Search Results (72)

Industrial scheduling plays a central role in Industry 4.0, where efficiency, robustness, and adaptability are essential for competitiveness. This review surveys recent advances in reinforcement learning, digital twins, and hybrid artificial intelligence (AI)–operations research (OR) approaches, which are increasingly used to address the complexity of flexible job-shop and distributed scheduling problems. We focus on how these methods compare in terms of scalability, robustness under uncertainty, and integration with industrial IT systems. To move beyond an enumerative survey, the paper introduces a structured analysis in three domains: comparative strengths and limitations of different approaches, ready-made tools and integration capabilities, and representative industrial case studies. These cases, drawn from recent literature, quantify improvements such as reductions in makespan, tardiness, and cycle time variability, or increases in throughput and schedule stability. The review also discusses critical challenges, including data scarcity, computational cost, interoperability with Enterprise Resource Planning (ERP)/Manufacturing Execution System (MES) platforms, and the need for explainable and human-in-the-loop frameworks. By synthesizing methodological advances with industrial impact, the paper highlights both the potential and the limitations of current approaches and outlines key directions for future research in resilient, data-driven production scheduling. Full article

Ensuring optimal adhesive curing during plywood and LVL (Layered Veneer Lumber) hot pressing requires accurate knowledge of internal temperature distribution, which is often difficult to assess using conventional surface-based measurements. This study introduces a custom-developed multi-layer smart sensor network capable of in situ, real-time temperature profiling across LVL layers during industrial hot pressing. The system integrates miniature embedded sensors and proprietary data acquisition software, enabling the simultaneous multi-point monitoring of thermal dynamics with a high temporal resolution. Experiments were performed on LVL panels of varying thicknesses, applying industry-standard pressing schedules derived from conventional calculation rules. Despite adherence to prescribed pressing times, results reveal significant core temperature deficits in thicker panels, potentially compromising adhesive gelation and overall bonding quality. These findings underline the need to revisit the pressing time determination for thicker products and demonstrate the potential of advanced sensing technologies to support adaptive process control. The proposed approach contributes to smart manufacturing and the remote-like monitoring of internal thermal states, providing valuable insights for enhancing product performance and industrial process efficiency. Full article

The resilience and sustainable development of modern power distribution systems faces escalating challenges due to increasing renewable integration and extreme events. Traditional single-system approaches often overlook the spatiotemporal coordination of cross-domain restoration resources. In this paper, we propose a multi-stage resilience enhancement method that employs transportation and hydrogen energy systems. This approach coordinates the pre-event preventive allocation and multi-stage collaborative scheduling of diverse restoration resources, including remote-controlled switches (RCSs), mobile hydrogen emergency resources (MHERs), and hydrogen production and refueling stations (HPRSs). The proposed framework supports cross-stage dynamic optimization scheduling, enabling the development of adaptive resource dispatch strategies tailored to the characteristics of different stages, including prevention, fault isolation, and service restoration. The model is applicable to complex scenarios involving dynamically changing network topologies and is formulated as a mixed-integer linear programming (MILP) problem. Case studies based on the IEEE 33-bus system show that the proposed method can restore a distribution system’s resilience to approximately 87% of its normal level following extreme events. Full article

To reduce GHG emissions from food miles and enhance urban food security, this study develops and evaluates an integrated framework combining three strategies: the 15-minute city concept, urban agriculture, and a renewable-energy-powered green transportation (GT) system. The goal is to create a scalable, holistic approach to sustainable food production and distribution within neighborhoods. A Food Production and Transportation Framework is proposed, modeling vegetable cultivation across rooftops, facades, and lot spaces, with optimized allocations based on a tailored Food Production Schedule. The harvested produce is distributed via GT powered by sidewalk-integrated photovoltaics (PVs). Results demonstrate that using 15% of roof, facade, and lot spaces yields an achieved annual food self-sufficiency of 100%. The transportation system operates with a single GT unit powered by 98 m² of sidewalk PVs, reducing CO₂ emissions by 98% from the base case. Economic analysis indicates a payback period of 2.8 years, with the cost of PV-generated electricity estimated at C$0.92/kWh. This framework highlights that 0.19 units of local food production offset one unit of CO₂ emissions. This integrated approach advances multiple UN Sustainable Development Goals (SDGs), including SDG 2 (Zero Hunger), SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Full article

The Distributed No-Wait Flow Shop Scheduling Problem (DNWFSP) arises in various manufacturing contexts, such as chemical production and electronic assembly, where strict no-wait constraints and multi-factory coordination are required. Solving the DNWFSP involves determining the allocation of jobs to factories and the no-wait processing sequences within each factory, making it a highly complex combinatorial problem. To address the limitations of existing methods—including poor initial solution quality, limited neighborhood exploration, and a tendency to converge prematurely—this paper proposes a Multi-Neighborhood Adaptive Tabu Search Algorithm (MNATS). The MNATS integrates a balance–lookahead NEH initializer (BL-NEH), an adaptive neighborhood local search (ANLS) strategy, and an Adaptive Tabu-Guided Perturbation (ATP) strategy. Experimental results on multiple benchmark instances demonstrate that MNATS algorithm significantly outperforms several state-of-the-art algorithms in terms of solution quality and robustness. Full article

