A high-performance memetic algorithm for integrated process planning and shop floor scheduling considering heterogeneous automated guided vehicle transportation system

Traditional petrol-fueled and electric mixed logistics vehicle fleet scheduling in urban logistics delivery under demand uncertainty

ABSTRACT Advances in vehicle technology have expanded household vehicle choice beyond vehicles’ body and age to include fuel options and technologies. This study examines preferences for vehicle body, vintage, fuel type, and the presence of advanced driving assistive systems (ADAS) using retrospective survey data from Canada. A multi-dimensional probit model is applied to capture correlations across these choice dimensions. Results confirm the existence of significant correlations, e.g., a strong positive correlation between alternative fuel vehicles (AFVs) and ADAS. The study also investigates the effects of households’ historical experiences, e.g., historical vehicle fleet composition, and exposure to technology in daily life and vehicles. Findings indicate that historically owning AFVs and ADAS has a positive influence on the preference for vehicles with ADAS. These insights support targeted marketing strategies to encourage adoption of sustainable and safe vehicles, and inform transportation and land-use policies aimed at shaping vehicle choice behavior across different population groups.

The recent increase in electric vehicles (EVs) has led to an escalation in the need for an intelligent battery management system that can deliver safe, efficient, and long-lasting lithium-ion battery operation. The aggressive charging practices result in battery degradation and exert a strong impact on the reliability, thermal stability, and lifecycle cost. The present paper suggests a hybrid framework using clouds to assist in the development of a State-of-Health (SoH) prediction using a Gated Recurrent Unit (GRU) with Double Deep Q-Network (Double DQN) charging optimisation to facilitate health-aware fast charging. The GRU model can capture both nonlinear and temporal degradation characteristics in historical battery characteristics, which allows real-time and correct estimation of SoH. These projections will form part of a double DQN reinforcement learning loop to eliminate the overestimation of Q-values and enhance policy stability in the process of charging control. A cloud-assisted architecture to update the parameters with scalable updates and monitor the fleet of vehicles also supports the proposed system. The performance of the simulation proves that the framework can minimise the capacity degradation, enhance thermal regulation, and optimise the efficiency of the charging process in contrast with the traditional constant-current and single-network DQN strategies. The co-discovery design facilitates making adaptive charging choices that trade off speed, safety, and long durability of the battery. All in all, the proposed GRU-Double DQN framework offers a strong and scalable framework to be used in the next-generation intelligent battery management system in electric vehicles.

Climate change is one of the most pressing global challenges and is closely related to the increase in greenhouse gas emissions resulting from human activities. The State of São Paulo stands out due to its economic relevance and pronounced socioeconomic heterogeneity among municipalities, making it an appropriate case for analyzing the relationship between development and environmental emissions. This study aims to examine the socioeconomic determinants of carbon dioxide equivalent emissions in the State of São Paulo, using the STIRPAT model estimated through fixed effects regressions. The results indicate that the expansion of the vehicle fleet has a positive and statistically significant impact on CO₂eq emissions. Conversely, industrial value added shows a negative coefficient, suggesting productivity gains and a potential decoupling between industrial growth and emissions in certain places. The findings reveal a strong spatial concentration of emissions among a small number of municipalities within the state.

Multi-source network-level pavement friction measurements via instrumented and dynamically routed autonomous vehicles

Estimating traffic from crowdsourced location activity: A new data source perspective

• A data-driven framework merges IoT, edge, and local analytics for EV fleets. • Machine learning forecasts battery health to guide energy-aware operations. • Hybrid BiLSTM–GRU and TabNet–TCN models enhance accuracy and scalability. • The system improves energy efficiency and stabilizes maintenance planning. • It bridges technical forecasting with strategic fleet-management insights. Efficient energy and asset management is a key economic driver in the large-scale deployment of electric vehicles (EVs). This research develops a data‑driven management framework that integrates Internet-of-Things (IoT) connectivity, edge computing, and on-premises analytics to forecast battery state-of-health (SOH) and optimize lifecycle performance in EV fleets. The proposed multilayered architecture establishes a closed decision-support loop combining localized intelligence for rapid diagnostics with centralized analytics for long-term strategic optimization. Machine-learning models—including BiLSTM–GRU and TabNet–TCN hybrids—are employed within an operational context to balance prediction accuracy, computational cost, and system scalability. Validation using the NASA PCoE lithium-ion dataset confirms that this integrated approach enhances energy-use efficiency and reduces maintenance cost variability under real-world uncertainty. In addition, connecting battery health forecasting to broader economic considerations reinforces effective management strategies, supporting cost–efficient and sustainable decision–making in real EV operations. By linking technical forecasting with managerial decision insights, the framework supports sustainable fleet operation, strengthens predictive maintenance planning, and aligns with UN SDG 7 objectives on affordable and clean energy. This study therefore bridges the technical and managerial perspectives of energy conversion and management, demonstrating how intelligent analytics can inform effective decision-making in EV-based energy ecosystems.

