In some small-scale powder metallurgy scenarios, uneven powder distribution during discharging due to the lack of a distributor affects the density and uniformity of green compacts in subsequent pressing, thereby impacting product quality and performance. To address this issue, a non-powered distributor with a two-stage distributor was designed. The influence of structural parameters on distribution uniformity was investigated by improving the distributor structure and establishing a discrete element simulation model, using material surface accumulation, particle diameter distribution, and porosity as evaluation indicators. The improvement effect of the distributor on powder distribution uniformity was verified by comparing working conditions with and without the distributor. Results indicated that adding the distributor effectively improved uneven particle diameter distribution, with optimal uniformity achieved when the sliding arm inclination angle was 20 degrees To further verify applicability, the 30 degrees distributor, 25 degrees distributor and 20 degrees distributor were used to conduct distribution experiments. Compared with the condition without a distributor, the 20 degrees distributor improved powder distribution uniformity by 33.33%, resulting in more uniform spatial distribution of particle diameters and stress in the mixture.

As air traffic demand continues to grow, airport ground movement optimisation faces increasing challenges related to safety and environmental concerns, necessitating robust and flexible optimisation methods. This paper proposes a multi-objective chance-constrained programming model that explicitly incorporates uncertainties in taxiing time and fuel consumption using a Monte Carlo simulation method based on the Follow-the-Greens guidance system. Building on this model, novel airport multi-objective A* based on chance-constrained programming (CCP-AMOA*) algorithm is developed to sequentially allocate robust, Pareto-optimal taxi routes through three key modules: candidate route generation, route selection, and time window updates. In the route selection, varying confidence levels for the two objectives control the level of conservatism under uncertainties, while preferences allow flexible prioritisation, enabling context-aware decision making. Benchmark experiments at Manchester Airport reveal that uncertainties affect different parts of the taxiway network unevenly. Comparative analyses show that several proposed CCP-AMOA* algorithms outperform deterministic baselines by generating flimsily robust efficient solutions, reducing economic costs, and minimising total delay. Further results indicate that adjusting confidence levels significantly influences the trade-off between taxiing time and fuel consumption, with longer routes providing enhanced robustness via greater temporal and spatial buffers.

As demand for high-speed telecommunication increases, automating the design of fibre networks is critical for minimising deployment costs and ensuring consistent service. However, existing search strategies within British Telecom’s (BT) design software struggle to consistently locate near-global optima as network scale increases, and the hard-coded definition of network domains limits flexibility for non-developers. To address these challenges, this paper proposes a novel framework with two key contributions: a Prolog Advisory System (PAS) for modular domain definition and a sequence-based selection hyper-heuristic for enhanced optimisation. The PAS utilises logic programming to decouple constraint checking and cost calculation from the core framework, enabling rapid domain modification. For optimisation, we introduce intelligent move sequencing using Luby-based and Hidden Markov Model approaches, augmented by a continuous pivot strategy that increases the exploration of constraint-breaking moves. Experimental results on real-world case studies illustrate the robustness and effectiveness of our method to obtain high-quality solutions, regardless of the size of the network. Specifically, on large-scale instances, the proposed method outperforms BT’s current approach, reducing average design cost by approximately 3.8% and decreasing the required evaluations by up to 65%. This work offers vital efficiency gains for BT’s multi-billion pound fibre roll-out program, one of the UK’s largest infrastructure investments, where even small design improvements yield substantial financial savings.

Algorithm performance in combinatorial optimization is highly sensitive to parameter settings, while a single globally tuned configuration often fails to exploit the heterogeneity of instances. This limitation is particularly evident in the Electric Capacitated Vehicle Routing Problem, where instances differ in structure, demand patterns, and energy constraints. This paper investigates instance-aware parameter configuration for Bilevel Late Acceptance Hill Climbing, a state-of-the-art metaheuristic for the Electric Capacitated Vehicle Routing Problem. An offline tuning procedure is used to obtain instance-specific parameter labels, which are then mapped from instance features via a regression model to enable parameter prediction for unseen instances prior to execution. Experimental results on the IEEE WCCI 2020 benchmark and its extensions show that the proposed approach achieves an average objective value reduction of 0.28% across eight held-out test instances relative to a globally tuned configuration. This corresponds to a significant cost reduction in multimillion-dollar transportation operations.

