Fleet Management Research Articles

Optimisation of River State Company (RTC) Fleet Management System

In the transportation industry, fleet maintenance is essential for ensuring operational efficiency, reducing costs, and extending vehicle lifespan. As fleets age, maintenance challenges escalate, impacting profitability. This study focuses on developing a maintenance reliability program for the Rivers State Transport Company (RTC) to optimize fleet management and enhance financial performance. The aim of this study is to assess RTC’s maintenance activities and design a reliability-based program that minimizes costs and maximizes profits. A mixed-method survey research design was adopted, with secondary data collected from RTC’s maintenance workshop records and financial reports spanning from 2014 to 2023. The dataset includes maintenance costs, replacement costs, and income for three vehicle models (Nissan Urvan, Toyota Hiace, and Ford Transit) across multiple years. The Dynamic Recursive Programming Model was employed to analyze vehicle age, cost, and income trends, and Microsoft Excel was used for data processing. The results show a significant rise in maintenance costs as vehicles age. For example, the Nissan Urvan’s maintenance costs increased from ₦1.2 million in 2014 to ₦2.6 million in 2023, representing a 116.7% increase over 9 years. Replacement costs for the Toyota Hiace surged from ₦4 million in 2014 to ₦8.5 million in 2023. Income generated by older vehicles also declined by approximately 25%, with the Ford Transit generating ₦5.1 million in 2023 compared to ₦6.8 million in 2014 due to frequent breakdowns and reduced reliability. The analysis further revealed that replacing vehicles after 6-7 years of operation would maximize financial returns, based on the balance of maintenance and income. In conclusion, maintaining an aging fleet presents a substantial financial burden, with rising maintenance costs, diminishing income, and increasing replacement expenses. This study recommends that RTC adopt a structured vehicle replacement policy guided by the Dynamic Recursive Programming Model. Furthermore, reliability-based maintenance strategies should be implemented to reduce unnecessary expenses and improve the overall performance and profitability of the fleet.

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.

Enhancing Fuel Economy in Heavy-Duty Trucks: A Contemporary Approach

This article reviews the effect of load-based speed control (LBSC) and gear-down protection (GDP) features on fuel consumption in heavy-duty trucks. These two electronic features, developed by Cummins Inc., are major developments in the field of engine management for optimum fuel economy. LBSC controls engine speed based on operating conditions, while GDP encourages operation in top gear for maximum fuel economy. Their mechanisms, benefits, and combined impact on fuel consumption and driver behavior are discussed. The discussion also involves challenges and future developments in this field. Research indicates that LBSC and GDP can offer significant improvements in fuel savings, up to 7% improvement in fuel efficiency. LBSC and GDP contribute to cutting fuel consumption and serve as virtual driver trainers, contributing to more efficient driving behavior. This article concludes that LBSC and GDP are important tools that help fleet managers reduce operation costs and environmental impact within the trucking industry.

Sustainability and Operational Efficiency in Air Cargo: Insights from Turkish Cargo's Management Practices

This study examines the concepts of sustainability management and efficiency in air cargo companies. In particular, the impacts of sustainability-focused activities carried out by Turkish Cargo between 2018 and 2023 on operational efficiency were evaluated. Based on THY Inc.’s sustainability reports, we conducted a qualitative analysis by evaluating and interpreting Turkish Cargo’s sustainability management and efficiency-focused initiatives. By extracting cargo data from the general reports published by THY Inc. between 2018 and 2023 and Turkish Cargo’s sustainable management policies, we determined that the company primarily focuses on sustainable growth, environmental sustainability, social sustainability, and efficiency. We also found that Turkish Cargo aims to achieve efficiency through digitalization and fleet management during this period. This study addresses a gap in existing research by focusing on the evaluation of sustainability management and efficiency concepts in air cargo transportation. Our analysis of Turkish Cargo provides a unique example at both the sectoral and institutional levels by revealing the tangible results of sustainability management. Offering a new perspective on the integration of sustainability management with efficiency, this study contributes to the literature at both theoretical and practical levels.

Human Factors Engineering (HFE) Considerations for Mounting Internal Interfaces in Heavy Vehicles

Trucking fleets often need to install additional displays and consoles in vehicles to interface with the driver. These devices may be used for fleet management, assistance with logistics, or connecting to business applications. However, if these devices are installed without consideration of the drivers, the users may become distracted or frustrated with the system. Therefore, a human factors engineering (HFE) approach to the installation of additional console(s) reduces the likelihood of rework, distractions, and driver inconvenience. Model-based systems engineering (MBSE) approach utilizing best practices for HFE design is presented in this paper. The results of the effort are captured with a prototype assembly bracket used in the production version of the heavy vehicle. The plan-do-study-act (PDSA) model-based human factors systems engineering methodology demonstrates efficiencies in the design and implementation of the display system. This method proved effective as new mounts were fitted in a preproduction electric vehicle before they were released in production.

