Search Results (929)

With the rapid advancement of the manufacturing industry and the widespread implementation of intelligent manufacturing systems, the energy-aware distributed heterogeneous flexible job shop scheduling problem (DHFJSP) has emerged as a critical challenge in optimizing modern production systems. This study introduces an innovative method to reduce both the makespan and the total energy consumption (TEC) in the context of the DHFJSP. A deep reinforcement advantage Actor-Critic-based co-evolution algorithm (DRAACCE) is proposed to address the issue, which leverages the powerful decision-making and perception abilities of the advantage Actor-Critic (AAC) method. The DRAACCE algorithm consists of three main components: First, to ensure a balance between global and local search capabilities, we propose a new co-evolutionary strategy. This enables the algorithm to explore the solution space efficiently while maintaining robust exploration and exploitation. Next, a novel evolution strategy is introduced to improve the algorithm’s convergence rate and solution diversity, ensuring that the search process is both fast and effective. Finally, we integrate deep reinforcement learning with the advantage Actor-Critic framework to select elite solutions, enhancing the optimization process and leading to superior performance in minimizing both TEC and makespan. Extensive experiments validate the effectiveness of the proposed DRAACCE algorithm. The experimental results show that DRAACCE significantly outperforms existing state-of-the-art methods on all 20 instances and a real-world case, achieving better solutions in terms of both makespan and TEC. Full article

Urban traffic management is highly complex, and inefficient control strategies often worsen congestion and increase energy consumption. This paper introduces a collaborative multi-agent reinforcement learning method tailored for sparse control scenarios, IKS-SAC (Improved Knowledge Sharing Soft Actor–Critic), which enhances coordination between traffic signals to optimize traffic flow. IKS-SAC incorporates a communication protocol for knowledge sharing among agents, enabling each agent to access and utilize traffic environment data collected by other agents, effectively addressing the challenge of data processing in asynchronous updates, thereby achieving a comprehensive understanding of the traffic environment within a sparse control framework. Validation of the synthetic data demonstrates that IKS-SAC exhibits superior adaptability and efficiency in managing traffic flow fluctuations and uncertainties, significantly outperforming existing reinforcement learning-based and traditional traffic control methods. The proposed method demonstrates significant advantages in reducing traffic congestion, lowering energy consumption, and mitigating environmental pollution. Full article

Rapid advances in renewable energy technologies offer significant opportunities for the global energy transition and environmental protection. However, due to the fluctuating and intermittent nature of their power generation, which leads to the phenomenon of power abandonment, it has become a key challenge to efficiently consume renewable energy sources and guarantee the reliable operation of the power system. In order to address the above problems, this paper proposes an electric vehicle aggregator (EVA) scheduling strategy based on a two-layer game by constructing a two-layer game model between renewable energy generators (REG) and EVA, where the REG formulates time-sharing tariff strategies in the upper layer to guide the charging and discharging behaviors of electric vehicles, and the EVA respond to the price signals in the lower layer to optimize the large-scale electric vehicle scheduling. For the complexity of large-scale scheduling, this paper introduces the A2C (Advantage Actor-Critic) reinforcement learning algorithm, which combines the value network and the strategy network synergistically to optimize the real-time scheduling process. Based on the case study of wind power, photovoltaic, and wind–solar complementary data in Jilin Province, the results show that the strategy significantly improves the rate of renewable energy consumption (up to 97.88%) and reduces the cost of power purchase by EVA (an average saving of RMB 0.04/kWh), realizing a win–win situation for all parties. The study provides theoretical support for the synergistic optimization of the power system and renewable energy and is of great practical significance for the large-scale application of electric vehicles and new energy consumption. Full article

In the fast-moving consumer goods (FMCG) industry, inventory management is a critical component of supply chain management because it directly impacts cost efficiency and customer satisfaction. For instance, effective inventory management can minimize overstocking and reduce replenishment delays, which are particularly important in multi-echelon supply chain systems characterized by high complexity and dynamic demand. This study proposes a method based on deep reinforcement learning (DRL) aimed at optimizing replenishment decisions in multi-echelon inventory systems for FMCG industries. We designed a Dynamic Replenishment FMCG Multi-Echelon Optimization (ME-DRFO) model and incorporated a Markov Decision Process (MDP) to model the multi-echelon inventory system. By applying an improved Soft Actor–Critic with an adaptive alpha and learning rate (SAC-AlphaLR) algorithm, which introduces adaptive temperature parameters and adaptive learning rate mechanisms, our approach not only dynamically adapts to environmental changes but also effectively balances exploration and exploitation, ultimately achieving global replenishment cost minimization while ensuring supply chain stability. Through numerical experiments, our method demonstrates excellent performance by reducing replenishment costs by 12.31% and decreasing inventory shortages to 2.21%, significantly outperforming traditional methods such as overstocking, Particle Swarm Optimization (PSO), and the standard Soft Actor–Critic (SAC). This research provides new theoretical insights into multi-echelon inventory optimization and practical solutions for effectively managing complex supply chains under uncertain and dynamic conditions. Full article

