Fleet Operations Research Articles

The autonomous urban passenger ferry milliAmpere2: Design and testing

Abstract Autonomous urban passenger ferries have the potential to enhance urban mobility. However, despite advances in recent years, operating Autonomous Surface Vehicles (ASVs) in urban waterways remains challenging, not least because transporting passengers raises safety standards to the fore. This paper presents the autonomous urban passenger ferry “milliAmpere2,” developed by the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. Design features and test results are presented, covering the investigation of five research questions related to human-centered design, batteries and propulsion, autonomous navigation and control, remote monitoring and control, and risk assessment. A three-week public trial of the “milliAmpere2,” held in 2022, helped to synthesize research outcomes in the context of its operational environment in a confined and well-trafficked urban waterway. The “milliAmpere2” project adds to a growing number of use cases demonstrating the viability of ASVs for passenger transportation. Outstanding challenges for future research are identified, including human-autonomy teaming, remote management of fleet operation, and interactions with passengers, traffic, and remote operators.

Autonomous Truck Scheduling and Platooning Considering Cargo Consolidation

Thanks to advancements in automated driving technology, autonomous trucks (ATs) can form platoons with minimal inter-vehicle distances on highways, significantly reducing air drag and fuel consumption for fleets. Given the dispersed distribution and small quantities of cargo, fleet operators should manage ATs to enable cargo consolidation during platooning. In this way, fleet operators can enhance operational efficiency and reduce fuel consumption. This study addresses the AT scheduling and platooning problem considering cargo consolidation. The problem is the scheduling of ATs to transport cargo while consolidating cargo and forming platoons between two terminals, all while minimizing operational costs. A mixed-integer linear programming (MILP) model is formulated for the proposed problem. In addition, we conduct extensive numerical experiments to evaluate the proposed model. The results show that Gurobi can solve instances with different sizes to optimality or near-optimality. Impact analysis is also conducted to explore the influences of several factors, such as maximal platoon size and the load capacity of AT, on the system performance and to provide managerial insights.

Optimization of charging infrastructure and strategy for an electrified public transportation system

Government policies and incentives aimed at reducing the carbon footprint are increasingly focusing on the electrification of public transportation, particularly transit buses. However, electrification faces significant challenges, including optimizing the charging infrastructure, battery size and type, and charging strategies. Addressing these challenges is crucial for the effective deployment and operation of electric bus fleets. This study presents an innovative method for optimizing these aspects of electric bus systems under diverse route conditions. By leveraging general transit feed specification (GTFS) and GeoTIFF data, the developed approach ensures scalability and is grounded in real-world data. A detailed physical model, especially concerning battery degradation, adds a unique dimension to the study, providing more accurate and reliable results. The optimization method employed in this study is dynamic programming (DP), which allows for a comprehensive evaluation of various factors influencing the performance and efficiency of electric buses. The proposed approach has been validated through three distinct case studies. The findings of this study indicate that the optimized solution can lead to a substantial cost reduction of nearly 35% for operators compared to current state-of-the-art practices in Zurich, which underscores the potential of the proposed approach to contribute to more sustainable and cost-effective public transportation systems.

Assessing the viability of dynamic wireless power transfer in long-haul freight transport: A techno-economic analysis from fleet operators’ standpoint

This study presents a techno-economic assessment of dynamic wireless power transfer for long-haul freight transport, focusing on the fleet operator’s perspective. In particular, we compared three different powertrain technologies: a conventional powertrain and a battery-electric with or without a dynamic charger installed. For all three technologies, we developed a cost model to assess the total cost of ownership for a fleet operator using different scenarios.Notably, dedicated cost models were devised to estimate energy carrier costs and costs related to time loss incurred by fleet operators due to extended delivery times of electric trucks compared to conventional ones. The novelties in the cost model are twofold. First, dedicated cost models have been devised to estimate the costs related to the energy carriers (including the cost of infrastructure) and to the time loss incurred by fleet operators due to the extended delivery times of electric trucks compared to conventional ones. Second, the energy consumption by source and travel time were derived from an ad-hoc developed simulation approach models longitudinal dynamics of the case-study as well as the powertrain’s performance on the basis of experimentally derived look-up tables provided by manufacturers as well as by previous research projects. The simulation results provided by this model are instrumental to our enhanced cost model as it provides the required inputs and it allowed us to tailor the results to a specific delivery mission.Our results provide valuable insights for fleet operators considering the adoption of zero-emission trucks and to policy-makers and other infrastructure stakeholders regarding the conditions required for the cost-effectiveness of electric road systems.

