Search Results (232)

As global shipping expands, Automated Container Terminals (ACTs) are vital for port competitiveness. However, modern three-stage yard layouts often suffer from spatio-temporal conflicts between dual yard cranes during relay operations, while uncoordinated container placement causes localized overloads and safety hazards. To address these issues, this study proposes a multi-objective mixed-integer linear programming (MILP) model integrating three-stage operations with spatio-temporal mutual exclusion constraints. The model minimizes makespan, external truck waiting time, and inventory disparities across landside bays. To solve this NP-hard problem, an Improved Octopus Optimization Algorithm (IOOA) is developed, featuring discrete space mapping, Euclidean-based state determination, integer flight steps, and local fine-tuning. Numerical experiments demonstrate that this approach significantly reduces the total makespan and truck waiting times while ensuring a highly uniform container distribution across bays. Ultimately, this study mitigates safety risks associated with space overloads and isolated stack collapses, providing a robust decision-making framework to enhance the efficiency and safety of next-generation ACTs. Full article

UAV-assisted data collection often suffers from spatial data holes and communication unfairness, a challenge exacerbated in Wireless Powered Communication Networks (WPCNs) by the inherent doubly near-far problem. To bridge these gaps, this paper proposes a novel Spatio-Temporal Trajectory-Driven Dynamic Time-Division Multiple Access (STD-TDMA) scheduling strategy. Deviating from conventional discrete hovering paradigms, we introduce a continuous-flight framework that exploits the UAV’s mobility to provide seamless spatial coverage. By jointly optimizing the UAV’s flight speed and dynamic time-slot allocation, the proposed strategy ensures that each sensor node can interact with the UAV at its optimal channel condition along the trajectory, thereby effectively mitigating the doubly near-far effect and ensuring quality of service-based fairness. To solve the formulated non-convex optimization problem, we develop a low-complexity algorithm that integrates Binary Search for speed optimization with the Hungarian algorithm for spatio-temporal mapping. Extensive simulations demonstrate that our STD-TDMA strategy significantly enhances nodal fairness and boosts overall task execution efficiency compared to conventional baseline schemes. Full article

This study investigates the Container Consolidation Problem (CCP), a critical operational challenge in container terminals where containers with specific attributes must be relocated during yard crane idle periods. The primary objective is to maximize yard space availability for incoming vessels by strategically grouping containers, thereby alleviating storage pressure and enhancing throughput. A mixed-integer programming model is formulated to minimize the total handling time, incorporating complex constraints related to crane availability, relocation sequencing, and slot assignment. Due to the combinatorial complexity inherent in large-scale yard operations, a comprehensive optimization framework is proposed. This framework balances computational efficiency with solution quality, offering a robust approach to solve large-scale instances within practical time limits. Computational experiments demonstrate that the proposed methodology consistently yields high-quality solutions, effectively resolving the trade-off between solution speed and optimality. The research provides not only a novel methodological perspective for solving this NP-hard problem but also offers significant practical value. By optimizing crane scheduling, the model directly contributes to reducing operational costs, improving the turnover rate of yard space, and strengthening the overall efficiency of the maritime supply chain. Full article

Proportional, fair scheduling in OFDM-based vehicle-to-everything (V2X) uplink causes the resource-block allocation of each vehicle to vary from slot to slot, yet conventional semantic encoders produce a fixed number of output tokens regardless of the instantaneous channel capacity. When the encoder output exceeds the slot budget, transmitted features are truncated and the resulting federated learning gradient is corrupted—a problem that affected 23% of training rounds for non-line-of-sight vehicles in our experiments. The difficulty is worsened by a spatial pattern common in urban deployments: vehicles at congested intersections suffer the poorest propagation conditions while carrying the training data most relevant to safety, and throughput-driven client selection excludes them in favor of vehicles with strong channels but uninformative scenes. We address both issues within a single framework for OFDM-based V2X federated learning. On the transmission side, a Sensing-Guided Adaptive Modulation (SGAM) module derives a per-slot token budget from the current resource-block allocation and selects tokens through differentiable Gumbel-TopK pruning with a hard capacity clip, so the transmitted token count stays within the slot budget. On the scheduling side, a Channel-Decoupled Federated Learning (CDFL) module partitions clients independently by channel quality and data complexity, selects diverse representatives per partition via facility location optimization, and corrects for partition-size imbalance through inverse propensity weighting during model aggregation. Experiments on NuScenes with 20 non-IID vehicular clients under realistic OFDM channel simulation demonstrate a Macro-F1 of 0.710 (+8.7 points over the Oort-adapted baseline), zero budget violations throughout training, and a 75% reduction in training variance; the worst-class F1 more than doubles relative to FedAvg. Full article