P-AEB performance and limiting factors for superior-rated P-AEB systems based on simulations of real-world pedestrian crashes: A simulation study on the VIPA database.

Scheduling and routing with degradation-triggered job arrivals: An application to forest firefighting with an unmanned aerial vehicle fleet

Assessing the sustainability of urban logistics solutions: Conceptual framework applied to an urban consolidation center in Madrid

Illustrative overview of CNG engine performance, emissions behavior, and operation challenges. The graphic highlights key trends identified in the systematic review, including typical 10–20% power and torque losses; reductions in CO, HC, PM, and CO 2 emissions; methane-slip escalation with mileage; and the dual effect of cleaner combustion but accelerated lubricant oxidation. The figure synthesizes evidence from 22 studies to show how engine architecture, retrofit quality, and accumulated mileage shape real-world CNG outcomes • Provides the first systematic, strata-based synthesis of CNG performance, emissions, and durability in aged and retrofitted SI and CI engines. • Demonstrates that performance and emissions outcomes under CNG are governed by engine design, retrofit quality, calibration strategy, and accumulated degradation rather than fuel properties alone. • Shows that retrofitted SI fleet engines commonly experience power loss and methane slip, while optimized dedicated SI engines achieve higher efficiency through compression ratio and combustion phasing control. • Identifies dual-fuel CI engines as offering strong particulate reduction but requiring careful pilot-injection and EGR management to avoid CO and HC drawbacks at low load. • Highlights durability and lubrication trade-offs under CNG, with reduced soot contamination but increased thermo-oxidative oil stress, emphasizing the need for robust maintenance and calibration practices. Compressed natural gas (CNG) offers significant emissions advantages over gasoline and diesel, yet most literature focuses on new or laboratory-optimized engines rather than the aged, retrofitted vehicles common in developing countries. With addition of other studies, the review followed PRISMA 2020 guidelines and a prospectively registered protocol (OSF) − https://osf.io/c8u7f/ . Searches across Scopus, IEEE Xplore, and Google Scholar identified 816 records, of which 26 studies met inclusion criteria. CNG consistently lowered CO, HC, PM, and CO 2 emissions, but retrofitted SI engines experienced 10–20% losses in power and torque due to methane’s low volumetric energy density and age-related declines in efficiency. High-mileage fleets showed methane-slip increases, catalyst deterioration, and lubricant oxidation, whereas optimized or dedicated CNG engines demonstrated improved thermal efficiency and fuel economy. Retrofit quality and calibration accuracy proved decisive in determining real-world outcomes. The findings highlight that CNG’s environmental and efficiency benefits are achievable but depend on proper engine design, maintenance, and regulatory support, especially in regions dominated by older vehicle fleets. This review provides the first systematic synthesis focused on aged, high-mileage, and retrofitted spark ignition (SI) and compression ignition (CI) engines operating on CNG, integrating evidence on performance, emissions, combustion behavior, methane slip, lubricant degradation, and catalyst aging. By comparing retrofitted and dedicated CNG engines against real-world aged engine across diverse regions, it reveals how engine architecture, retrofit quality, and accumulated mileage shape CNG outcomes and identifies the operational challenges and research priorities needed for durable, efficient, and low-emission operation.

The vehicle rental industry is undergoing rapid digital transformation, shifting from manual, paper-based processes to integrated online platforms. Existing solutions remain fragmented, often focusing on isolated features rather than providing a comprehensive, real-time ecosystem. This paper presents a Modern Full-Stack Car and Bike Rental System — a web-based marketplace developed using the MERN stack (MongoDB, Express.js, React.js, Node.js) to unify vehicle discovery, booking, and fleet management in a single platform. The system integrates JWT-based authentication, real-time atomic database synchronization to prevent double-booking, role-specific dashboards for Customers, Providers, and Administrators, automated email communication via Nodemailer, and cloud media handling via Cloudinary. The proposed architecture follows a three-tier Model-View-Controller (MVC) design pattern, ensuring separation of concerns, independent scalability, and high fault tolerance. Experimental evaluation demonstrates API response latencies between 250–450 ms for standard operations, end-toend booking transaction completion within 1.8–2.5 seconds, and 100% success in preventing concurrent duplicate reservations through atomic database operations. The system is fully functional as a Minimum Viable Product and serves as a robust foundation for future extensions including integrated payment gateways, native mobile applications, and AI-driven dynamic pricing