Spanish film director Pedro Almodóvar has been detected early on by film critics as a Baroque filmmaker, a qualification to which he has agreed in interviews. This promotion of his style is certainly questionable as the word ‘Baroque’ is often used outside of its artistic and historical contexts. It is undeniable, however, that there are many Baroque features in his tragicomedy. One of the key aspects that ties Almodóvar’s early films to Baroque art is their exaggerated and melodramatic storytelling. Like Baroque art, which often featured grandiose and emotionally charged narratives, Almodóvar’s films are filled with intense emotions, complex relationships, and larger-than-life characters. This exaggerated portrayal of human emotions and experiences is a hallmark of Baroque aesthetics, which sought to evoke strong emotional responses from the audience. This paper seeks to focus exclusively on the rise of the director’s style in the last two decades of the 20th century that corresponds to Spain’s problematic and somewhat tragic transition from dictatorship to democracy and explore the ‘Baroque transitions’ that led Almodóvar to national, European and international recognition prior to the obtention of the Academy Awards he received for “All about my mother” in 2000. After defining the Baroqueness of his early filmography, this article will take a closer look at the ricochet trajectory he designed for actors such as Carmen Maura, Victoria Abril, and Antonio Banderas, who will all act in several corresponding roles and embody characters in transition, before becoming emblematic for the public. In the tradition of the Spanish Baroque, Almodóvar will develop his tragic outlook on his ever-changing culture around these iconic actors who will, in turn, unfold the complexity of the transition years for Spanish women and men. Full article

The elevated penetration of renewable energy has seen a significant increase in the integration of inverter-based resources (IBRs) into the electricity network. According to various industrial standards on interconnection and interoperability, IBRs should be able to withstand variability in grid conditions. Positive sequence voltage-oriented control (PSVOC) with a feed-forward decoupling approach is often adopted to ensure closed-loop control of inverters. However, the dynamic response of this control scheme deteriorates during fluctuations in the grid voltage due to the sensitivity of proportional–integral controllers, the presence of the direct- and quadrature-axis voltage terms in the cross-coupling, and predefined saturation limits. As such, a twin delayed deep deterministic policy gradient-based voltage-oriented control (TD3VOC) is formulated and trained to provide effective current control of inverter-based resources under various dynamic conditions of the grid through transfer learning. The actor–critic-based reinforcement learning agent is designed and trained using the model-free Markov decision process through interaction with a grid-connected photovoltaic inverter environment developed in MATLAB/Simulink^® 2023b. Using the standard PSVOC method results in inverter input voltage overshoots of up to 2.50 p.u., with post-fault current restoration times of as high as 0.55 s during asymmetrical faults. The designed TD3VOC technique confines the DC link voltage overshoot to 1.05 p.u. and achieves a low current recovery duration of 0.01 s after fault clearance. In the event of a severe symmetric fault, the conventional control method is unable to restore the inverter operation, leading to integral-time absolute errors of 0.60 and 0.32 for the currents of the d and q axes, respectively. The newly proposed agent-based control strategy restricts cumulative errors to 0.03 and 0.09 for the d and q axes, respectively, thus improving inverter regulation. The results indicate the superior performance of the proposed control scheme in maintaining the stability of the inverter DC link bus voltage, reducing post-fault system recovery time, and limiting negative sequence currents during severe asymmetrical and symmetrical grid faults compared with the conventional PSVOC approach. Full article

Multi-agent systems often face challenges such as elevated communication demands, intricate interactions, and difficulties in transferability. To address the issues of complex information interaction and model scalability, we propose an innovative hierarchical graph attention actor–critic reinforcement learning method. This method naturally models the interactions within a multi-agent system as a graph, employing hierarchical graph attention to capture the complex cooperative and competitive relationships among agents, thereby enhancing their adaptability to dynamic environments. Specifically, graph neural networks encode agent observations as single feature-embedding vectors, maintaining a constant dimensionality irrespective of the number of agents, which improves model scalability. Through the “inter-agent” and “inter-group” attention layers, the embedding vector of each agent is updated into an information-condensed and contextualized state representation, which extracts state-dependent relationships between agents and model interactions at both individual and group levels. We conducted experiments across several multi-agent tasks to assess our proposed method’s effectiveness, stability, and scalability. Furthermore, to enhance the applicability of our method in large-scale tasks, we tested and validated its performance within a curriculum learning training framework, thereby enhancing its transferability. Full article