This paper proposes a joint optimization algorithm based on reinforcement learning to address the time slot and power allocation problem in underwater acoustic communication networks (UACNs). By maximizing the total capacity of successful transmissions as the optimization objective, two sub-objectives are formulated corresponding to time-slot scheduling and power allocation. The sub-objective corresponding to time-slot scheduling is addressed by constructing a Markov Decision Process (MDP) model based on Deep Q-Network (DQN) learning. In this model, the agent learns the time slot allocation policy with the goal of increasing the number of successfully transmitted links while reducing the collision. For the sub-objective corresponding to power allocation, another MDP model is developed, solved by the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, in which each underwater transmission node acts as an independent agent. The MADDPG approach enables the system to improve channel capacity under energy limitation, which maximizes the total capacity of successfully transmitted links. In terms of model execution, the DQN adopts a centralized training and time slot allocation, while MADDPG uses a centralized training and distributed execution to select the transmission power by each node. Simulation results show that the proposed joint optimization algorithm demonstrates better performance in the number of successfully transmitted links and channel capacity compared to TDMA, Slotted ALOHA, and other algorithms. Full article

As mobile networks evolve toward Beyond 5G and 6G architectures, energy efficiency and sustainability have become increasingly critical due to growing traffic volumes, denser base station deployments, and the rising number of connected devices. Supporting Ultra-Reliable Low-Latency Communication (URLLC) services is particularly challenging, as their stringent requirements for both high reliability and minimal latency can lead to a significant increase in energy consumption within the radio access network. This paper examines slot structure mechanisms for concurrently servicing URLLC and enhanced Mobile Broadband (eMBB) traffic within the 5G Advanced framework, with a focus on improving energy efficiency and optimizing radio resource utilization. We propose an adaptive algorithm for managing radio interface time resources, which dynamically allocates sub-slots based on current network load and radio channel conditions. The system model is implemented in Simulink and incorporates URLLC and eMBB traffic generation, signal-to-noise ratio estimation, and a priority-based scheduling mechanism. Simulation results demonstrate that the proposed approach meets URLLC latency and reliability requirements while reducing redundant transmissions and enhancing the energy efficiency of the radio access network. These findings position the proposed method as a promising solution for the design of energy-efficient, next-generation mobile networks. Full article

Digital calendars are interactive representations of time that shape both scheduling outcomes and the micro-process of searching, verifying, and revising candidate placements. We examine calendar horizon—whether weekend time is visible in the default week view—as a boundary affordance in scheduling interfaces. Using eye tracking and interaction logs, we model each scheduling episode as a sequence of placement attempts and align gaze to each attempt, partitioning it into Early/Mid/Late phases and summarizing attention across structural AOIs (task panel, calendar grid, and the weekend column when present). Two experiments used drag-and-drop and dropdown slot-picking; weekend visibility was manipulated within the dropdown interface, while evening slots remained available. Across 105 participants (1018 task episodes), AttemptsCount ranged from 1 to 7. AttemptsCount predicted gaze-based process cost: each additional attempt corresponded to ~56% more total fixation duration. Personal tasks required more attempts than work tasks and elicited stronger Late-phase weekend verification when the weekend was visible. Horizon cues also shifted boundary outcomes: hiding the weekend reduced weekend placements and increased reliance on evening scheduling, indicating displacement into adjacent time regions. These findings position calendar horizon as a design lever that shapes both process (verification) and outcomes (boundary placements), with implications for calendar UIs and mixed-initiative scheduling tools. Full article

As an indispensable component of space–terrestrial integrated networks, low-Earth orbit (LEO) satellite networks are capable of providing flexible access and low delay communication services for emerging time-sensitive traffic. However, the inconsistent transmission rates between intra-satellite wired links and inter-satellite wireless links will undoubtedly result in unstable delay at the satellites. This disparity poses a challenge to ensuring deterministic communication for time-sensitive traffic. Aiming at this problem, we put forward an integrated scheduling algorithm based on matching game theory to concurrently determine the positions of wired and wireless time slots. First, we establish a theoretical model to quantify the influence of integrated scheduling on deterministic communication by elucidating the interrelationships among time-sensitive traffic, wired time slots, and wireless time slots. Second, drawing inspiration from scheduling sequences and matching game theory, the established integrated scheduling model is reformulated into a cyclic three-sided matching game model. Third, we design an integrated scheduling algorithm (ISA) to derive scheduling optimization solutions. Experimental results demonstrate that the proposed algorithm ISA outperforms existing scheduling algorithms, achieving an average delay reduction of 16.6% over all comparison algorithms. Full article

