Search Results (265)

The continued growth of international maritime trade has driven automated container terminals (ACTs) to pursue more efficient operational management strategies. In practice, the horizontal yard layout in ACTs significantly enhances transshipment efficiency. However, the more complex horizontal transporting system calls for an effective approach to enhance automated guided vehicle (AGV) scheduling. Considering AGV charging and path conflicts, this paper proposes a multi-agent reinforcement learning (MARL) approach to address the AGV dispatching and path planning (VD2P) problem under a horizontal layout. The VD2P problem is formulated as a Markov decision process model. To mitigate the challenges of high-dimensional state-action space, a multi-agent framework is developed to control the AGV dispatching and path planning separately. A mixed global–individual reward mechanism is tailored to enhance both exploration and corporation. A proximal policy optimization method is used to train the scheduling policies. Experiments indicate that the proposed MARL approach can provide high-quality solutions for a real-world-sized scenario within tens of seconds. Compared with benchmark methods, the proposed approach achieves an improvement of 8.4% to 53.8%. Moreover, sensitivity analyses are conducted to explore the impact of different AGV configurations and charging strategies on scheduling. Managerial insights are obtained to support more efficient terminal operations. Full article

Electrolytic aluminum is one of the most energy-intensive industrial processes and offers strong potential for demand-side flexibility and renewable energy integration. However, existing studies mainly focus on operational scheduling, while comprehensive planning frameworks at the industrial-park scale remain limited. This study proposes an optimal planning framework for electrolytic aluminum that co-optimizes renewable energy investments, waste heat recovery, and green power trading while capturing the temperature safety constraints of electrolytic cells. The electrolytic aluminum process is explicitly modeled with heat exchangers to enable combined cooling–heating–power supply for nearby users. A wind power priority subscription mechanism and green certificate compliance are incorporated to enhance practical applicability and support future decarbonization requirements. Moreover, a two-stage particle swarm-deterministic optimization scheme is developed to provide a tractable solution to the inherently nonconvex mixed-integer nonlinear model. Case studies based on a real plant in Xinjiang, China, demonstrate that the proposed framework can raise the green electricity aluminum share to 60.4%, reduce annual carbon emissions by 52.0%, and significantly increase total system profit compared with the benchmark configuration, highlighting its economic and sustainability benefits for industrial park development. Full article

We study UAV-assisted 5G uplink connectivity for disaster response, in which a UAV (unmanned aerial vehicle) acts as an aerial base station to restore service to ground users. We formulate a joint control problem coupling UAV kinematics (bounded acceleration and velocity), per-subchannel uplink power allocation, and uplink non-orthogonal multiple access (UL-NOMA) scheduling with adaptive successive interference cancellation (SIC) under a minimum user-rate constraint. The wireless channel follows 3GPP urban macro (UMa) with probabilistic line of sight/non-line of sight (LoS/NLoS), realistic receiver noise levels and noise figure, and user equipment (UE) transmit-power limits. We propose a bounded-action proximal policy optimization with generalized advantage estimation (PPO-GAE) agent that parameterizes acceleration and power with squashed distributions and enforces feasibility by design. Across four user distributions (clustered, uniform, ring, and edge-heavy) and multiple rate thresholds, our method increases the fraction of users meeting the target rate by 8.2–10.1 percentage points compared to strong baselines (OFDMA with heuristic placement, PSO-based placement/power, and PPO without NOMA) while reducing median UE transmit power by $64.6\%$. The results are averaged over at least five random seeds, with $95\%$ confidence intervals. Ablations isolate the gains from NOMA, adaptive SIC order, and bounded-action parameterization. We discuss robustness to imperfect SIC and CSI errors and release code/configurations to support reproducibility. Full article

Efficient truck–shovel allocation is essential for optimizing open-pit mining operations, but the integration of heterogeneous diesel and electric fleets introduces complex scheduling challenges, including charging requirements, range limitations, and equipment capacity constraints. This study proposes an integrated allocation framework tailored to heterogeneous fleets, formulating a multi-objective optimization model that minimizes transportation cost and waiting time under realistic constraints. An enhanced multi-objective particle swarm optimization algorithm with adaptive penalty mechanisms is developed, providing superior convergence and computational efficiency compared to traditional methods. A case study demonstrates that heterogeneous fleets achieve a better trade-off, with a balanced fleet configuration reducing transportation cost by 26.1% and waiting time by 19.2% compared to pure diesel and electric fleets, respectively. Sensitivity analyses reveal that fluctuations in fuel and electricity prices reshape the trade-off, while faster charging enhances electric truck competitiveness but increases diesel idle time. These findings offer practical insights for configuring heterogeneous fleets and adapting scheduling strategies in dynamic energy and technology environments, supporting sustainable mining operations. Full article