Smart Fuel Monitoring for Agricultural Fleet Operations Using Tilt Sensors and Cloud Data Processing

This paper presents a novel Internet of Things (IoT)-based fuel monitoring and tracking system designed to address limitations of traditional fuel gauges and enhance fleet management efficiency. The system leverages an accelerometer and Arduino microcontroller to measure fuel level by detecting tilt angles within the tank. This data, along with real-time location from GPS, is transmitted to a cloud platform for processing and visualization. A mobile application, "Tracer," empowers users with insights into fuel level, location on a map, and distance traveled. Graphical fuel consumption patterns further aid in optimization strategies. This solution offers real-time data for improved fuel efficiency, proactive route planning, and overall fleet management. The proposed system is successfully implemented IOT based technology. This sys in Ada fruit IO cloud issued to provide information about vehicle location, fuel level in tank and distance travelled by the vehicle.

Calculations of Performance Characteristics of Submerged Cargo Pumps with Hydraulic Drive and Constant Torque Controllers, Taking into Account the Energy Efficiency of the Drive Motor

Submerged cargo pumps installed on board tankers are one of the most important components of their cargo system. As they are installed directly in the cargo tanks, they are usually equipped with a hydraulic drive whose power and capacity are controlled by constant-torque controllers. These have a significant impact on the technical and performance characteristics of the cargo pumps. This paper presents a methodology for calculating the performance characteristics of submerged cargo pumps, taking into account the energy efficiency of their hydraulic drive motors. Due to their number and power, the cargo pumps are powered from the ship’s hydraulic central loading system. This paper describes the main parts of the hydraulic power system structure and the functions of the constant torque controller of the STC type. A mathematical model has been developed to use the basic characteristics of submerged cargo pumps made for the base cargo (water) sent by the pump manufacturer for the case of handling liquid cargo with different parameters. The model considers the characteristics of the hydraulic drive, including a Bosch Rexroth A2FM type hydraulic drive motor and a constant torque controller. The results of simulation calculations of the performance characteristics of an example cargo pump are presented and compared with measurements of the characteristics of this pump on a product tanker. The mathematical model presented in this paper is of utilitarian value, enabling calculations to be carried out without the need for time-consuming CFD numerical methods, making it useful for port and fuel terminal logistics services, ship crews and services managing the operation of product tanker fleets.

Electric Autonomous Mobility-on-Demand: Jointly Optimal Vehicle Design and Fleet Operation

Electric Autonomous Mobility-on-Demand: Jointly Optimal Vehicle Design and Fleet Operation

A Multi-Module Modeling and Optimization Framework for Integrated Planning and Operation of Electric Bus Shuttle Fleets

A Multi-Module Modeling and Optimization Framework for Integrated Planning and Operation of Electric Bus Shuttle Fleets

Strategic deployment of hydrogen fuel cell buses and fueling stations: Insights from fleet transition models

Establishing new hydrogen value chains is challenging, requiring economies of scale and balanced supply-demand dynamics. Municipalities can mitigate this risk through government support and deployment strategies. This study analyzes Edmonton's transition to zero-emission buses (ZEBs), focusing on hydrogen fuel cell electric vehicles (HFCEVs) and hydrogen fueling stations (HFSs). Using scenario-based modeling and S-curve models for technology diffusion, we project the adoption of battery electric vehicles (BEVs) and HFCEVs. Deploying over 1000 ZEBs by 2040 is necessary to meet Net-Zero targets, with 310–760 HFCEVs required for the municipal bus inventory. This results in an estimated hydrogen demand of 6.2–14.5 t-H2/day and a reduction of 0.4–1.0 Mt-CO2 in tailpipe emissions by 2050. We use these scenario projections to develop a phased deployment strategy, optimizing fleet operations to reduce HFS costs by 50–60% from 8 to 9 C$/kg-H2 to 3–4 C$/kg-H2. The study underscores the importance of strategic planning and infrastructure investment in realizing net-zero goals, providing a model applicable globally.