The efficient use of resources represents a critical challenge for public healthcare systems facing increasing demand. In this study, an operational analysis was conducted at Hospital del Mar (Barcelona) to demonstrate that persistent bottlenecks and capacity deficits are primarily organizational and not only quantitative. Through a prospective observational study and exploratory data analysis (EDA), it was identified that high apparent workloads often coexist with structural inefficiencies, particularly regarding the unpredictable demand of urgent and inpatient procedures. To address these gaps, a Mixed-Integer Linear Programming (MILP) model was implemented to optimize spatial and temporal resource allocation. Unlike reactive scheduling, this data-driven approach explicitly incorporates capacity reserves for non-programmable activities and ensures realistic time slots without increasing physical or human resources. It is shown that MILP-optimized scheduling significantly balances workload, eliminates artificial overlaps, and improves room utilization—reaching rates of 99.5%. The findings highlight that temporal agenda design constitutes a critical, yet underutilized, lever for hospital management. A scalable tool for evidence-based decision-making is provided by this framework, allowing for a clear distinction between apparent productivity and real efficiency. The proposed model is considered highly transferable to other clinical settings facing similar operational constraints. Full article

Space–Air–Ground–Sea Integrated Networks (SAGSINs) are emerging as a key enabling architecture for broadband maritime connectivity, where heterogeneous access tiers (shore, aerial, and satellite) jointly support delay-sensitive and mission-critical uplink traffic such as alarms, telemetry, and surveillance video. However, uplink resource scheduling in maritime SAGSINs remains challenging due to time-varying channels, locally bursty traffic, and intense contention, while centralized optimization is ill-suited, as global information collection is often delayed, incomplete, and inconsistent over long-haul maritime links. Therefore, this paper investigates distributed uplink scheduling in maritime SAGSINs, where maritime nodes jointly select the access tier, spectrum slice, and transmit power under interference, spectrum, deadline, and energy constraints. Specifically, we formulate the uplink resource scheduling as a cumulative value of information (VoI) maximization problem, and develop a game-theoretic distributed multi-agent reinforcement learning algorithm, termed GTMARL. Therein, maritime nodes learn transmission policies from local observations, coordinated through congestion prices broadcast by access nodes. These prices are derived from Lagrangian relaxation and act as coordination signals that align individual decisions with global objectives. To ensure stable operation, a two-timescale mechanism is adopted, where maritime nodes make fast slot-level transmission decisions, while access nodes adapt and broadcast congestion prices on a slower timescale. Extensive experiments demonstrate that GTMARL achieves up to 90% of the idealized upper bound, significantly outperforming baselines in deadline satisfaction, throughput, delay, energy efficiency and fairness under varying traffic loads and network densities. Full article

Future wireless networks require efficient device-to-device (D2D) communication to meet the demands of increasing connectivity; however, practical challenges such as limited coverage and severe interference persist. This paper addresses these issues by employing simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) equipped with low-resolution phase shifters, thereby enabling full-space coverage while conforming to hardware constraints. To further improve system performance, we propose an irregular STAR-RIS configuration, in which only a subset of elements is activated to enhance spatial diversity without increasing power consumption. Additionally, we introduce a group scheduling strategy that assigns users to different time slots, effectively mitigating interference and improving the overall sum rate. To solve the resulting high-dimensional and non-convex optimization problem, we develop a cross-entropy optimization framework that jointly optimizes element selection, amplitude and phase configurations, and user scheduling. Simulation results demonstrate that the proposed design significantly outperforms existing benchmarks in terms of both the sum rate and scalability, thus providing a practical and efficient solution for STAR-RIS-assisted D2D communication systems. Full article

Ad hoc cognitive radio-enabled Industrial Internet of Things (CR-IIoT) networks offer dynamic spectrum access (DSA) to mitigate the spectrum shortage in wireless communication. However, spectrum utilization is limited by the spectrum availability and resource constraints. In the ad hoc CR-IIoT context, this challenge is further complicated by bandwidth fragmentation arising from small IIoT packet transmissions within primary user (PU) slots. For resource-constrained ad hoc CR-IIoT networks, a medium access control (MAC) scheme is essential to enable opportunistic channel access with a low computational complexity. This work proposes a lightweight DTDMA-assisted MAC scheme (LDCRM) to minimize the queuing delay and maximize transmission opportunities. LDCRM employs a lightweight channel-selection mechanism, an adaptive minislot duration strategy, and spectrum-energy-aware distributed clustering to optimize both energy and spectrum utilization. DTDMA scheduling was formulated using a multiple knapsack problem (MKP) framework and solved using a greedy heuristic to minimize the queuing delay with a low computational overhead. The simulation results under an ON/OFF PU-sensing model showed that LDCRM outperformed CogLEACH and DPPST achieving up to 89.96% lower queuing delay, maintaining a higher packet delivery ratio (between 58.47 and 92.48%) and achieving near-optimal utilization of the minislot and bandwidth. An experimental evaluation of the clustering stability and fairness indicated a 56.25% extended network lifetime compared to that of E-CogLEACH. These results demonstrate LDCRM’s scalability and robustness for Industry 4.0 deployments. Full article