In arid Turkana County, over 90% of the population is food insecure, and wild edible plants (WEPs) provide 12–30% of dietary intake. However, climate change and overexploitation threaten these crucial resources. This study employed sequential qualitative methods to investigate community perceptions, conservation priorities for WEPs, barriers, and necessary actions in Turkana. It combined participatory community workshops and expert validation interviews. The research revealed critical threats to WEP availability, including climate change, shifting cultural practices, and a lack of natural regeneration. Key conservation barriers included intergenerational knowledge gaps, inadequate policy implementation, and conflicts between immediate needs and long-term conservation goals. In developing conservation plans, the stakeholders identified and prioritized WEP species based on food value, medicinal properties, cultural significance, utility, and drought resistance. The co-developed conservation strategy emphasized both in situ protection measures, such as community awareness programs and local policy enforcement mechanisms, and restoration actions that include planting prioritized WEPs in home gardens and community spaces. Collaborative roles for communities, non-governmental organizations, researchers, and government actors were identified to provide training, resources, and technical support. This strategy also emphasizes the need for incentivization through food/cash-for-work programs and small business grants to promote alternative livelihoods. The strategies align with some of the most-utilized conservation frameworks and principles, and present new ideas such as integrating indigenous knowledge. Expert validation confirmed the feasibility of proposed actions, highlighting the importance of multi-stakeholder approaches. This study contributes to expanding our knowledge base on community-based conservation and provides insights for policymakers, emphasizing WEPs’ critical role in food security, cultural preservation, and ecological resilience. The findings could serve as a model for similar initiatives in other arid regions facing comparable challenges. Full article

This paper constructs an evolutionary game model involving the “central government–local government–coal enterprises” to explore the impact mechanism of central environmental protection inspection on green coal mining. By analyzing the strategic behaviors of the key actors, this study identifies critical factors that influence their decisions. System simulations are conducted to assess the effects of key parameters on system stability and convergence. The findings indicate the following: (1) Increasing inspection costs weaken the central government’s support for green mining, with excessive costs potentially causing regulatory fatigue. Moderating inspection investments is key to sustaining long-term effectiveness. (2) Higher penalties for local governments improve the enforcement of green mining policies, particularly in the mid-term, showing that stringent penalties are an effective regulatory tool. (3) Lower technical costs and greater economic incentives encourage coal enterprises to adopt green mining practices, highlighting the role of innovation and profitability in driving green transitions. (4) Central government subsidies enhance local governments’ short-term enforcement but may lead to dependence if overused. Balanced subsidy policies are essential for sustained policy implementation at the local level. Based on these findings, the paper proposes policy recommendations to improve inspection mechanisms, optimize policy tools, and establish a collaborative regulatory system to ensure the long-term effectiveness of green coal mining. Full article

The capability of UAVs for efficient autonomous navigation and obstacle avoidance in complex and unknown environments is critical for applications in agricultural irrigation, disaster relief and logistics. In this paper, we propose the DPRL (Distributed Privileged Reinforcement Learning) navigation algorithm, an end-to-end policy designed to address the challenge of high-speed autonomous UAV navigation under partially observable environmental conditions. Our approach combines deep reinforcement learning with privileged learning to overcome the impact of observation data corruption caused by partial observability. We leverage an asymmetric Actor–Critic architecture to provide the agent with privileged information during training, which enhances the model’s perceptual capabilities. Additionally, we present a multi-agent exploration strategy across diverse environments to accelerate experience collection, which in turn expedites model convergence. We conducted extensive simulations across various scenarios, benchmarking our DPRL algorithm against state-of-the-art navigation algorithms. The results consistently demonstrate the superior performance of our algorithm in terms of flight efficiency, robustness and overall success rate. Full article

In this paper, a deep reinforcement learning (DRL) approach based on generative adversarial imitation learning (GAIL) and long short-term memory (LSTM) is proposed to resolve tracking control problems for robotic manipulators with saturation constraints and random disturbances, without learning the dynamic and kinematic model of the manipulator. Specifically, it limits the torque and joint angle to a certain range. Firstly, in order to cope with the instability problem during training and obtain a stability policy, soft actor–critic (SAC) and LSTM are combined. The changing trends of joint position over time are more comprehensively captured and understood by employing an LSTM architecture designed for robotic manipulator systems, thereby reducing instability during the training of robotic manipulators for tracking control tasks. Secondly, the obtained policy by SAC-LSTM is used as expert data for GAIL to learn a better control policy. This SAC-LSTM-GAIL (SL-GAIL) algorithm does not need to spend time exploring unknown environments and directly learns the control strategy from stable expert data. Finally, it is demonstrated by the simulation results that the end effector of the robot tracking task is effectively accomplished by the proposed SL-GAIL algorithm, and more superior stability is exhibited in a test environment with interference compared with other algorithms. Full article