Comparison of Carbon-Dioxide Emissions of Diesel and LNG Heavy-Duty Trucks in Test Track Environment

Environmental protection and greenhouse gas (GHG) emissions are getting increasingly high priority in the area of mobility. Several regulations, goals and projects have been published in recent years that clearly encourage the reduction of carbon dioxide (CO2) emission, the adoption of green alternatives and the use of renewable energy sources. The study compares CO2 emissions between conventional diesel and liquefied natural gas (LNG) heavy-duty vehicles (HDVs), and furthermore investigates the main influencing factors of GHG emissions. This study was carried out in a test–track environment, which supported the perfect reproducibility of the tests with minimum external influencing factors, allowing different types of measurements. At the results level, our primary objective was to collect and evaluate consumption and emission values using statistical methods, in terms of correlations, relationships and impact assessment. In this research, we recorded CO2 and pollutant emission values indirectly via the fleet management system (FMS) using controller area network (CAN) messages. Correlation, regression and statistical analyses were used to investigate the factors influencing fuel consumption and emissions. Our scientific work is a unique study in the field of HDVs, as the measurements were performed on the test track level, which provide accuracy for emission differences. The results of the project clearly show that gas technology can contribute to reducing GHG emissions of HDVs, and LNG provides a reliable alternative way forward for long-distance transportation, especially in areas of Europe where filling stations are already available.

Fuzzy Multi-Agent Simulation for Collective Energy Management of Autonomous Industrial Vehicle Fleets

This paper presents a multi-agent simulation implemented in Python, using fuzzy logic to explore collective battery recharge management for autonomous industrial vehicles (AIVs) in an airport environment. This approach offers adaptability and resilience through a distributed system, taking into account variations in AIV battery capacity. Simulation scenarios were based on a proposed charging/discharging model for an AIV battery. The results highlight the effectiveness of adaptive fuzzy multi-agent models in optimizing charging strategies, improving operational efficiency, and reducing energy consumption. Dynamic factors such as workload variations and AIV-infrastructure communication are taken into account in the form of heuristics, underlining the importance of flexible and collaborative approaches in autonomous systems. In particular, an infrastructure capable of optimizing charging according to energy tariffs can significantly reduce consumption during peak hours, highlighting the importance of such strategies in dynamic environments. An optimal control model is established to improve the energy consumption of each AIV during its mission. The energy consumption depends on the speed, as demonstrated via numerical simulations using realistic data. The speed profile of each AIV is adjusted according to the various constraints within an airport. Overall, the study highlights the potential of incorporating adaptive fuzzy multi-agent models for AIV energy management to boost efficiency and sustainability in industrial operations.

Distributed intelligence in industrial and automotive cyber-physical systems: a review.

Cyber-physical systems (CPSs) are evolving from individual systems to collectives of systems that collaborate to achieve highly complex goals, realizing a cyber-physical system of systems (CPSoSs) approach. They are heterogeneous systems comprising various autonomous CPSs, each with unique performance capabilities, priorities, and pursued goals. In practice, there are significant challenges in the applicability and usability of CPSoSs that need to be addressed. The decentralization of CPSoSs assigns tasks to individual CPSs within the system of systems. All CPSs should harmonically pursue system-based achievements and collaborate to make system-of-system-based decisions and implement the CPSoS functionality. The automotive domain is transitioning to the system of systems approach, aiming to provide a series of emergent functionalities like traffic management, collaborative car fleet management, or large-scale automotive adaptation to the physical environment, thus providing significant environmental benefits and achieving significant societal impact. Similarly, large infrastructure domains are evolving into global, highly integrated cyber-physical systems of systems, covering all parts of the value chain. This survey provides a comprehensive review of current best practices in connected cyber-physical systems and investigates a dual-layer architecture entailing perception and behavioral components. The presented perception layer entails object detection, cooperative scene analysis, cooperative localization and path planning, and human-centric perception. The behavioral layer focuses on human-in-the-loop (HITL)-centric decision making and control, where the output of the perception layer assists the human operator in making decisions while monitoring the operator's state. Finally, an extended overview of digital twin (DT) paradigms is provided so as to simulate, realize, and optimize large-scale CPSoS ecosystems.