The growing share of variable renewable energy sources in power systems is increasing the need for short-term operational flexibility—particularly from large industrial electricity consumers. This study proposes a practical, two-stage optimization framework to unlock this flexibility in cement manufacturing and support participation in electricity balancing markets. In Stage 1, a mixed-integer linear programming model minimizes electricity procurement costs by optimally scheduling the raw milling subsystem, subject to technical and operational constraints. In Stage 2, a flexibility assessment model identifies and evaluates profitable deviations from this baseline, targeting participation in Spain’s manual Frequency Restoration Reserve market. The methodology is validated through a real-world case study at a Spanish cement plant, incorporating photovoltaic (PV) generation and battery energy storage systems (BESS). The results show that flexibility services can yield monthly revenues of up to €800, with limited disruption to production processes. Additionally, combined PV + BESS configurations achieve electricity cost reductions and investment paybacks as short as six years. The proposed framework offers a replicable pathway for integrating demand-side flexibility into energy-intensive industries—enhancing grid resilience, economic performance, and decarbonization efforts. Full article

Coaxial closed-loop geothermal systems, increasingly recognized as scalable and low-impact geothermal solutions, remain limited by conductive heat transfer between the reservoir and wellbore. This study investigates three strategies to enhance thermal output: (i) dynamic operation scheduling, (ii) substitution of conventional fluids with Organic Rankine Cycle (ORC) working fluids, and (iii) targeted conductive enhancements near the well. Using a CMG STARS simulation framework, system performance was evaluated over 1- to 20-year horizons, introducing a characteristic thermal recovery curve as a tool for analyzing long-term behavior. Results show that extended recovery durations raise outlet temperatures but with diminishing returns, identifying approximately 80% recovery as a practical optimization point. Fluids such as n-pentane and R245fa deliver substantially greater ORC-compatible heat than water, with thermo-siphoning observed under low-flow conditions. Conductive enhancement geometries, namely ring and fishbone configurations, exhibit distinct performance profiles, with rings outperforming fishbones due to larger injected volumes and greater advantage due to reservoir reach. One-year gains range from 4.5–9.4% for rings and 0.65–1.37% for fishbones, stabilizing at 3.7–7.8% and 0.55–1.18% after 20 years. These findings provide design and operational guidance for advancing coaxial closed-loop systems in low-carbon energy deployment. Full article

This study investigates the optimization of Pumped Storage Hydropower (PSH) integrated with Floating Photovoltaic (FPV) systems, with a focus on sustainable energy management. A nonlinear programming framework combined with scenario analysis was applied to a real hydropower system in Trentino, Italy. The optimization maximizes revenues through energy arbitrage while accounting for water resource and environmental objectives. Upgrading the traditional hydropower plant to PSH operation increases revenues by 4–8% over two hydrological years. Multi-objective optimization further reveals large gains in water availability, confirming PSH’s dual role as energy storage and water management infrastructure. Different FPV configurations analyzed show a 2–3% increase in photovoltaic energy yield due to the water-cooling effect, while the overall hybrid PSH–FPV integration mainly reduces grid dependency and pumping-related emissions, with near-complete decarbonization achievable under optimized scheduling. Overall, PSH provides the primary economic and operational advantage, while FPV strengthens sustainability, enabling resilient hydro–solar operation and contributing to renewable integration and decarbonization in future energy systems. Full article

Time division duplexing (TDD) technology holds great promise for future satellite communication systems. To address the interference and low resource utilization encountered in satellite TDD scenarios, this paper proposes a flexible and on-demand frame structure, where the interference can be mitigated by scheduling the UE transmissions instead of configuring a long guard period (GP). Based on the frame structure, the interference between downlink broadcasting signals and preambles is analyzed, followed by formulating a random access channel (RACH) occasion (RO) configuration optimization problem that aims to maximize the RO utilization, and a structured global candidate exploration algorithm (SGCEA) is proposed to solve it. Some simulation experiments are carried out based on the practical configurations from the third-generation partnership project (3GPP)standards. Simulation results show that the proposed algorithm consistently identifies the optimal RO configuration from the predefined configurations, and the utilization remains above 80% as the satellite coverage area increases, which demonstrates the superior performance of the proposed approach and highlights its potential for practical deployment in future TDD-based satellite communication systems. Full article