This study examines market actors’ perspectives on agri-food data sharing within traceability- and transparency-oriented digital systems, which are crucial for enhancing sustainable food supply chains. Drawing on the ‘Digital Food Passports’ pilot in Poland, the research aimed to identify factors influencing market actors’ willingness to share data to provide reliable and comprehensive information on the origin, journey, and quality of agri-food products. Through thematic analysis, key motivators and barriers to stakeholder engagement were identified. Findings highlight the necessity of a clear value proposition for all actors within the production and distribution chain to invest resources and time in additional data-exchange systems. For farmers, reducing burdensome reporting procedures and providing direct financial incentives were key motivators. Agri-food processing, transport, and packaging companies viewed data collaboration as a practical tool to ensure high-quality raw materials and promote premium-priced food. Appropriate data management policies were critical for all stakeholders. While sustainability was recognized as important, opportunities for collaboration going beyond economic considerations were not widely explored. Additionally, concerns about how data will be interpreted—even among producers demonstrating sustainable practices—emerged as a significant issue, a topic not extensively discussed in the existing literature. These findings underscore the need for data-sharing strategies that better align economic benefits with broader sustainability goals. Further research should also explore strategies to mitigate concerns over data misinterpretation to encourage greater involvement in data-sharing initiatives. Full article

As renewable energy sources become more integrated into the power grid, traditional virtual synchronous generator (VSG) control strategies have become inadequate for the current low-damping, low-inertia power systems. Therefore, this paper proposes a VSG inertia and damping adaptive control method based on multi-agent deep deterministic policy gradient (MADDPG). The paper first introduces the working principles of virtual synchronous generators and establishes a corresponding VSG model. Based on this model, the influence of variations in virtual inertia (J) and damping (D) coefficients on fluctuations in active power output is examined, defining the action space for J and D. The proposed method is mainly divided into two phases: “centralized training and decentralized execution”. In the centralized training phase, each agent’s critic network shares global observation and action information to guide the actor network in policy optimization. In the decentralized execution phase, agents observe frequency deviations and the rate at which angular frequency changes, using reinforcement learning algorithms to adjust the virtual inertia J and damping coefficient D in real time. Finally, the effectiveness of the proposed MADDPG control strategy is validated through comparison with adaptive control and DDPG control methods. Full article

This article explores the concept of inclusive education in contexts of forced displacement, where refugeehood intersects with special educational needs and disabilities (SEND), as well as gender, poverty, and overlapping forms of discrimination. Drawing on extensive engagement with a refugee-led, non-formal educational organization in Lebanon, we revisit inclusion for refugee children with SEND through a bottom-up lens. We consider inclusion within Lebanon’s sociopolitical landscape, focusing on a community of educators, most of whom are refugees themselves. Grounded in decolonial feminist epistemologies and critical refugee studies, we highlight the role of educators as cultural actors who employ engaged pedagogies to humanize the educational experiences of refugee children with SEND. By challenging traditional top-down, outcome-oriented policies that focus solely on structural access, this paper advocates for an alternative framework based on refugee educators’ orientations to working with children with SEND. This framework prioritizes holistic, context-sensitive approaches to inclusion and underscores the importance of humanizing education for refugees. Full article

Fast charge/discharge scheduling of battery storage systems is essential in microgrids to effectively balance variable renewable energy sources, meet fluctuating demand, and maintain grid stability. To achieve this, parallel processing is employed, allowing batteries to respond instantly to dynamic conditions. By managing the complexity, high data volume, and rapid decision-making requirements in real time, parallel processing ensures that the microgrid operates with stability, efficiency, and safety. With the application of deep reinforcement learning (DRL) in scheduling algorithm design, the demand for computational power has further increased significantly. To address this challenge, we propose a Ray-based parallel framework to accelerate the development of fast charge/discharge scheduling for battery storage systems in microgrids. We demonstrate how to implement a real-world scheduling problem in the framework. We focused on minimizing power losses and reducing the ramping rate of net loads by leveraging the Asynchronous Advantage Actor Critic (A3C) algorithms and the features of the Ray cluster for real-time decision making. Multiple instances of OpenDSS were executed concurrently, with each instance simulating a distinct environment and efficiently processing input data. Additionally, Numba CUDA was utilized to facilitate GPU acceleration of shared memory, significantly enhancing the performance of the computationally intensive reward function in A3C. The proposed framework enhanced scheduling performance, enabling efficient energy management in complex, dynamic microgrid environments. Full article