With the continuous acceleration of industrialization, air pollution has become increasingly severe and has, to some extent, contributed to the progression of global climate change. Against this backdrop, accurate temperature forecasting plays a vital role in various fields, including agricultural production, energy scheduling, environmental governance, and public health protection. To improve the accuracy and stability of temperature prediction, this study proposes a hybrid modeling approach that integrates convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, and random forests (RFs). This model fully leverages the strengths of CNNs in extracting local spatial features, the advantages of LSTM in modeling long-term dependencies in time series, and the capabilities of RF in nonlinear modeling and feature selection through ensemble learning. Based on daily temperature, meteorological, and air pollutant observation data from Wuhan during the period 2015–2023, this study conducted multi-scale modeling and seasonal performance evaluations. Pearson correlation analysis and random forest-based feature importance ranking were used to identify two key pollutants (PM_2.5 and O₃) and two critical meteorological variables (air pressure and visibility) that are strongly associated with temperature variation. A CNN-LSTM model was then constructed using the meteorological variables as input to generate preliminary predictions. These predictions were subsequently combined with the concentrations of the selected pollutants to form a new feature set, which was input into the RF model for secondary regression, thereby enhancing the overall model performance. The main findings are as follows: (1) The six major pollutants exhibit clear seasonal distribution patterns, with generally higher concentrations in winter and lower in summer, while O₃ shows the opposite trend. Moreover, the influence of pollutants on temperature demonstrates significant seasonal heterogeneity. (2) The CNN-LSTM-RF hybrid model shows excellent performance in temperature prediction tasks. The predicted values align closely with observed data in the test set, with a low prediction error (RMSE = 0.88, MAE = 0.66) and a high coefficient of determination (R² = 0.99), confirming the model’s accuracy and robustness. (3) In multi-scale forecasting, the model performs well on both daily (short-term) and monthly (mid- to long-term) scales. While daily-scale predictions exhibit higher precision, monthly-scale forecasts effectively capture long-term trends. A paired-sample t-test on annual mean temperature predictions across the two time scales revealed a statistically significant difference at the 95% confidence level (t = −3.5299, p = 0.0242), indicating that time granularity has a notable impact on prediction outcomes and should be carefully selected and optimized based on practical application needs. (4) One-way ANOVA and the non-parametric Kruskal–Wallis test were employed to assess the statistical significance of seasonal differences in daily absolute prediction errors. Results showed significant variation across seasons (ANOVA: F = 2.94, p = 0.032; Kruskal–Wallis: H = 8.82, p = 0.031; both p < 0.05), suggesting that seasonal changes considerably affect the model’s predictive performance. Specifically, the model exhibited the highest RMSE and MAE in spring, indicating poorer fit, whereas performance was best in autumn, with the highest R² value, suggesting a stronger fitting capability. Full article

The paper advances the theory of queuing networks by presenting generalized product-form solutions that explicitly take into account the service intensity depending on the number of customers in the network nodes, including the presence of multiple service channels and multi-threaded nodes. This represents a significant extension of the classical results on the Jackson network by integrating graph-theoretic methods, including basic subgraphs with service rates depending on the number of requests. The originality of the article is in the combination of stationary and non-stationary approaches to modeling service networks within a single approach. In particular, acyclic networks with deterministic service time and non-stationary Poisson input flow are considered. Such systems present a significant difficulty, which is noted in well-known works. A stationary model of an open queuing network with service intensity depending on the number of customers in the network nodes is constructed. The stationary network model is related to the problem of marine linear navigation along a strictly defined route and schedule. A generalization of the product theorem with a new form of stationary distribution is developed for it. It is shown that even a small increase in the service intensity with a large number of requests in a queuing network node can significantly reduce its average value. A non-stationary model of an acyclic queuing network with deterministic service time in network nodes and a non-stationary Poisson input flow is constructed. The non-stationary model is associated with irregular (tramp) sea transportation. The intensities of non-stationary Poisson flows in acyclic networks are represented by product formulas using paths between the initial node and other network nodes. The parameters of Poisson distributions of the number of customers in network nodes are calculated. The simplest formulas for calculating such queuing networks are obtained for networks in the form of trees. Full article

