Search Results (149)

This paper studies large-scale last-mile delivery using a heterogeneous fleet of trucks, onboard drones in a hybrid truck–drone mode, and independent drones. Orders are first screened by a feasibility check; feasible orders are then assigned to one of the three modes by a delivery mode selection policy and routed using mode-specific planning algorithms. The delivery mode selection policy is trained with Proximal Policy Optimization (PPO), warm-started by behaviour cloning from heuristic decisions. For route planning, we use a five-step procedure for the hybrid mode and simple depot round trips for independent drones. Experiments on Solomon VRPTW benchmarks and extended instances (100/200/400 customers; R/C/RC distributions) show lower total cost than representative heuristic baselines and metaheuristics, with practical runtime. Sensitivity analysis over fleet sizes further indicates competitive performance across a range of truck and drone configurations, especially for medium and large fleets. Full article

Despite the large amount of successful existing methods and frameworks for planning sets of multiple unmanned aerial systems (UASs), there is still a lack of coordination frameworks that are capable of coping with real-world operational conditions. This paper presents U-Plan, an integrated management framework for the coordination of multi-UAS missions. U-Plan is designed to plan, schedule, monitor, and replan a heterogeneous set of UASs to complete point of interest (PoI) visiting missions while ensuring that all the generated trajectories are safe, feasible, and compliant with the required PoIs’ arrival times, UAS kinematics and energy constraints, and the existing 3D no-fly zones (NFZs). U-Plan is designed as a practical tool for strongly dynamic missions and is built upon three core components: (1) an NFZ-aware route computation method that explicitly accounts for NFZs prior to vehicle routing problem (VRP) optimization, resulting in shorter NFZ-safe routes; (2) a trajectory smoothing module that ensures the generation of kinematically feasible trajectories for fixed-wing UASs; and (3) a mission supervision module for real-time monitoring and replanning in case of changes in the UAS, mission, wind speed, or airspace restrictions. To validate the proposed architecture, we conducted rigorous experiments utilizing the VECTOR-SIL autopilot and Visionair Ground Control Station to realistically replicate the behavior of certified fixed-wing autopilots under various weather conditions using the exact same hardware and flight control software that runs onboard the physical drones. The validation shows U-Plan’s capacity to efficiently satisfy complex mission requirements with strong scalability. Due to its high computational efficiency, U-Plan enables online mission replanning, allowing UAS fleets to seamlessly adapt to changes that are typical of real-world operational scenarios. Full article

Post-disaster Search and Rescue (SAR) missions demand rapid coordination of Heterogeneous Unmanned Aerial Vehicle (UAV) fleets under stringent payload and flight range limitations. Traditional heuristic solvers struggle to solve the Large-Scale Heterogeneous Team Orienteering Problem (LSH-TOP) within operational time limits due to the coupled complexity of task allocation and route planning. A Hierarchical Deep Reinforcement Learning framework decomposes this high-dimensional combinatorial problem into tractable sub-problems. An upper-level policy, guided by Monte Carlo Tree Search (MCTS), partitions the global target set to balance fleet workload distribution, whereas a lower-level Transformer-based model constructs near-optimal trajectories for individual agents. A Curriculum-Integrated Alternating Cooperative Training (C-ACT) protocol resolves the convergence difficulties associated with sparse feasible solutions in constrained environments. This protocol incorporates a dynamic constraint annealing strategy and a virtual agent buffer to progressively shape the solution space from relaxed to strictly constrained formulations. Experiments conducted on real-world geographic data demonstrate the proposed approach consistently outperforms all baselines across scales of 80 to 300 targets, improving over the strongest competitor by 0.63–8.51% and over conventional heuristics by up to 53.27% in objective value. Results indicate a task completion rate of 27.5% at the 300-target scale (versus 25.1% for the strongest baseline MCTS + OR) and balanced workload distribution, validating framework adaptability to complex emergency response scenarios. Full article