Utilizing Spatial Statistical Bounds on Residual Stress Fields From Cold Expansion: Effects on Fatigue Crack Growth Behavior

ABSTRACTThis paper assesses the impact of utilizing statistically defined residual stress fields from cold expansion (Cx) in linear elastic, multi‐point fracture mechanics analyses using the spatial analysis of residual stress (SpARS) methodology. There is significant value and interest in leveraging the increased fatigue life afforded by Cx, but it is imperative to quantify the variability of the residual stress to understand the expected variability in benefit due to Cx. Comparisons of the predicted fatigue lives from SpARS‐produced statistical residual stress fields are made to fatigue test data. Results demonstrated that the less compressive 95% upper bound from the mean residual stress would be a reasonable strategy as it supplies a compromise between safety and inherent material and process variability while still producing a sizable improvement in predicted fatigue life. In this study, using SpARS to incorporate statistically representative residual stress fields in fatigue crack growth analyses demonstrates a methodology to aircraft structural engineers for improved fleet management and allow increased aircraft availability through fewer inspections.

Fleet sizing and static rebalancing strategies for shared E-scooters: A case study in Indianapolis, USA

With the rapid development of shared e-scooters, it is essential to understand their usage patterns for formulating informed e-scooter fleet management policies. This study first analyzes the usage pattern of shared e-scooters in Indianapolis, USA, by mining big e-scooter trip data. The analysis reveals an oversupply of shared e-scooters relative to actual user demand. Thus, a minimum fleet sizing algorithm is proposed to determine the required minimum e-scooter fleet size with the objective of reducing total operation cost, while ensuring demand coverage. Furthermore, three heuristic algorithms are proposed to address the static e-scooter rebalancing problem, focusing on minimizing rebalancing distance cost and rebalancing time. These algorithms consider practical operational constraints, including the number of rebalancing vehicles, their capacity, and the frequency of visits to e-scooter stations by rebalancing vehicles. The proposed algorithms are applied to e-scooter rebalancing scenarios with comparisons between the minimum and actual fleet sizes. The case study results in Indianapolis, USA demonstrate that the rebalancing distance cost with the minimum fleet size is significantly lower than that with the actual fleet size. What’s more, the rebalancing time can be reduced by about 12.34% to 27.80% when using the minimum fleet size. The findings of this study offer valuable policy implications and managerial insights for shared e-scooter operators and policymakers in developing effective e-scooter management strategies.

A mathematical model for potash supply chain management with a strategic logistics perspective

Abstract Accepted by: M. Zied Babai This paper introduces a novel Integer Linear Programming model designed to enhance the efficiency and sustainability of the potash supply chain, a crucial element supporting global agriculture. The developed mathematical optimization model focuses on fleet selection (private/outsource) and incorporates the concept of ‘inter-warehouse collaboration’, which addresses key logistics considerations. Integrating mining, processing, storage and transportation, the model encompasses decision variables like extracted carnallite amount, production, storage levels and shipped potash amount. Illustrated through a case study on the Arab Potash Company in Jordan, the results showcase the model’s proficiency in meeting local and international market demands. The model ensures resilient and sustainable supply chain performance by emphasizing logistics optimization, particularly in fleet selection. The study attains the highest ‘warehouse-to-warehouse’ support for Standard and Granular potash types in the international demand scenario, contributing to efficient production planning and fleet management. In conclusion, the presented mathematical model is a valuable tool for potash industry stakeholders, offering insights for strategic decision-makers involved in production planning and fleet management.

Digital Transformation In The LPG Industry: Leveraging IoT And Data Analytics For Operational Efficiency

This study presents a comprehensive systematic review exploring the transformative impact of Internet of Things (IoT), Supervisory Control and Data Acquisition (SCADA) systems, and automation technologies on the operational efficiency of the Liquefied Petroleum Gas (LPG) industry. Leveraging the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a rigorous review process was conducted to identify, screen, and synthesize existing research. A total of 90 high-quality articles were systematically reviewed to assess how digital technologies are revolutionizing key aspects of LPG operations, including inventory management, predictive maintenance, and fleet optimization. The findings reveal that integrating IoT and SCADA systems enhances real-time monitoring, significantly reduces manual errors, and improves accuracy in inventory tracking, leading to cost reductions of up to 30%. Moreover, predictive maintenance enabled by IoT data analytics was shown to decrease unplanned downtime by 40% and maintenance costs by 25%, further ensuring the reliability of equipment in this high-stakes industry. The review also highlights how automation and digitalization streamline logistics, optimize fuel usage, and improve customer satisfaction by reducing delivery lead times by 20%. Additionally, these technologies support sustainability goals by minimizing carbon emissions through optimized fleet management. By providing a detailed synthesis of current research, this review not only underscores the benefits of digital transformation in the LPG industry but also identifies gaps in the literature, suggesting avenues for future studies to enhance operational excellence, safety, and sustainability in this critical sector.