In the context of pavement management systems (PMSs), overloaded trucks impose severe economic and environmental burdens by accelerating pavement deterioration and increasing greenhouse gas (GHG) emissions. Existing research on Weigh-in-Motion (WIM) placement has rarely incorporated environmental impacts, particularly greenhouse gas (GHG) emissions, into the decision-making process. Instead, most studies have focused on infrastructure damage and have paid limited attention to how enforcement interacts with driver evasion behavior and schedule-related constraints. To address this gap, this study develops a bi-level optimization framework that simultaneously minimizes PMS costs, travel costs, and environmental (GHG) costs. The upper-level problem represents the total social cost minimization, while the lower-level problem models drivers’ routes and demand shift. The framework endogenously captures utility-based demand shifts, allowing overloaded drivers to switch to legal operations when enforcement and schedule-related constraints outweigh overloading benefits. A numerical study using the Sioux Falls network demonstrates that dual WIM installations significantly outperform single configurations, achieving network-wide cost reductions of up to 1.5% compared to 0.4%. Notably, PMS costs for overloaded trucks decreased by nearly 60%, confirming the effectiveness of strategic enforcement. Ultimately, this study contributes a unified decision-support tool that reframes WIM enforcement from a passive control measure into a proactive strategy for sustainable freight management. Full article

To address the efficiency and safety challenges arising from the mixed operation of autonomous and human-driven container trucks during the automation transformation of traditional container terminals, this study designed a simulation-based optimization framework for mixed vehicle scheduling. A spatio-temporal graph dynamic scheduling model was constructed, incorporating node capacity, arc capacity, and path constraints, to establish a multi-objective optimization model aimed at minimizing the maximum completion time of internal trucks and the average waiting time of external trucks. An improved NSGA-II algorithm was employed to generate task assignment solutions, which were evaluated using discrete-event simulation, integrating a dynamic programming-based yard block selection strategy for external trucks and a congestion-aware path planning algorithm. Experimental results demonstrate that the dynamic priority strategy effectively adapts to different traffic flow scenarios: under low external truck flow, the autonomous internal truck priority strategy reduces task completion time by 18% to 25%, while under high flow, the external truck priority strategy significantly decreases the average waiting time. The optimal configuration ratio between internal and external trucks was identified as approximately 1:2. This research provides a theoretical basis and decision support for enhancing terminal operational efficiency and automation transformation. Full article

This paper proposes a hybrid integer optimization method based on the Whale Optimization Algorithm (WOA) for the asymmetric central air conditioning chiller system of a 530-m super high-rise building in Guangzhou. Firstly, a three-hidden-layer multilayer perceptron (MLP) chiller model based on 16,276 sets of measured data and a gradient boosting regression cooling tower model based on 21,369 sets of operating condition data were constructed, achieving high-precision modeling of the energy consumption of all equipment in the chiller system. Secondly, a hybrid encoding strategy of “threshold truncation + continuous relaxation” was proposed to integrate discrete on-off states and continuous operating parameters into WOA, and a three-layer constraint repair mechanism was designed to ensure the physical feasibility of the optimization process and the safe operation of equipment. Verification across three load scenarios—low, medium, and high—showed that the optimized system’s energy efficiency ratio (EER) increased by 15.01%, 12.61%, and 11.86%, respectively, with energy savings of 12.91%, 11.18%, and 10.58%. The annual rolling optimization results showed that the average EER increased from 5.07 to 5.88 (16.1%), with energy savings ranging from 8.59% to 18.92%. Sensitivity analysis indicated that pump quantity is the most influential parameter affecting system energy consumption, with an additional pump reducing it by 1.1%. The optimization method proposed in this paper meets the minute-level real-time scheduling requirements of building automation systems and provides an implementable solution for energy-saving optimization of central air conditioning chiller systems in super high-rise buildings. Full article

In response to the challenges of low computational efficiency, insufficient detail restoration, and dependence on multiple GPUs in 3D Gaussian Splatting for large-scale UAV scene reconstruction, this study introduces an improved 3D Gaussian Splatting framework. It primarily targets two aspects: optimization of the partitioning strategy and enhancement of adaptive density control. Specifically, an adaptive partitioning strategy guided by scene complexity is designed to ensure more balanced computational workloads across spatial blocks. To preserve scene integrity, auxiliary point clouds are integrated during partition optimization. Furthermore, a pixel weight-scaling mechanism is employed to regulate the average gradient in adaptive density control, thereby mitigating excessive densification of Gaussians. This design accelerates the training process while maintaining high-fidelity rendering quality. Additionally, a task-scheduling algorithm based on frequency-domain analysis is incorporated to further improve computational resource utilization. Extensive experiments on multiple large-scale UAV datasets demonstrate that the proposed framework can be trained efficiently on a single RTX 3090 GPU, achieving more than a 50% reduction in average optimization time while maintaining PSNR, SSIM and LPIPS values that are comparable to or better than representative 3DGS-based methods; on the MatrixCity-S dataset (>6000 images), it attains the highest PSNR among 3DGS-based approaches and completes training on a single 24 GB GPU in less than 60% of the training time of DOGS. Nevertheless, the current framework still requires several hours of optimization for city-scale scenes and has so far only been evaluated on static UAV imagery with a fixed camera model, which may limit its applicability to dynamic scenes or heterogeneous sensor configurations. Full article