Dynamic Adaptive Charging Network Planning Under Deep Uncertainties

Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution and charging space constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, vehicle manufacturers, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments. To this extent we propose a transport-energy-demand-centric, dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In the SDSS, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, Battery Electric Vehicle (BEV) enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and constraints and interactively and continuously calibrate and/or expand their network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access to grid capacity and real-estate and presence of competition) are gradually discovered/observed. This paper describes the components and the planning support of the SDSS and how these can be used in competitive and collaborative settings. Qualitative user evaluations of the SDSS with 33 stakeholder organizations in commercial discussions and pilots have shown that both transport-energy-demand-centric and dynamic adaptive planning of charging infrastructure planning are useful.

Life cycle management of critical refurbishable parts of ship equipment

To ensure the product life cycle (PLC), foreign and domestic information technologies have been developed that mainly ensure the stages of design and manufacture of products, but they do not allow for automation of the design of technologies for restoration and strengthening of parts. The relevance of restoration of critical parts of ship equipment is due to dependence on foreign supplies of equipment and spare parts (SP), as well as sanctions from unfriendly states, which complicated the acquisition of SP, and the need for import independence arose. The paper shows that life cycle management of a restored part has a significant impact on the efficiency of marine fleet operation, and provides stages and main sub-stages of the life cycle of restored parts of ship equipment. Restoration of parts is considered appropriate if the required durability and savings are ensured compared to manufacturing or purchasing a new part. The concept of life cycle management of restored parts is provided. Life cycle management of restored parts includes planning, control and management of all stages, as well as decision-making to ensure the durability of the part through the use of advanced materials and coating formation technologies. The stage of the life cycle of the restored part, determining the service life of the part, the economic efficiency of its restoration and subsequent operation, is the technological process that ensures the optimal chemical composition, mechanical and structural parameters of the coating. All the properties of the restored part are laid down at the stage of designing the technological process. The prospects of using the life cycle management of the restored part are shown using the example of restoring precision parts of the fuel equipment of marine diesel engines. The use of various technologies for the same type of restored precision parts allows obtaining coatings with different chemical composition, mechanical properties, structure and, accordingly, different values of durability and cost price to ensure the specified service life of the tribological unit at an acceptable price.

Demand-aware distributed pathfinding for repositioning vehicles in shared-use autonomous mobility services

Shared-use autonomous mobility services (SAMS) have the potential to provide accessible and demand-responsive mobility to passengers, while benefitting from autonomous vehicle (AV) technology and bypassing challenges related to supply-side incentives or individual driver goals. SAMS operators typically aim to achieve efficiency and improved service quality in their fleet operations, both of which are further enabled by the use of AVs. Specifically, fleet repositioning decisions in anticipation of future demand can improve service quality, but existing approaches in the literature seldom consider the problem of routing repositioning vehicles in a way that further improves SAMS objectives. This paper presents an approach for demand-aware distributed pathfinding for repositioning vehicles, which can supplement existing vehicle repositioning approaches. The problem is formulated with a multi-criteria objective that minimizes the vehicles’ total travel time and maximizes their total demand-serving potential, while distributing that potential equitably among the ride-seeking passengers across the transportation network. We evaluate the proposed approach via numerical experiments using an agent-based simulation of SAMS operations in the network of Manhattan in New York City. The proposed approach is compared to a baseline simple shortest path approach for routing the repositioning vehicles. The results demonstrate that mean passenger waiting times for pick-up can be reduced, while also reducing the total vehicle miles and the empty miles travelled due to repositioning. Thus, the proposed approach can help improve the overall system performance in terms of both service quality and efficiency metrics, relative to the baseline approach.