As the global manufacturing industry’s transformation accelerates toward being intelligent, “unmanned”, and low-carbon, manufacturing workshops face conflicts between production schedules and transportation tasks, leading to low efficiency and resource waste. This paper presents a multi-agent collaborative scheduling optimization method based on a hybrid game–genetic framework to address issues like high AGV (Automated Guided Vehicle) idle rates, excessive energy consumption, and uncoordinated equipment scheduling. The method establishes a trinity system integrating distributed decision-making, dynamic coordination, and environment awareness. In this system, the multi-agent decision-making and collaboration process exhibits significant symmetry characteristics. All agents (machine agents, mobile agents, etc.) follow unified optimization criteria and interaction rules, forming a dynamically balanced symmetric scheduling framework in resource competition and collaboration, which ensures fairness and consistency among different agents in task allocation, path planning, and other links. An improved best-response dynamic algorithm is employed in the decision-making layer to solve the multi-agent Nash equilibrium, while the genetic optimization layer enhances the global search capability by encoding scheduling schemes and adjusting crossover/mutation probabilities using dynamic competition factors. The coordination pivot layer updates constraints in real time based on environmental sensing, forming a closed-loop optimization mechanism. Experimental results show that, compared with the traditional genetic algorithm (TGA) and particle swarm optimization (PSO), the proposed method reduces the maximum completion time by 54.5% and 44.4% in simple scenarios and 57.1% in complex scenarios, the AGV idling rate by 68.3% in simple scenarios and 67.5%/77.6% in complex scenarios, and total energy consumption by 15.7%/10.9% in simple scenarios and 25%/18.2% in complex scenarios. This validates the method’s effectiveness in improving resource utilization and energy efficiency, providing a new technical path for intelligent scheduling in manufacturing workshops. Meanwhile, its symmetric multi-agent collaborative framework also offers a reference for the application of symmetry in complex manufacturing system optimization. Full article

Driven by the “dual carbon” goal, virtual power plants (VPPs) are the core vehicle for integrating distributed energy resources, but the multiple uncertainties in wind power, electricity/heat load, and electricity price, coupled with the impact of carbon-trading cost, make it difficult for traditional scheduling methods to balance the robustness and economy of VPPs. Therefore, this paper proposes an oxy-fuel combustion capture (OCC)-VPP architecture, integrating an OCC unit to improve the energy efficiency of the system through the “electricity-oxygen-carbon” cycle. Ten typical scenarios are generated by Latin hypercube sampling and K-means clustering to describe the uncertainties of source and load probability distribution, combined with the polyhedral uncertainty set to delineate the boundary of source and load fluctuations, and the stepped carbon-trading mechanism is introduced to quantify the cost of carbon emission. Then, a three-stage stochastic–robust scheduling model is constructed. The simulation based on the arithmetic example of OCC-VPP in North China shows that (1) OCC-VPP significantly improves the economy through the synergy of electric–hydrogen production and methanation (52% of hydrogen is supplied with heat and 41% is methanated), and the cost of carbon sequestration increases with the prediction error, but the carbon benefit of stepped carbon trading is stabilized at the base price of 320 DKK/ton; (2) when the uncertainty is increased from 0 to 18, the total cost rises by 45%, and the cost of purchased gas increases by the largest amount, and the cost of energy abandonment increases only by 299.6 DKK, which highlights the smoothing effect of energy storage; (3) the proposed model improves the solution speed by 70% compared with stochastic optimization, and reduces cost by 4.0% compared with robust optimization, which balances economy and robustness efficiently. Full article

This study tackles scheduling challenges in multi-product assembly within distributed manufacturing, where components are produced simultaneously at dedicated factories (single capacity per site) and assembled centrally upon completion. To minimize makespan and maximum tardiness, we design a symmetry-exploiting enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II) integrated with Q-learning. Our approach systematically explores the solution space using dual symmetric variable neighborhood search (VNS) strategies and two novel crossover operators that enhance solution-space symmetry and genetic diversity. An ε-greedy policy leveraging maximum Q-values guides the symmetry-aware search toward optimality while enabling strategic exploration. We validate an MILP model (Gurobi-implemented) and present our symmetry-refined algorithm against six heuristics. Multi-scale experiments confirm superiority, with Friedman tests demonstrating statistically significant gains over benchmarks, providing actionable insights for efficient distributed manufacturing scheduling. Full article

Background: This study aims to compare the logistical challenges of Home Health Care (HHC) services in Portugal and Brazil, highlighting the structural and operational differences between both systems. Methods: Guided by an abductive research approach, data were collected using a semi-structured survey with open-ended questions, applied to 13 HHC teams in Portugal and 18 in Brazil, selected based on national coordination recommendations. The data collection process was conducted in person, and responses were analyzed using descriptive statistics and qualitative content analysis. Results: The results reveal that Portugal demonstrates higher productivity, stronger territorial coverage, and a more integrated inventory management system, while Brazil presents greater multidisciplinary team integration, more flexible fleet logistics, and more advanced digital health records. Despite these strengths, both countries continue to address key logistical aspects, such as scheduling, supply distribution, and data management, largely through empirical strategies. Conclusions: This research contributes to the theoretical understanding of international HHC logistics by emphasizing strategic and systemic aspects often overlooked in operational studies. In practical terms, it offers insights for public health managers to improve resource allocation, fleet coordination, and digital integration in aging societies. Full article