Heating, ventilation, and air conditioning (HVAC) systems are the primary energy consumers in modern office buildings, with chillers consuming the most energy. As critical components of building air conditioning, the effective functioning of HVAC systems holds substantial importance for energy preservation and emission mitigation. To enhance the operational performance of HVAC systems and accomplish energy conservation objectives, precise cooling load forecasting is essential. This research employs an office facility in Binzhou City, Shandong Province, as a case investigation and presents a day-ahead scheduling-based model predictive control (MPC) approach for HVAC systems, which targets minimizing the overall system power utilization. An attention mechanism-based long short-term memory (LSTM) neural network forecasting model is developed to predict the building’s cooling demand for the subsequent 24 h. Based on the forecasting outcomes, the MPC controller adopts the supply–demand equilibrium between cooling capacity and cooling demand as the central constraint and utilizes the particle swarm optimization (PSO) algorithm for rolling optimization to establish the optimal configuration approach for the chiller flow rate and temperature, thereby realizing the dynamic control of the HVAC system. To verify the efficacy of this approach, simulation analysis was performed using the TRNSYS simulation platform founded on the actual operational data and meteorological parameters of the building. The findings indicate that compared with the conventional proportional–integral–derivative (PID) control approach, the proposed day-ahead scheduling-based MPC strategy can attain an average energy conservation rate of 9.23% over a one-week operational period and achieve an energy-saving rate of 8.25% over a one-month period, demonstrating its notable advantages in diminishing building energy consumption. Full article

This work proposes an intelligent strategy for the coordinated management of active and reactive power in Battery Energy Storage Systems (BESSs) within AC microgrids operating under both grid-connected (GCM) and islanded (IM) modes to minimize daily operational costs. The problem is formulated as a mixed-variable optimization model that explicitly leverages the control capabilities of BESS power converters. To solve it, a Parallel Particle Swarm Optimization (PPSO) algorithm is employed, coupled with a Successive Approximation (SA) power flow solver. The proposed approach was benchmarked against parallel implementations of the Crow Search Algorithm (PCSA) and the JAYA algorithm (PJAYA), both in parallel, using a realistic 33-node AC microgrid test system based on real demand and photovoltaic generation profiles from Medellín, Colombia. The strategy was evaluated under both deterministic conditions (average daily profiles) and stochastic scenarios (100 daily profiles with uncertainty). The proposed framework is evaluated on a 33-bus AC microgrid that operates in both grid-connected and islanded modes, with a battery energy storage system dispatched at both active and reactive power levels subject to network, state-of-charge, and power-rating constraints. Three population-based optimization algorithms are used to coordinate BESS schedules, and their performance is compared based on daily operating cost, BESS cycling, and voltage profile quality. Quantitatively, the PPSO strategy achieved cost reductions of 2.39% in GCM and 1.62% in IM under deterministic conditions, with a standard deviation of only 0.0200% in GCM and 0.2962% in IM. In stochastic scenarios with 100 uncertainty profiles, PPSO maintained its robustness, reaching average reductions of 2.77% in GCM and 1.53% in IM. PPSO exhibited consistent robustness and efficient performance, reaching the highest average cost reductions with low variability and short execution times in both operating modes. These findings indicate that the method is well-suited for real-time implementation and contributes to improving economic outcomes and operational reliability in grid-connected and islanded microgrid configurations. The case study results show that the different strategies yield distinct trade-offs between economic performance and computational effort, while all solutions satisfy the technical limits of the microgrid. Full article

This study investigates the limitations of existing models in optimizing equipment resource allocation for the large-scale production of precast concrete components in highway engineering. There are abundant investigations on scheduling models of precast concrete components. However, there is a scientific problem that previous models often overlooked the interruptibility of specific processes and the possibility of performing tasks outside of regular working hours, leading to suboptimal resource utilization. To address this limitation, an improved differential evolution (DE) algorithm was developed, which incorporates an adaptive mutation operator and a dual mutation strategy to enhance population diversity and accelerate convergence speed. The proposed optimization model significantly reduced equipment resource consumption. In a real-world case study, the model achieved an 11.11% reduction in project duration and a 21.4% increase in production capacity under the same resource configuration. The improved DE algorithm demonstrated superior performance in maintaining population diversity and accelerating convergence. These findings provide a scientifically grounded approach for enhancing productivity and resource efficiency in prefabricated construction, with potential applications extending beyond highway projects. Full article