Cooperative control of multi-vehicle formation based on cooperative game

In this paper, an optimization decision-making method of vehicle fleet queue based on game theory is proposed. By adopting the upper and lower controller structure, the longitudinal motion stability problem of the vehicle fleet is solved. The upper controller utilizes a game control algorithm specially developed for this study. The game payoff function is constructed by considering real-time vehicle information and comprehensively evaluating factors such as expected distance, vehicle safety, and stability. The optimal expected acceleration of the vehicle is determined. Subsequently, the lower controller adjusts the engine throttle opening or brake pressure to regulate the vehicle operation according to the desired acceleration. Simulation results show that the designed game strategy can effectively reduce vehicle spacing and speed errors, thus significantly improving the stability as well as safety of fleet operation.

New Approach on the Development of Operational Fleet Management Systems Using Adaptative AI Techniques: Analysis of Adaptative Goal Weights

New Approach on the Development of Operational Fleet Management Systems Using Adaptative AI Techniques: Analysis of Adaptative Goal Weights

Analysis and directions of development of transport and logistics schemes for the delivery of grain cargoes with the participation of inland waterway transport in Russia

In this article, the authors analyzed the transport and logistics schemes for transporting grain cargoes with the possible participation of water transport in a new geopolitical situation. After the introduction of sanctions by Western countries against the Russian Federation, the existing grain delivery schemes lost their relevance, therefore, the authors proposed a scheme for transporting grain cargoes from grain-producing regions of central Russia to southern ports on the Caspian Sea. Based on the presented scheme, the authors proposed options for the delivery of grain cargoes from the subjects of the Russian Federation, tending to inland waterways to Iranian ports, within the framework of the developing international transport corridor (ITC) "North-South". The proposed delivery options necessarily imply the operation of a river fleet and river-sea vessels to deliver grain raw materials to the countries of the Middle East and Africa during navigation, as well as during the inter-navigation period.

Artificial Intelligence in Maritime Transportation: A Comprehensive Review of Safety and Risk Management Applications

Maritime transportation is crucial for global trade but faces significant risks and operational challenges. Ensuring safety is essential for protecting lives, the environment, and economic stability. This review explores the role of artificial intelligence (AI) in enhancing maritime safety and risk management. Key AI applications include risk analysis, crew resource management, hazardous material handling, predictive maintenance, and navigation systems. AI systems identify potential hazards, provide real-time decision support, monitor hazardous materials, predict equipment failures, and optimize shipping routes. Case studies, such as Wärtsilä’s Fleet Operations Solution and ABB Ability™ Marine Pilot Vision, illustrate the benefits of AI in improving safety and efficiency. Despite these advancements, integrating AI poses challenges related to infrastructure compatibility, data quality, and regulatory issues. Addressing these is essential for successful AI implementation. This review highlights AI’s potential to transform maritime safety, emphasizing the need for innovation, standardized practices, and robust regulatory frameworks to achieve safer and more efficient maritime operations.

Effect of Layout Discretization on the Performance of Zone Control-Based Multi-AGV Traffic Management Systems

Automatic Guided Vehicles (AGVs) are widely used in flexible manufacturing systems for material handling inside the factory. Traffic management strategies, required to guarantee a conflict-free operation of the overall fleet, discretize the workspace of the AGVs and use the resulting graph model for route planning and execution. In zone control approaches, AGVs move from node to node on a permit basis, with limitations on the allowed number of AGVs at a time in each area of the graph to prevent and/or resolve deadlocks and conflicts. Hence, for an optimal implementation of traffic controllers in real manufacturing systems, it is essential to understand how the layout discretization influences the performance of the AGV network. This paper analyzes its effect in grid-like shaped workspaces by using a representative zone control algorithm and a recently developed improvement of it. Realistic numerical experiments on different layouts reveal that denser discretizations do not yield faster executions or increase in throughput, while lower control periods in the permit system entail significant performance uplifts.