Battery-based electrification is increasingly recognised as a key pathway for reducing greenhouse-gas emissions in maritime transport, particularly for vessel segments characterised with short, predictable operation profiles. To ensure an environmentally sustainable transition, it is essential to quantify the potential environmental benefits of these solutions. Life Cycle Assessment (LCA), standardised by ISO 14040 and ISO 14044, is the internationally recognised methodology for evaluating environmental impacts across the entire life cycle and for consistently comparing options providing the same function. This study presents a methodological review of LCA applications to battery-based ship electrification, with the objective of analysing key assumptions, comparability issues, and limitations across the existing literature. A systematic review was conducted on 24 studies, focusing on core methodological aspects, including product system definition, functional unit selection, system boundaries, life cycle inventory modelling, and impact assessment methods, while considering contextual elements such as fleet segmentation and propulsion configurations to support the interpretation of methodological choices. The analysis reveals significant methodological heterogeneity across studies, particularly in product-system definitions, functional unit selection, modelling detail, and impact category coverage, which limits cross-study comparability. This review also highlights a strong concentration of applications on short-route passenger ferries, while other vessel categories remain underrepresented, further constraining the generalisability of the findings. Although a direct quantitative comparison of results is not methodologically appropriate due to this heterogeneity, climate change mitigation consistently emerges as a key benefit across the analysed studies. At the same time, the multi-impact perspective of LCA highlights relevant trade-offs related to material use, toxicity, and resource depletion. Overall, the findings underline the need for more harmonised methodological approaches and a holistic life cycle perspective to support robust and comparable environmental assessments as battery-based solutions expand within the maritime sector. This review provides a structured interpretation of methodological variability and identifies priorities for future LCA applications. Full article

Background: Smart-city road and intersection management increasingly aims to smooth bus operations and reduce stop-and-go driving, but cities often lack auditable indicators linking routine fleet data with comparable energy and environmental KPIs. Methods: This study develops a Monitoring–Reporting–Verification (MRV) workflow for daily bus records from a 2024 Polish metropolitan fleet (diesel, compressed natural gas (CNG), hybrid, and battery-electric buses). Records were quality checked, harmonized to MJ/km, aggregated to bus-month observations, and analyzed using a linear mixed-effects model with propulsion technology, season, and activity level as fixed effects and vehicle-level random intercepts. Environmental impacts were then calculated under well-to-wheel (WTW) boundaries using Environmental Footprint 3.1 (EF 3.1) impact categories, Poland’s 2024 electricity mix, and illustrative electricity-mix scenarios through 2050. Results: Relative to diesel, BEV and HEV were associated with lower adjusted energy intensity (ratios 0.272 and 0.681, respectively), whereas the CNG–diesel contrast was directionally higher but statistically inconclusive under the available CNG sample. BEV energy intensity more than doubled in winter in descriptive terms, and vehicle-specific heterogeneity remained high (ICC ≈ 0.61). The BEV climate profile improved under electricity decarbonization, while some EF categories showed mix-dependent trade-offs. The 3–10% traffic-management variants are interpreted as screening assumptions rather than measured ITS effects. Conclusions: Routine bus records can support auditable MRV and preliminary screening of fleet and corridor interventions, but causal traffic-management evaluation requires route-level trajectory, congestion, and before–after data. Full article

The optimisation design of agricultural machinery is shifting from offline, experience-driven engineering towards adaptive, data-driven, and closed-loop intelligent optimisation. Conventional approaches based on computer-aided engineering (CAE), empirical testing, mathematical modelling, and static multi-objective optimisation have provided an important engineering foundation, but they remain limited under unstructured field conditions involving soil heterogeneity, crop variability, climatic disturbance, and nonlinear machinery–environment interactions. This review systematically examines the evolution of intelligent optimisation design for agricultural machinery from conventional simulation-based methods to artificial intelligence (AI)- and digital twin (DT)-enabled paradigms. First, mathematical modelling, response surface methodology, discrete element method (DEM), computational fluid dynamics (CFD), multi-body dynamics (MBD), heuristic algorithms, and early AI-assisted surrogate optimisation are reviewed to clarify their contributions and limitations. Second, frontier enabling technologies are analysed, including agriculture-specific large models, generative AI, lightweight edge intelligence, deep reinforcement learning (DRL), embodied AI, federated learning (FL), and privacy-preserving computing. Third, system-level applications integrating DT and AI are discussed, with emphasis on full-lifecycle machinery optimisation, device–edge–cloud collaborative control, multi-agent fleet coordination, predictive maintenance, and Agriculture 5.0-oriented intelligent equipment systems. Key deployment bottlenecks are further identified, including sim-to-real inconsistency, virtual–physical mismatch in DTs, edge-side trade-offs among accuracy, latency, energy consumption, and cost, insufficient validation standards, and economic adoption barriers. Finally, a 2025–2030 roadmap is proposed, highlighting large-model–DT closed loops, control biomimetics, green low-carbon optimisation, and trustworthy human–machine symbiosis for sustainable Agriculture 5.0. Full article