Analysis of The EwacsPro Auto Dispatch System in XYZ Company Using Technology Readiness Level (TRL) and Technology Acceptance Model 2 (TAM2) Adoption

Fleet Management System (FMS) is a system that quite good to manage the haul cycle and can be useful for reducing the idle time of the conveyance. The development of an FMS with an Auto Dispatch System (ADS) that utilizes digital information technology can make management easier. ADS is the system which has assignment logic systems for transport equipment, either directly suggesting to the operator through computerized systems or through dispatchers. XYZ Company developed its own ADS, called Early Warning & Control System Pro (EwacsPro), which has been tested on 1 of 4 total pits they have in ABC jobsite, using the LTE Communication network, which is the first in Indonesia. The problem behind this research is that the system has never been tested for readiness. This research aims to analyze, evaluate and explore the level of readiness of the EwacsPro system using the Technology Readiness Level (TRL) method and also analyze the level of operator acceptance of the use of the EwacsPro system adopting Technology Accetance Model 2 (TAM2) using Structural Equation Modeling (SEM) analysis. The research will provide recommendations to the XYZ management for improvements related to EwacsPro at ABC Jobsite so that it can provide benefits according to the technological design which has been made, using Business Model Canvas (BMC) tool. The EwacsPro system reached level 9 TRL with a fulfillment rate of 80%. There are 7 variables that significantly influence EwacsPro's Usage Behavior directly and 1 variable that significantly influences using 2 influencing moderator variables of the TAM2 analysis. The R-square test also shows that all values are above 0.75, which means that there is a strong influence from the measurement of the exogenous latent variable on the endogenous latent variable.

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

Revolutionizing Logistics and Fleet Management: A Comprehensive Analysis of the Impact of EunoKinetiX on Operational Efficiency and Societal Dynamics

The purpose of this paper is to analyze the impacts of EunoKinetiX, an Enterprise Resource Planning SaaS ( Software as a Service ) in the Fleet Management spheres coupled with Route Optimization for more efficient Logistical provision. EunoKinetiX is a platform intended to assist Logistical providers manage their services and resources, both machine and human power. The product employs artificial intelligence, both predictive and generative for route optimization and payload allocation, effectively reducing costs, CO2 emissions, and saving valuable time. Through advanced analytics, it streamlines logistics, enhancing operational efficiency while contributing to environmental sustainability. The product's integration of AI technology showcases its potential to revolutionize contemporary fleet management practices, offering a compelling solution to disorganized systems to maximize profits.

Study of Global Navigation Satellite System Receivers' Accuracy for Unmanned Vehicles.

The development of unmanned ground vehicles and unmanned aerial vehicles requires high-precision navigation due to the autonomous motion and higher traffic intensity. The existing L1 band GNSS receivers are a good and cheap decision for smartphones, vehicle navigation, fleet management systems, etc., but their accuracy is not good enough for many civilian purposes. At the same time, real-time kinematic (RTK) navigation allows for position precision in a sub-centimeter range, but the system cost significantly narrows this navigation to a very limited area of applications, such as geodesy. A practical solution includes the integration of dual-band GNSS receivers and inertial sensors to solve high-precision navigation tasks, but GNSS position accuracy may significantly affect IMU performance due to having a great impact on Kalman filter performance in unmanned vehicles. The estimation of dilution-of-precision (DOP) parameters is essential for the filter performance as the optimality of the estimation in the filter is closely connected to the quality of a priori information about the noise covariance matrix and measurement noise covariance. In this regard, the current paper analyzes the DOP parameters of the latest generation dual-band GNSS receivers and compares the results with the L1 ones. The study was accomplished using two types of antennas-L1/L5 band patch and wideband helix antennas, which were designed and assembled by the authors. In addition, the study is extended with a comparison of GNSS receivers from different generations but sold on the market by one of the world's leading GNSS manufacturers. The analyses of dilution-of-precision (DOP) parameters show that the introduction of dual-band receivers may significantly increase the navigation precision in a sub-meter range, in addition to multi-constellation signal reception. The fast advances in the performance of the integrated CPU in GNSS receivers allow the number of correlations and tracking satellites to be increased from 8-10 to 24-30, which also significantly improves the position accuracy even of L1-band receivers.