Airport ground movement (AGM) is a critical component of air traffic management, connecting airside and airspace operations, with significant implications for efficiency, safety, and sustainability. With traffic volumes only set to grow in the coming years, the demand for robust decision support systems has intensified, particularly under the uncertainty introduced by human behaviour, environmental variability, and system complexity. While traditional optimisation methods rooted in operational research have provided strong foundations for routing and scheduling in AGM operations, they struggle to adapt to the dynamic and uncertain environments present in real-world problems. In parallel, advances in surveillance, guidance, and sensing technologies have enabled the widespread collection of historical and real-time data, creating opportunities for adaptive, data-driven approaches. This review traces the evolution of AGM decision support, contrasting knowledge-based and data-driven frameworks, evaluating their strengths and limitations in managing uncertainties. Particular emphasis is placed on human factors and uncertainty management, distinguishing between proactive planning approaches that incorporate uncertainty, and reactive approaches that respond to real-time disturbances. Future research opportunities and challenges lie in enhancing human-machine interaction, accelerating algorithmic speed for real-time decision making, and developing generalisable, data-augmented and explainable frameworks through interdisciplinary collaboration.

This paper tackles the Electric Capacitated Vehicle Routing Problem (E-CVRP) through a bilevel optimization framework that handles routing and charging decisions separately or jointly depending on the search stage. By analyzing their interaction, we introduce a surrogate objective at the upper level to guide the search and accelerate convergence. A bilevel Late Acceptance Hill Climbing algorithm (b-LAHC) is introduced that operates through three phases: greedy descent, neighborhood exploration, and final solution refinement. b-LAHC operates with fixed parameters, eliminating the need for complex adaptation while remaining lightweight and effective. Extensive experiments on the IEEE WCCI-2020 benchmark show that b-LAHC achieves superior or competitive performance against eight state-of-the-art algorithms. Under a fixed evaluation budget, it attains near-optimal solutions on small-scale instances and sets 9/10 new best-known results on large-scale benchmarks, improving existing records by an average of 1.07

The multi-objective multigraph Shortest Path Problem (SPP) is intractable, necessitating efficient solution approaches. To address general multi-objective multigraph SPPs, this article introduces a Multi-Objective Multi-Graph A* (MOMGA*) algorithm and develops a learning-based heuristic function to expedite the search. MOMGA* generalises the Airport Multi-Objective A* (AMOA*), which was designed for a specific application on multigraphs, and further modifies its path selection and expansion procedures. Theoretical analysis demonstrates that the modifications in MOMGA* yield advantages over AMOA*, including higher search efficiency, more effective use of admissible heuristics for accelerating search, and seamless integration with likely-admissible heuristics without sacrificing solution quality. The admissibility proof of MOMGA* is also provided. The developed heuristic function is likely-admissible. It embraces node embedding techniques to extract node characteristics, based on which shortest path costs (heuristics) for every two nodes are estimated through neural networks. In particular, we present an extensive review of walk-based shallow embedding methods and experimentally validate their superior ability in capturing the characteristics of nodes for accurately predicting heuristics. Evaluation based on randomly generated multi-objective multigraphs confirms: (i) MOMGA* comprehensively outperforms AMOA*, consistent with the theoretical analysis; (ii) walk-based sampling for node embeddings is key to preserving distance-related information in graphs; (iii) the proposed likely-admissible heuristics, even learnt with a limited amount of training data, can empower MOMGA* to efficiently obtain a collection of optimal and near-optimal solutions; and (iv) a good balance between optimality and tractability in MOMGA* is controllable by tuning the predictive accuracy of learning heuristics.

This paper investigates heuristic initialisation for shortest path search on multi-objective multigraphs using genetic algorithms. An initialised solution comprises a node path and an edge path. The state-of-the-art initialisation method encodes node paths into random-key node priority sequences and assigns random values to represent the indices of traversed parallel edges, where the node priority is a combination of hop count and a random value. However, this method has three limitations. (i) Hop counts are computed using Dijkstra’s algorithm, incurring non-negligible computational cost. (ii) The upper bound of the random values added to hop counts is defined by a hyperparameter, requiring tedious parameter tuning. (iii) Heuristic information about edges is ignored, despite its potential to improve solution quality. To address these issues, this paper first eliminates the randomisation hyperparameter and constrains randomness to the range [0, 1). Second, a heuristic edge initialisation method is proposed. Finally, to reduce the computational burden of Dijkstra’s algorithm, a learning-based node priority generation method is devised using neural networks trained on node embeddings. Experiments on benchmark multi-objective multigraphs show that (i) combining hop counts with randomness in [0, 1) and heuristic edge initialisation outperforms the state-of-the-art method, and (ii) with limited training data, the learning-based node priorities surpass entirely random node priorities and are comparable to state-of-the-art performance, highlighting their potential in large-scale and dynamic scenarios.