Optimization Strategies for Electric Vehicle Charging and Routing: A Comprehensive Review

Based on information from recent research, by 2045, Electric Vehicles (EV) will dominate the roads with presence of more than 80% of its kind. Hence, these vehicles' grid level penetration will increase proportionally, which challenges the existing grid infrastructure in terms of its reliability and energy management capabilities. New techniques to store and consume massive quantities of energy from the power grid, as well as infusing the captive energy within the EV in response to grid demands, are emerging with the advent of electric vehicles. Everything could be handled smoothly only if we schedule the EV operation (charging/discharging) more optimally and efficiently using scheduling algorithms. Despite the existence of many routings and charging schedule computations, nature-inspired optimization approaches might play a critical role in responding to such routing challenges. Researchers have created several optimum scheduling approaches, such as Dynamic Programming, Differential Evolutionary Optimization Techniques, Collaborative Optimization Scheduling, Two-stage optimal scheduling strategy, and so on. The optimum schedule review examines the operation of an EV fleet while considering uncertainty sources and varied EV operating circumstances by integrating heuristic and meta-heuristic techniques. This paper exhibits a deep review on the various EV optimal scheduling techniques and adopted algorithms which are the emerging best practices like predictive analytics, dynamic routing, user centric planning, multi-objective optimization, etc. that reflect the industry's focus on leveraging advanced technologies, data-driven decision-making, and collaborative approaches to enhance the efficiency and sustainability of electric vehicle routing and charging scheduling.

Determining demersal fisheries status in fisheries management arena (FMA) 712 under data limited situation

Abstract Demersal fisheries in FMA 712 have been established since the early 1950’s, industrialization in demersal fisheries was started on 1970’s with the operation of trawl fleets in Java Sea. The exploitation continued until the 1980s when the Trawl Ban regulation was enacted. According to historical statical data from 1980 the number of demersal fishing fleets are still dominating and endangered the sustainability of demersal fish stock in FMA 712. This study presented alternatives approaches to determining demersal fisheries status in FMA 712 using the Bayesian surplus production model (cMSY) and Length-Based Spawning Potential Ratio (LBSPR). The cMSY analysis was conducted using time series production data and known CPUE represent the area in FMA 712 and the LBSPR model was conducted using the life-history parameters and the length frequency data. The results show that current demersal fisheries condition is overexploited, points of estimation of the Biomass condition based on 1980-2022 data: B/BMSY 0,803, F/FMSY 1,75 and MSY 323.000 ton. Additionally, the spawning potential ratio (SPR) of the dominant demersal fish species (Lutjanus malabarius and Portunus pelagicus) was lower than the limit reference point (<0,20), indicating overexploited. This study demonstrates that alternatives approaches could be applied to limited data fisheries and provide useful and robust recommendations.

On the impact of co-optimizing station locations, trip assignment, and charging schedules for electric buses

As many public transportation systems around the world transition to electric buses, the planning and operation of fleets can be improved via tailored decision-support tools. In this work, we study the impact of jointly locating charging facilities, assigning electric buses to trips, and determining when and where to charge the buses. We propose a mixed integer linear program that co-optimizes planning and operational decisions jointly and an iterated local search heuristic to solve large-scale instances. Herein, we use a concurrent scheduler algorithm to generate an initial feasible solution, which serves as a starting point for our iterated local search algorithm. In the sequential case, we first optimize trip assignments and charging locations. Charging schedules are then determined after fixing the optimal decisions from the first level. The joint model, on the other hand, integrates charge scheduling within the local search procedure. The solution quality of the joint and sequential iterated local search models are compared for multiple real-world bus transit networks. Our results demonstrate that joint models can help further improve operating costs by 14.1% and lower total costs by about 4.1% on average compared with sequential models. In addition, energy consumption costs and contracted power capacity costs have been reduced significantly due to our integrated planning approach.