service quality for riders. To address this problem, an arc-based mixed-integer linear programming (MILP) model and a trip-based set-partitioning model are developed, both aiming to minimize total operational costs. To identify promising trips, we propose a tailored labeling algorithm with a novel dominance rule, along with a time window shift algorithm to determine the best schedule. To yield high-quality solutions in a short computation time, a tailored column-generation matheuristic algorithm is introduced. A branch-and-price exact algorithm and an adaptive large neighborhood search algorithm are developed to assess the matheuristic algorithm. Numerical experiments are conducted to demonstrate the effectiveness and applicability of the proposed models and algorithms. Experiments also show that this kind of ridesharing service can provide low-cost and high-quality services for the first-mile problem.

With the growing demand for high-quality mobility services, transportation service providers need to offer transit services that not only fulfill passengers’ basic travel needs but also ensure an appealing quality of service. During rush hours, fleet sizes are often insufficient to cater to all passenger preferences on service quality, such as ride time and number of co-riders, leading to the sacrifice of service quality for some passengers. Motivated by these practices, we investigate a first-mile ridesharing problem incorporating passenger service quality preferences. This problem involves intricate decisions about the match between requests and vehicles, vehicle routing, and route schedules. To solve this problem, we first develop an arc-based mixed-integer linear programming (MILP) model for this problem. For obtaining near-optimal solutions within practical computation time requirements, we reformulate the MILP model as a trip-based set-partitioning model and propose a math-heuristic algorithm. This algorithm builds upon the column-generation algorithm and tailored bidirectional labeling algorithms with novel dominance rules. Additionally, we introduce a proposition to determine the best schedule for each ridesharing route. To obtain the optimal solution for large-scale instances, we introduce a branch-and-price exact algorithm. Computational experiments based on real-world road networks and randomly generated instances confirm the effectiveness and efficiency of the proposed approaches, demonstrating that the proposed matheuristic finds near-optimal solutions within 40 s for all instances. The results also show that the presented approach significantly improves the quality of first-mile services for prioritized riders, with the ratio of satisfied requests increasing by 23% even when the fleet is generally insufficient.

One of the major challenges in the development of Automated Driving is its assessment. It is expected that Automated Vehicles behave differently than human drivers. Therefore, mixed human-robot traffic will yield different and new driving situations as human-only traffic. It is important to know how this mixed traffic will change the composition of traffic situations to be able to quantify the impact Automated Vehicles will have on everyday traffic. This paper presents a methodology on how to find metrics that quantify traffic in order to detect changes in the traffic space that will come with the introduction of Automated Vehicles. Additionally, this methodology provides tools to help with the validation of virtual testing platforms such as simulation.