Airport airside operations, including gate assignment, ground movements and runway scheduling, are under increasing pressure from the recovery of air traffic demand since COVID-19. Most existing studies integrate airside operations hierarchically, losing the chance to further collaborate on airside resources. Aiming to address the problem from the systematic perspective, this paper integrates the airside operations in a non-hierarchical way, where the operations on gate, taxiway, and runway are optimised without preference. The proposed mixed integer programming model shows the interaction between the airside operations. And adaptive large neighbourhood search algorithm is applied as the solution method. The proposed removal/insertion operators and deconflicting procedure are imperative in addressing airside conflicts. Real world cases from the Nanjing Lukou International Airport are used for numerical experiments. Results illustrate the proposed approach outperforms existing hierarchical methods in terms of reducing airside conflicts and delays, without sacrificing gate and runway utilisation.

Airspace complexity is defined as an essential indicator to comprehensively measure the safety of air traffic operational situations. A reliable prediction of airspace complexity can provide practical guidance for formulating air traffic management strategies and resource allocation. Although extensive efforts have been devoted to computing airspace complexity, previous studies can rarely model the multi-dimensional and combined spatio-temporal features within airspace complexity data. In this paper, we propose a multimodal adaptive spatio-temporal graph neural network to simultaneously explore the spatio-temporal dependencies in the airspace sector network. Specifically, we design a multimodal adaptive graph convolution module to effectively learn the diverse spatial relationships and adaptively adjust the impact of different spatial modes on airspace complexity in a data-driven manner. To model dynamic long-short-term temporal patterns, we develop a dilated causal convolution layer with a multiple-time-step self-attention mechanism to accurately predict airspace complexity over a longer time horizon. Extensive experiments on real-world air traffic datasets show that the proposed approach can harness differing spatial modes in achieving higher generalization performance across different temporal patterns, outperforming state-of-the-art methods in all prediction time horizons.

In the pursuit of cost-effective services, the telecommunication industry faces increasing and intense competition, with tight constraints on budgets. The automation and optimisation of network design becomes crucial to minimise the overall cost of service deployment. BT NetDesign is a tool developed by British Telecom (BT) to reduce the capital expenditure of designing a fibre network, using a single-point heuristic search algorithm. Although NetDesign facilitates different exploration and exploitation moves on a single-point search, the utilisation of these moves in the context of a population, enabling the possibility of interaction between multiple individuals in the population, is a potential alternative. To investigate the performance of population-based search algorithms for network design optimisation, this study utilises two population-based search strategies, namely genetic algorithms (GA) and memetic algorithms (MA). These strategies are rigorously evaluated on network instances of different sizes. Experimental results show that GA and MA cannot reach high quality solutions for large networks compared to an existing Simulated Annealing-based hyper-heuristic approach.

Congestion is considered to be one of the most problematic issues in international airspace. In the twenty-first century, European airports experience many challenges, including those relating to capacity and the environment. Airports and their transportation systems can avoid the occurrence of huge bottleneck problems via appropriate expansion plans or the efficient utilization of existing resources. The investment in infrastructure can help in expanding the airport surface area which leads to increase the capacity of the airports. However, this is expensive in most cases and can lead to harmful effects on the environment, which may lead to noise and pollution as well as increasing the overall complexity of operations which thus add to more workload. Airports work almost closer to their maximum capacity. The continued increase in airport surface area can be difficult and costly, so we argue here that research should focus on finding solutions that use the existing space in more efficient ways rather. Decision support systems, planning and scheduling have to be increasingly and continuously improved and advanced. Improving the efficiency of the airports is considered to be one of the most important issues of aircraft ground management, because it is a link to all other ground operations which involves the coordination of machines and humans. The attempt to taxi an aircraft in an optimal and efficient manner using automatic systems are increasingly being deployed across major airports across the world. However, many of these systems do not use adequate aircraft models, nor do they continuously seek to optimize objective functions such as minimizing fuel consumption and minimizing harmful greenhouse gas emissions. In this paper, a new approach is proposed for optimal taxiing navigation of a high-fidelity aircraft model. A routing and scheduling algorithm that determines the waypoints, taxi route and time deadline is conjunct to work with the new approach. The proposed approach, which integrated a MATLAB-Simulink model of the BOEING-747 aircraft with Artificial Intelligence (AI) base control successful generates fuel-efficient 4DTs in real time, while taking constraints on operations into account.