Vehicle emission inventories are highly sensitive to vehicle activity data, yet annual vehicle kilometers traveled (VKT) is still commonly represented using generalized default values whose representativeness at the city scale remains uncertain. In this study, large-scale vehicle inspection data from Haikou, China, were used to derive inspection-based VKT estimates and to quantify how activity assumptions affect urban vehicle emission inventories and policy evaluation. By holding vehicle population and emission factors constant across scenarios, we explicitly isolated the effect of activity representation on emission estimates. An inspection-based, age-sensitive VKT framework was further developed to capture within-fleet heterogeneity. The results showed that inspection-derived VKT accounted for only 36–75% of guideline-recommended values across major vehicle categories, with the largest discrepancies observed for diesel freight vehicles. As a result, the use of guideline-based VKT produced higher emission estimates by 34–39% for carbon monoxide (CO) and volatile organic compounds (VOCs) and by approximately 66–67% for nitrogen oxides (NOx) and particulate matter (PM). The influence of activity representation was also evident in policy assessment. In a case study of old diesel vehicle retirement, guideline-based VKT produced estimated emission reduction benefits that were more than 120% higher for most pollutants and nearly 200% higher for NOx than those derived from inspection-based VKT. These findings demonstrate that generalized activity assumptions can substantially affect both emission inventory estimates and policy-oriented assessments. Rather than merely refining a local mileage parameter, this study highlights a potential representativeness limitation of generalized activity assumptions when they are applied to city-specific emission inventories, particularly in medium-sized or geographically constrained urban systems. The inspection-based, age-sensitive approach proposed here provides a practical pathway for improving activity representation in data-rich urban environments, while its transferability should be evaluated according to local fleet structure and transport conditions. Full article

The Multi-Destination Vehicle Routing Problem (MD-VRP) with a heterogeneous electric fleet is a critical challenge in optimizing commuter services for large-scale institutions and logistics operations. To address the complexities of electric fleet composition uncertainty and multi-center routing in “micro-city” campus environments, this paper establishes a robust multi-objective programming model. The model aims to simultaneously minimize three conflicting objectives, the total number of vehicles, total driving distance, and total electric energy consumption (kWh), under constraints of flow conservation and vehicle availability. Considering the nondeterministic polynomial-time hard (NP-hard) nature of the problem, a novel two-stage hybrid heuristic algorithm is proposed. In the first stage, a Modified Kruskal’s algorithm is employed to aggregate scattered stops into optimized clusters to reduce dimensionality. In the second stage, a State-Compressed Dynamic Programming (SC-DP) algorithm is applied to determine the optimal routing and electric vehicle type selection for each cluster. The methodology is validated using a case study of a large-scale campus network with 100 nodes. The optimization results identify an optimal fleet configuration of 41 campus electric commuter vehicles across three specific types (capacities of 45, 55, and 60), resulting in an annual total energy consumption of 5893.98 kWh. Compared with a global Ant Colony Optimization (ACO) baseline in this case study, the proposed framework reduces the required fleet size by 22.6% and annual energy consumption by 9.2%; however, this comparison should be interpreted as a preliminary case-study benchmark because the proposed method adopts a decomposition-based “Cluster-First, Route-Second” strategy. The results indicate that the approach achieves higher solution efficiency, offering an economically and environmentally friendly scheme for electric vehicle fleet operations. Full article