This article examines aspects of implementing energy efficiency monitoring for an electric vehicle fleet operating in an urban environment. Monitoring is considered as a tool of intelligent energy management for a motor transport enterprise. The electric vehicle fleet is viewed not only as an active consumer of electricity, but also as a participant in the energy market. The monitoring task is considered from the perspective of ensuring sustainable e-mobility and efficient enterprise operation. In addition, issues related to the integration of the electric vehicle fleet into the power grid and its impact on the electrical load of the energy system are also taken into account. A set of tasks for monitoring the energy efficiency of an electric vehicle fleet has been formulated to support managerial decision-making aimed at improving the energy efficiency of motor transport enterprise operations. The system of information and analytical support for monitoring the energy efficiency of an electric vehicle fleet is considered as a set of complex subsystems focused on data collection, processing, and storage, as well as data analysis, modeling, and energy efficiency control. A stratified representation approach for complex systems has been applied to solve the problem of multitasking in energy efficiency monitoring. This made it possible to simplify the description of the components of the information and analytical support system. Data collection, preprocessing, and storage are implemented on the basis of IoT technology. The analytical support subsystem consists of computational agents, each of which ensures the solution of a specific monitoring task. Object-oriented technology has been applied to formalize the system of information and analytical support for monitoring the energy efficiency of an electric vehicle fleet. The system objects are represented through three categories of classes. These class categories have integrated properties and ensure the functioning of the monitoring subsystems. The description of data structures and class functions has been developed taking into account the formulated monitoring tasks.

The paper presents an analysis of the effectiveness of the MyCar system in monitoring vehicle fleets in road transport and identifies its development prospects. Key functionalities of the MyCar system are discussed and its effectiveness is analyzed based on a selected case study. Methods for measuring effectiveness are presented, as well as how to calculate the benefits of implementing the described solution. Based on a case study describing the implementation of the MyCar system at a transport and construction company, and using real-world data, the author demonstrates that implementing the monitoring system brings measurable financial benefits.

Joint Design of Unidirectional Dedicated Lanes and Multi-Depot Shared Autonomous Vehicle Fleets with Flexible Rebalancing

The emergence of autonomous vehicles has led to the need to establish a secure and effective Vehicle-to-Vehicle (V2V) communication system to support safety and trustworthiness in both highway and urban driving environments. Nevertheless, the traditional methods of encryption are not suitable in these dynamically changing networks because of the fact that change of network topology occurs regularly, real-time processing is to be achieved and also limited access to computational resources is available. This paper presents DASKM (Dynamic Autonomous Secure Key Management), a novel solution for secure key management that is based on dynamic encryption protocol especially used to support V2V communication in autonomous vehicle networks. DASKM takes advantage of adaptive key management approach where a hierarchical and decentralized key distribution algorithm is applied on the never-ending motion and communication among vehicles. Such an adaptive model will allow changes to be done in the encryption keys in real-time with respect to the contextual information thus maintaining strong data integrity and confidentiality even under high mobility rates. The proposed dynamic encryption protocol is lightweight having lowered the computational overhead to make sure that it does not jeopardize the security and still fits into the limited processing capabilities of in-vehicle units. Although decentralized key distribution introduces additional coordination overhead, DASKM minimizes this through three design choices: (1) AES-128 in CTR mode is selected for its stream-oriented, hardware-accelerable operation avoiding block-padding overhead; (2) key rotation is event-driven rather than continuous, triggered only when vehicle speed or proximity crosses defined thresholds, eliminating unnecessary computation; and (3) the hierarchical structure uses pre-computed group keys for platoon members, reducing per-vehicle key negotiation to inter-group exchanges only. As a result, computational overhead is maintained at 10-25 ms per packet, within the processing budget of standard in-vehicle OBUs. The term lightweight therefore refers to optimized overhead relative to the security level achieved. Wide-ranging simulations confirm the effectiveness of DASKM against man-in-the-middle, replay and spoofing, and latency is reduced by a factor of 30 per cent and security response times improved by 25 per cent compared to the fixed encryption models. Among other things, DASKM was shown to scale under a high-density fleet network, achieving stable throughput, and low latency, which is scalable to the predicted increase of the number of autonomous fleet vehicles. The results demonstrate that adaptive key management protocols such as DASKM can offer a practically scalable solution to secure V2V communication, and a stepping-tone to safer and more certain network of autonomous vehicles.