Directional flying ad hoc networks (FANETs) equipped with phased array antennas are pivotal for applications demanding high-capacity, low-latency communications. While directional beamforming extends the communication range, it necessitates the intricate joint optimization of the beam direction, power, and time-slot scheduling under hardware constraints. Existing resource allocation schemes predominantly follow two paradigms: (i) conventional physical-layer multiple access (CPMA) approaches, which enforce strict orthogonality within each beam and thus limit spatial efficiency; and (ii) advanced physical-layer techniques like rate-splitting multiple access (RSMA), which have been applied to terrestrial and omnidirectional UAV networks but not systematically integrated with the beam-based scheduling constraints of directional FANETs. Consequently, jointly optimizing the beam direction, intra-beam rate-splitting-based node pairing, transmit power, and time-slot scheduling remains largely unexplored. To bridge this gap, this paper introduces an intra-beam rate-splitting-based resource allocation (IBRSRA) framework for directional FANETs. This paper formulates an optimization problem that jointly designs the beam direction, constrained rate-splitting (CRS)-based node pairing, power control, modulation and coding scheme (MCS) selection, and time-slot scheduling, aiming to minimize the total number of time slots required for data transmission. The resulting mixed-integer nonlinear programming (MINLP) problem is solved via a computationally efficient two-stage algorithm, combining greedy scheduling with successive convex approximation (SCA) for non-convex optimization. Simulation results demonstrate that the proposed IBRSRA algorithm substantially enhances spectral efficiency and reduces latency. Specifically, for a network with 16 nodes, IBRSRA reduces the required number of transmission time slots by more than 42% compared to the best-performing baseline scheme. This confirms the significant practical benefit of integrating CRS into the resource allocation design of directional FANETs. Full article

Considering the global objective of carbon emission reduction, this paper focuses on optimizing the operational efficiency of grid-connected electric vehicles (EVs) and promoting sustainable energy integration and thus proposes a novel dual-incentive mechanism combining real-time pricing (RTP) and carbon quotas. A core of this study is the development of a bilevel programming model that effectively captures the strategic interaction between power suppliers (PS) and microgrid (MG) users. At the upper level, the model enables the PS to optimize electricity prices, achieving both revenue maximization and grid balance maintenance; at the lower level, it supports MGs in rational scheduling of EV charging/discharging, photovoltaic and wind energy (PWE) utilization, and load consumption, ensuring the fulfillment of user demands while maximizing MG profits. To address the non-convex factors in the model that hinder an efficient solution, another key is the design of a bilevel distributed genetic algorithm, which realizes efficient decentralized decision making and provides technical support for the practical application of the model. Through comprehensive simulations, the study verifies significant quantitative outcomes. The proposed algorithm converges after only 61 iterations, ensuring efficient solution performance. The average purchase price of electricity from the PS for the MG is USD 1.1, while the selling price of PWE sources from MG for the PS is USD 0.6. This effectively promotes the MG to prioritize the consumption of PWE sources and encourages the PS to repurchase the electricity generated by PWE sources. On average, carbon emissions decreased by approximately 300 g each time slot, and the average amount of carbon trading was around USD 8. Ultimately, this research delivers a practical and impactful solution for the development of MGs and the advancement of carbon reduction goals. Full article

This paper studies long-term resource allocation for rate-splitting multiple access (RSMA) in multi-carrier downlink systems. RSMA provides a flexible interference-management mechanism that bridges spatial division multiple access (SDMA) and non-orthogonal multiple access (NOMA), but guaranteeing long-term quality-of-service (QoS) performance under dynamic fading channels remains challenging. To address this limitation, we develop an opportunistic scheduling framework based on Lagrangian duality and stochastic optimization, which maximizes the long-term weighted sum rate (WSR) while satisfying per-user time-average QoS constraints. The proposed method decomposes the long-term problem into per-slot subproblems with adaptive effective weights, and each subproblem is efficiently solved through a two-stage procedure consisting of subcarrier–user pair matching and power allocation. Simulation results show that the proposed RSMA scheduling framework significantly outperforms conventional NOMA while ensuring the QoS requirements of all users. These results demonstrate the practical applicability of RSMA for next-generation wireless networks requiring both high spectral efficiency and long-term reliability. Full article