The rapid electrification of road transport has increased interest in distributed energy strategies that reduce grid demand and support decarbonization. Vehicle-integrated photovoltaics (VIPV), including vehicle-applied photovoltaic configurations (VAPV), can generate electricity directly on the vehicle. This systematic review examines peer-reviewed VIPV literature published between 2015 and 2026, focusing on the distinction between theoretical photovoltaic generation and practically usable energy. A Scopus search conducted on 2 May 2026 identified 196 records, of which 88 studies were included after screening against predefined criteria. Due to heterogeneity in vehicle types, climates, technologies, modeling assumptions, and reported metrics, no meta-analysis was performed. Instead, the review applies a multi-layered framework covering climate, geometry, thermal effects, electrical mismatch, battery state-of-charge interactions, fleet-scale modeling, economics, and life-cycle implications. The evidence shows that VIPV is technically feasible and can deliver measurable energy yields, especially in high-irradiance regions and vehicles with favorable daytime parking exposure. However, useful contribution depends strongly on curvature losses, dynamic shading, electrical configuration, SOC limits, charging behavior, seasonality, and vehicle energy demand. Therefore, VIPV is best understood as a context-dependent supplementary energy strategy rather than a transformative standalone solution. Its strongest value lies in specific vehicle classes, climates, and usage patterns where on-board generation can reduce charging demand, support operational resilience, or improve distributed self-consumption. The review also proposes minimum reporting requirements for future studies, including annual energy yield, Wh/km contribution, PV area or capacity, mileage assumptions, SOC modeling, and curtailment treatment. The review was not formally registered, and no formal risk-of-bias or certainty assessment was applied. Full article

Publicly available electricity datasets from operational industrial facilities remain limited due to instrumentation cost, retrofit complexity, and data governance constraints. This paper presents an openly accessible dataset of asset-level electrical energy measurements collected from a medium-scale industrial manufacturing facility over an approximately one-year observation window, with staged commissioning resulting in heterogeneous temporal coverage. The dataset includes time-series measurements from production machinery, auxiliary systems, and distribution-level assets instrumented using a heterogeneous fleet of Ethernet and RS-485 energy meters integrated via industrial gateways and programmable logic controllers. Measurements were acquired via a SCADA-based logging infrastructure and exported from an operational SQL historian. The publicly released dataset comprises fixed 15 min aggregated energy and power metrics derived from high-frequency SCADA telemetry. In its released ALL-phase representation, the dataset comprises measurements from 43 monitored assets and 1,039,873 15 min windows, corresponding to 2.96 GWh of measured electrical energy. Mean window-level data coverage is 99.99%, and 97.72% of ALL-phase windows satisfy the dataset’s reliability criterion. Interval records include energy consumption, demand, data coverage metrics, and reliability indicators. The dataset reflects real-world industrial monitoring conditions, including mixed communication pathways and irregular sampling behaviour, and is intended to support research in industrial energy analytics, data quality assessment, load profiling, and operational energy modelling. Full article

The rapid expansion of air cargo transportation has necessitated fleet expansion to meet growing demand. Due to the high capital costs associated with new aircraft acquisitions, attention has increasingly shifted toward used aircraft as a cost-effective alternative. However, selecting an appropriate used aircraft from a range of heterogeneous options is a critical multi-criteria decision-making challenge. To address this issue, this study introduces an integrated decision-making framework for used aircraft selection by combining the technique for order preference by similarity to ideal solution (TOPSIS) and the best–worst method (BWM) in a hesitant fuzzy environment. First, in response to the limitations of existing distance measures, a novel distance measure for hesitant fuzzy sets (HFSs) is proposed that explicitly incorporates the hesitation degree to better capture uncertainty. Subsequently, this measure is incorporated into a modified hesitant fuzzy TOPSIS (M-HFTOPSIS) to enable a more precise evaluation of alternatives. The hesitant fuzzy BWM (HFBWM) is employed to calculate criteria weights, and the proposed M-HFTOPSIS is used to rank the alternatives. A case study involving ten criteria from technical, economic, and environmental perspectives is conducted to validate the effectiveness of the proposed method. Comparative results demonstrate that the proposed approach provides reasonable and reliable outcomes and that the enhanced HFS distance measure effectively models the differences between hesitant fuzzy sets. Full article