Urban air mobility (UAM) is an emerging transport mode, offering on-demand and automated air tran-sit of passengers or cargo within urban ecosystems. Unrestricted by terrestrial road networks, UAM provides a timely and efficient way of transportation. Due to its profound significance, industries, regulatory authorities, and academic researchers have made extensive efforts toward its development and improvement. This article contrib-utes to these ongoing endeavors by providing a comprehensive and systematic review of existing literature on UAM from the perspective of airspace and traffic management. Specifically, this article chronologically encapsulates the development history of UAM, aiming to foster a holistic understanding of the contributions made by industries, aca-demics, and regulatory authorities. It also highlights specific advances, such as the integration of AI in traffic f low algorithms and the development of collision avoidance systems, addressing current technological and regulatory hurdles. Furthermore, this article provides an in-depth review of the studies on the topic of urban air transportation network design, urban air traffic f low management, and urban f light safety management. The diverse scenarios and methodologies employed across those studies are discussed and compared, identifying gaps in current research and potential for future exploration. Finally, this article highlights opportunities and prospective directions for future research in relation to traffic management strategies, thus propelling UAM toward greater efficiency.This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

Efficient airport airside ground movement (AAGM) is key to successful operations of urban air mobility. Recent studies have introduced the use of multi-objective multigraphs (MOMGs) as the conceptual prototype to formulate AAGM. Swift calculation of the shortest path costs is crucial for the algorithmic heuristic search on MOMGs, however, previous work chiefly focused on single-objective simple graphs (SOSGs), treated cost enquires as search problems, and failed to keep a low level of computational time and storage complexity. This paper concentrates on the conceptual prototype MOMG, and investigates its node feature extraction, which lays the foundation for efficient prediction of shortest path costs. Two extraction methods are implemented and compared: a statistics-based method that summarises 22 node physical patterns from graph theory principles, and a learning-based method that employs node embedding technique to encode graph structures into a discriminative vector space. The former method can effectively evaluate the node physical patterns and reveals their individual importance for distance prediction, while the latter provides novel practices on processing multigraphs for node embedding algorithms that can merely handle SOSGs. Three regression models are applied to predict the shortest path costs to demonstrate the performance of each. Our experiments on randomly generated benchmark MOMGs show that (i) the statistics-based method underperforms on characterising small distance values due to severe overestimation, (ii) a subset of essential physical patterns can achieve comparable or slightly better prediction accuracy than that based on a complete set of patterns, and (iii) the learning-based method consistently outperforms the statistics-based method, while maintaining a competitive level of computational complexity.

Due to the inherent nature of being highly digitalized, networked and intelligent, Unmanned Aerial System (UAS) operations pose a huge challenge to traditional aviation regulation and technical systems. How to keep safe, efficient and integrated operation for different Airspace users has become a pressing issue faced by civil aviation around the world. This paper focuses on the main operational scenarios and characteristics of unmanned aviation development in China. New operational characteristics and associated challenges due to diverse low-altitude users are analyzed, including operation concepts, UAS traffic management, technological test and verification, and standards. Drawing light on the practices in Europe and the United States, this paper summarizes China's practices and progress in low-altitude operations management, and analyzes future technological development needs and trends, as well as feasible implementation pathways and measures based on actual needs.

Routing and scheduling problems with increasingly realistic modeling approaches often entail the consideration of multiple objectives, time constraints, and modeling the system as a multigraph. This detailed modeling approach has increased computational complexity and may also lead to violation of the additivity property of the costs. In the worst scenario, increased complexity makes the problem intractable for exact algorithms. Even when the problem is solvable, exact algorithms may not provide solutions within the given time budget, and the found solutions are not guaranteed to be optimal due to the additivity property violation. Approximate solution methods become more suitable in this case. This article focuses on one particular real-world application, the Airport Ground Movement Problem, where both time constraints and parallel arcs are involved. We introduce a novel memetic algorithm for routing in multigraphs with time constraints (MARMT) and present a comprehensive study of its different variants based on diverse genetic representation methods. We propose a local search operator that enhances search efficiency and effectiveness. MARMT is tested on real data based on two airports of different sizes. Our results show that MARMT does not suffer from the nonadditivity property problem as it outperforms the state-of-the-art exact algorithm when allowed to converge. When a time budget of 10 s is imposed on MARMT, it is able to provide solutions with quality comparable (within 1%-5% degradation) to the ones given by the exact algorithm with respect to the aggregated objective values. MARMT can be adapted for other applications, such as train operations.