From operational to strategic modelling: A continuous multi-scale approach for last-mile analysis

Supply chain planning requires decision-making at all levels, especially in the new normal of intervened supply networks. The integration of strategic network design and operational routing decisions has been widely studied in the literature as the location-routing problem (LRP). However, the LRP does not consider the differences in the planning horizon of each sub-problem, nor have geospatial elements been included in the distribution of demand. This paper aims to redesign the conceptual modelling of the last mile to inform strategic network design problems. A continuous multi-scale approach (CMA) is proposed by taking elements from the districting problem (DP) and continuous approximation (CA). This approach includes stochastic demand in the analysis and the effects of time windows and failed deliveries. The validation of CMA in a case of rural parcel deliveries in Belgium shows an estimate of the distance travelled similar to traditional routing algorithms in scenarios with high demand density. Likewise, the effects of time windows on the spatial configuration of multi-scale districting are explored. This approach provides insights for decision-making in strategic and tactical planning, such as sonification, differentiated services to satisfy consumer preferences, and fleet management. The limitations of the CMA lie in its implementation in purely operational scenarios since it does not offer detailed routing information. Subsequent research aims to exploit the potential of strategic last-mile modelling with effective integration into network design problems.

Qualitative Data Coding of User Experience With an Urban Air Mobility Fleet Manager Interface

The NASA Aeronautics Research Mission Directorate created the High Density Vertiplex (HDV) project to integrate and evaluate a prototype Urban Air Mobility (UAM) ecosystem. Part of the HDV testing environment included a prototype operator user interface called the Fleet Manager Interface (FMI). In 2023, HDV conducted flight testing with the FMI during which a user experience (UX) study was performed to assess the quality of UX and elicit design recommendations. As a result, a large database of open-ended, qualitative responses was generated and then coded using a new qualitative data coding technique called Directive String Coding. The key themes that arose from the coded responses showed that the UX was pleasant, system notifications should be more salient, and information across multiple screens should be integrated into a central display. As the Fleet Manger operational role is still being defined, it is vital to increase our understanding of the tools and capabilities needed for such a role. The results from HDV work will eventually feed into standards for vertiport operations that will enable safe and scalable UAM operations in the United States.

Integrated planning approach for fleet sizing and fleet management of freight railcars

AbstractDespite the general support for rail as sustainable and efficient transport mode, its share on modal split remains rather low. One of the reasons for this is the low flexibility and adaptability of rail system to changes in, e.g., demand due to the complex multi-stage planning processes that are partly done manually. In our paper we focus on the fleet planning process for freight railcars and we propose an integrated planning approach consisting of two parts. Firstly, a fleet sizing optimization model is applied to obtain the optimal assignment of railcars to customers’ orders. Within this model, we consider a heterogeneous fleet and take into account substitution between clusters and customers’ preferences. The optimal fleet size is computed on a monthly basis for a planning horizon of multiple years, accounting also for railcar flows between months and years. Secondly, a fleet management model in form of knowledge-based decision support system is applied to help the planner to make changes in the fleet mix by investing into or leasing new railcars, performing revisions and decommissioning of old or unutilized railcars. As also illustrated in the designed real-world case study, the integrated planning approach is able to provide the planner with an overview of possible actions including their direct impact on the fleet mix for a mid-term planning horizon.

Assessment of bus fleet service quality: a graph theoretical approach

PurposeThe purpose of this paper is to develop a framework for the assessment of service quality of bus fleet services based on the service quality influencing factors. The paper also tries to evolve a quantitative measure for fleet service quality in the form of a fleet service quality index.Design/methodology/approachA graph theoretical approach is employed in this paper for bus fleet service quality assessment. Modelling of fleet service quality factors and their interrelations with due attention towards their structure is achieved through graph theory. A directed graph (digraph) of the service quality is developed, where its nodes represent factors influencing the quality while its edges show the degree of interrelationships. A matrix, which is equivalent to the digraph, is established that will generate a service quality function that will result in the development of a fleet service quality index (FSQI).FindingsAttaining customer satisfaction through assurance of quality is the cornerstone of the existence and survival of any business organization, and bus fleet services are no exception to this. Several influential factors are there for the bus fleet service quality. This research paper has identified factors such as fleet management practices, operational characteristics, safety and reliability features, travel comfort, bus maintenance and environmental concerns that affect fleet service quality. Every factor is composed of distinct sub-factors. Furthermore, these factors are linked with one another. A higher value of the fleet service quality index indicates the adequate performance of the bus fleet service organization.Practical implicationsThe methodology is useful for not only evaluating but also for comparison of service quality of different fleet agencies or organizations. The perceptions would be useful to the fleet service managers to create procedures and arrangements for improving the service quality.Originality/valueThe paper identifies various service quality factors of the bus fleet and an evaluation scheme for those factors has been developed. Based on these, a framework had been developed for the assessment of the service quality of different fleet service providers.