As complex agricultural machinery, traditional combine harvesters face numerous challenges during operation due to their reliance on manual observation. To meet the demands of modern agriculture, intelligent combine harvesters have emerged. Intelligent sensing uses multi-sensor fusion and deep learning to monitor crop lodging, feed rate, loss rate, and impurity content. Under suboptimal conditions, multi-source fusion strategies improve perception reliability. Information processing and decision-making enable dynamic optimization of operational parameters and reduce harvest losses. Multi-machine coordination transforms single-machine operations into fleet control, while remote monitoring leverages a cloud edge collaboration architecture to enable status visualization, remote control, and predictive maintenance for faults. Unmanned operations utilize high-precision positioning and intelligent path planning to improve fleet efficiency and field coverage. However, the field still faces common challenges, including insufficient real-time processing capabilities for multi-source heterogeneous data, poor adaptability to complex agronomic scenarios, and limited economic feasibility. In this review, we examine the complete technology chain, which includes intelligent perception, intelligent decision-making and coordination, remote monitoring, and unmanned operations. We conduct a comparative analysis of the current state of these systems and the challenges they face, providing a systematic reference for future research and industrial applications. Full article

High-Mix Low-Volume (HMLV) manufacturing increasingly relies on heterogeneous robot fleets, but automatic generation of vendor-specific robot control code remains difficult due to platform fragmentation and safety-critical feasibility constraints. Although recent Large Language Model (LLM)-based approaches have shown promise for translating natural language into robot programs, they remain largely limited to single-platform or simulation-oriented settings and are vulnerable to physical hallucination, including spatially inconsistent commands and dynamically infeasible motions. This paper proposes a Digital Twin-integrated verification framework for adaptive control code generation in heterogeneous robot systems. The framework uses a structured intermediate task representation to support runtime spatial grounding, robot selection, pre-execution dynamics validation, and adaptive motion scaling before vendor-specific code generation and execution. Evaluation on 170 task-description scenarios and eight robot selection tasks showed improved ranking discriminability in lightweight stress cases where conventional baselines exhibited limited separation. In addition, adaptive dynamics scaling enabled safe execution in all analytically verified test cases, compared with 50% without scaling. These results suggest that Digital Twin-grounded verification and adaptive feasibility control can improve the reliability of LLM-based multi-vendor robot programming and help mitigate physical hallucination in heterogeneous robot systems. Full article

The NP-hard coordination of heterogeneous robots for time-windowed warehouse tasks remains challenging: metaheuristics are precise but slow, whereas neural methods cannot handle heterogeneous constraints, leading to infeasible allocations. This paper presents the Constraint-Aware Heterogeneous Transformer (CAHT), a lightweight encoder–decoder architecture that performs end-to-end task assignment and sequencing in a single forward pass. The central innovation is a dynamic feasibility masking mechanism that enforces capacity and energy constraints directly within the softmax computation, eliminating infeasible allocations at the architectural level. This is complemented by a spatial-bias Transformer encoder and a two-stage supervised–reinforcement learning training paradigm using ALNS-generated labels. Experiments across four problem scales (5–20 robots, 50–200 tasks) demonstrate that CAHT achieves objective values within 7–13% of the ALNS reference while being 29–91× faster (23–104 ms vs. 2–3 s). Constraint violation rates remain below 6%, with time-window satisfaction above 94%. Ablation analysis identifies dynamic masking as the dominant contribution (+213% degradation upon removal), and cross-scale generalization reveals that the optimality gap decreases from 13.0% to 10.7% as the problem scale grows. With only 0.91 M parameters, CAHT occupies a new trade-off point on the Pareto frontier, offering a practical path toward real-time autonomous warehouse coordination. Full article

Efficient task allocation and coordination are critical for heterogeneous multi-agent systems operating in dynamic field environments. This paper presents a closed-loop framework that integrates Large Language Models (LLMs) with graph-based optimisation to enable end-to-end task decomposition, allocation, and adaptive execution. High-level task scripts are initially parsed by an LLM into structured execution flows, which are transformed into Directed Acyclic Graphs (DAGs) capturing action-level dependencies. A Genetic Algorithm (GA) then optimises agent-to-task assignments by minimising makespan under capability and battery constraints. To ensure robustness, the framework incorporates an LLM-driven recovery module that enables localised replanning under execution failures without interrupting unaffected agents. System-level experiments in a high-fidelity agroforestry simulation demonstrate a 37% increase ($p<0.001$) in harvesting productivity and a 19% reduction in human idle time compared to manual baselines. Under mid-execution failures, the system maintains significantly higher performance, with replanning latencies averaging 24 s. The framework scales to large fleets (up to 1000 agents) and effectively enhances human–robot collaboration through structured, dependency-aware coordination. Full article