To address energy shortages and achieve carbon peaking/neutrality, this study develops a distributed renewable-based integrated energy system (IES) for rural active zero-energy buildings (ZEBs). Energy consumption patterns of typical rural houses are analyzed, guiding the design of a resource-tailored IES that balances economy and sustainability. Key equipment capacities are optimized to achieve net-zero/zero energy consumption targets. For typical daily cooling/heating/power loads, equipment output is scheduled using a dual-objective optimization model minimizing operating costs and CO₂ emissions. Results demonstrate that: (1) Net-zero-energy IES outperforms separated production (SP) and full electrification systems (FES) in economic-environmental benefits; (2) Zero-energy IES significantly reduces rural building carbon emissions. The proposed system offers substantial practical value for China’s rural energy transition. Full article

This article presents an empirical evaluation of the technical and economic performance of a building-integrated photovoltaic (PV) system implemented at the Faculty of Architecture and Urbanism of the University of Cuenca, Ecuador. This study explores both stages of deployment, beginning with a 7.7 kWp pilot system and later scaling to a full 75.6 kWp configuration. This hourly monitoring of power exchanges with utility was conducted over several months using high-resolution instrumentation and cloud-based analytics platforms. A detailed comparison between projected energy output, recorded production, and real energy consumption was carried out, revealing how seasonal variability, cloud cover, and academic schedules influence system behavior. The findings also include a comparison between billed and actual electricity prices, as well as an analysis of the system’s payback period under different cost scenarios, including state-subsidized and real-cost frameworks. The results confirm that energy exports are frequent during weekends and that daily generation often exceeds on-site demand on non-working days. Although the university benefits from low electricity tariffs, the system demonstrates financial feasibility when broader public cost structures are considered. This study highlights operational outcomes under real-use conditions and provides insights for scaling distributed generation in institutional settings, with particular relevance for Andean urban contexts with similar solar profiles and tariff structures. Full article

In order to address the Distributed Displacement Flow Shop Scheduling Problem (DPFSP) with uncertain processing times in real production environments, Plant Simulation is employed to construct a simulation model for the MSRDPFSP. The model conducts quantitative analyses of workshop layout, assembly line design, worker status, operating status of robotic arms and AGV vehicles, and production system failure rates. A hybrid NEH-DDQN algorithm is integrated into the simulation model via a COM interface and DLL, where the NEH algorithm ensures the model maintains optimal performance during the early training phase. Four scheduling strategies are designed for workpiece allocation across different workshops. A deep neural network replaces the traditional Q-table for greedy selection among these four scheduling strategies, using each workshop’s completion time as a simplified state variable. This approach reduces algorithm training complexity by abstracting away intricate workpiece allocation details. Experimental comparisons show that for the data of 500 workpieces, the NEH algorithm in 3 s demonstrates equivalent quality to that produced by the GA algorithm in 300 s. After 2000 iterations, the DDQN algorithm achieves a 15% reduction in makespan with only a 2.5% increase in computational time compared to random search, this joint simulation system offers an efficient and stable solution for the modeling and optimization of the MSRDPFSP issue. Full article

This paper presents a novel real-time overload detection algorithm for distributed control systems (DCSs), particularly applied to thermoelectric power plant environments. The proposed method is integrated with a modular multi-functional processor (MFP) architecture, designed to enhance system reliability, optimize resource utilization, and improve fault resilience under dynamic operational conditions. As legacy DCS platforms, such as those installed at the Tae-An Thermoelectric Power Plant, face limitations in applying advanced logic mechanisms, a simulation-based test bench was developed to validate the algorithm in anticipation of future DCS upgrades. The algorithm operates by partitioning function code executions into segment groups, enabling fine-grained, real-time CPU and memory utilization monitoring. Simulation studies, including a modeled denitrification process, demonstrated the system’s effectiveness in maintaining load balance, reducing power consumption to 17 mW under a 2 Gbps data throughput, and mitigating overload levels by approximately 31.7%, thereby outperforming conventional control mechanisms. The segmentation strategy, combined with summation logic, further supports scalable deployment across both legacy and next-generation DCS infrastructures. By enabling proactive overload mitigation and intelligent energy utilization, the proposed solution contributes to the advancement of self-regulating power control systems. Its applicability extends to energy management, production scheduling, and digital signal processing—domains where real-time optimization and operational reliability are essential. Full article