Vehicle platooning has emerged as a prominent Intelligent Transportation Systems (ITS) application due to its promise toward enabling high-speed movement of Connected Autonomous Vehicle (CAV) fleets in a close formation. This close formation is usually associated with stringent constraints such as a short and strictly bounded safety gaps between consecutive platoon vehicles. In order to meet these stringent specifications, CAV fleets critically depend on the underlying platoon communication protocols, which are vulnerable to various types of attacks that may be launched by an attacker. For instance, a common attack, namely False Data Injection (FDI) attack, can potentially disrupt and destabilize a platoon’s close formation by causing collisions among platoon vehicles, or causing potential traffic disruption due to platoon slowdown, thus making the platoon unsafe . One mechanism for mitigating an FDI attack can be the placement of uniformly separated Road-Side Units (RSUs) along the path of a vehicle platoon. The RSUs can act as the root of trust to detect and mitigate attack attempts. However, frequent RSU placements over a path can lead to prohibitive deployment costs. In this work, we first formulate a constraint optimization problem which aims to minimize RSU deployments along a path (by maximizing the inter-RSU distance), while ensuring that the safety of a platoon under a given FDI attack scenario is guaranteed. Our methodology outputs an RSU placement solution such that the worst-case attack (which spans the entire inter-RSU blind spot) is unable to violate the safety guarantee of the platoon. A platoon’s robustness, in the presence of state-of-the-art attack detectors and trusted RSUs, is defined by its resilience against possible stealthy FDI attacks in the inter-RSU blind spots. We leverage this concept and propose a novel SMT-based hierarchical solution strategy. Our method iteratively hypothesizes an inter-RSU distance and formally checks the safety of the resulting platooning solution against possible attack scenarios. The process terminates when the RSU deployment spacings can no longer be relaxed without violating safety constraints. We motivate this work through simulations in PLEXE. Our experimental results demonstrate that the method is able to minimize RSU deployments while preserving safety, under diverse real-world highway platooning scenarios.

In São Paulo, the largest city in the Southern Hemisphere, the air pollution is mainly associated with vehicular traffic. The use of biofuels gives unique characteristics to the Brazilian vehicle fleet. In this study, PM2.5 samples were collected in the city at the beginning of the lockdown (from March to August 2020) and in the same period in 2022 (n = 93 samples), after the full economic reopening. The source apportionment by the FA-MLR approach showed that vehicular traffic, biomass burning, and secondary formation contributed to PM2.5 concentrations. Biomass burning made remarkable contributions in both years, and was the main source in 2020. Vehicular traffic was the primary source after the economic reopening. In 2020, concentrations of most pollutants (water-soluble ions, elements, PAHs, and their derivatives) decreased compared to previous studies, but the air pollution in 2022 reached levels close to the pre-pandemic. Additionally, Cu and Mo, elements linked to vehicular traffic, unexpectedly decreased in 2022 in relation to the lockdown and pre-pandemic period. These results were associated with the decrease in ethanol (and consequently, increase in gasoline) consumption in flexible-fuel vehicles, due to increased fuel prices. Biomass burning markers (Rb, retene, and anhydride monosaccharides) showed no change during and after the pandemic, indicating that this activity was not affected by social distancing measures. The resurgence of anthropogenic activities in 2022 resulted in increasing health risks due to exposure to carcinogenic species.

This study disaggregates pedestrian injury severity outcomes by both vehicle type and vehicle generation to statistically examine how pedestrian risk has evolved in the modern U.S. vehicle fleet. Using nationally representative Crash Report Sampling System data from 2021 to 2023, random-parameter logit models with heterogeneity in means are estimated separately for passenger cars and light trucks (which includes SUVs, and pick-up trucks) across three model-year cohorts: older vehicles manufactured between 2000 and 2010, transitional vehicles produced between 2011 and 2015, and modern vehicles produced in 2016 or later. This disaggregation reflects major shifts in vehicle mass, geometry, and the diffusion of pedestrian-related safety technologies over the past two decades. The results reveal substantial generational instability in pedestrian injury-severity relationships, particularly for light-truck vehicles. Out-of-sample simulations show that, under identical crash circumstances, transitional and modern vehicles in both classes are associated with higher predicted probabilities of severe pedestrian injury compared to older vehicles. While light trucks exhibit higher severe-injury risk than passenger cars in older generations, this class-based gap narrows substantially in modern vehicles, indicating a convergence in pedestrian injury severity outcomes. These findings demonstrate that treating vehicle classes as static masks important generational effects and highlight the need to explicitly account for vehicle generation when evaluating pedestrian risk and developing safety countermeasures.