A routing and scheduling optimization approach for the airport ground movement problem considering runway spacing is introduced. An integrated modeling that considers both the routing of aircraft and runway required separations, is implemented through Aircraft Multi-Objective Optimization Algorithm AMOA* and a correct spacing validation module, coupled by a genetic algorithm in search of real-world feasible, yet optimized solutions, for a modern-day aviation setting based on London's Stansted Airport. The proposed genetic algorithms successfully optimize taxiing time and fuel consumption for different airport traffic scenarios while fully respecting runway separation constraints. The difference between algorithms is emphasized to stress the risk of over-evaluation of savings by overlooking real-world operational conditions in the modeling phase of the problem.

Airspace complexity is a paramount safety metric to measure the difficulty and effort required to safely manage air traffic. The continuing growth in air traffic demand results in increasing airspace complexity and unprecedented safety concerns. Most existing methods treat the minimization of airspace complexity as the sole objective, ignoring the path deviation cost induced by the re-scheduled aircraft. In this paper, regarding reduction of airspace complexity and path deviation cost as two conflicting objectives, a multi-objective airspace complexity mitigation model is proposed to simultaneously ensure the safety and efficiency of air transport by optimizing flight trajectories. To effectively solve this multi-objective and non-linear optimization problem, a novel Memetic Algorithm with Adaptive Local Search (called MA-ALS) is developed. Specifically, we design a new crossover and three new local search operators under the flight trajectory representation. MA-ALS conducts exploration by crossover, and exploitation by a hill-climbing local search process. Moreover, we proposed an adaptive local search selection mechanism which facilitates the dynamic collaboration of different local search operators during evolution. A comprehensive comparison with the most recently developed algorithms on Chinese air traffic dataset is conducted. The Pareto front generated by the proposed algorithm dominates that of the compared baselines. Moreover, compared with a real flight schedule, the flight plan obtained by the proposed algorithm can significantly reduce the airspace complexity.

Airports and their related operations have become the major bottlenecks to the entire air traffic management system, raising predictability, safety, and environmental concerns. One of the underpinning techniques for digital and sustainable air transport is airport ground movement optimization. Currently, real ground movement data is made freely available for the majority of aircraft at many airports. However, the recorded data is not accurate enough due to measurement errors and general uncertainties. In this article, we aim to develop a new interval type-2 fuzzy logic-based map matching algorithm, which can match each raw data point to the correct airport segment. To this aim, we first specifically design a set of interval type-2 Sugeno fuzzy rules and their associated rule weights, as well as the model output, based on preliminary experiments and sensitivity tests. Then, the fuzzy membership functions are fine-tuned by a particle swarm optimization algorithm. Moreover, an extra checking step using the available data is further integrated to improve map matching accuracy. Using the real-world aircraft movement data at Hong Kong airport, we compared the developed algorithm with other well known map matching algorithms. Experimental results show that the designed interval type-2 fuzzy rules have the potential to handle map matching uncertainties, and the extra checking step can effectively improve map matching accuracy. The proposed algorithm is demonstrated to be robust and achieve the best map matching accuracy of over 96% without compromising the run time.

Affected by the nondeterministic nature of flight trajectories, the external environment, and airport operational conditions, the prediction of the estimated time of arrival (ETA) is one of the most challenging tasks for air traffic control in the terminal maneuvering area (TMA). Previous studies lack adequate utilization of the spatial and temporal behaviors embedded in continuous trajectories. We propose a novel spatiotemporal neural network model for estimating the time of arrival (STNN-ETA), which consists of three components: 1) trajectory pattern recognition, which classifies historical trajectories into several patterns/clusters; 2) trajectory prediction, which predicts a target flight’s subsequent positions based on trajectory pattern matching; and 3) arrival time prediction, in which nonlinear function and recurrent units are adopted to capture spatiotemporal features for prediction purposes. In the proposed model, we also utilize spatial and temporal attention mechanisms to focus on important features from radar echo maps and trajectory series, respectively, and suppress unnecessary ones for ETA prediction. To validate the effectiveness of the proposed method, we apply it to predict the ETA of flights within the Beijing TMA. Extensive experiments show that STNN-ETA outperforms the state-of-the-art models in terms of the mean absolute error.