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In recent years, automation in construction scheduling has gained popularity due to advancements in digital construction, yet it has not achieved widespread adoption. Significant challenges remain in developing adaptive schedules that effectively manage unforeseen events and construction delays. This study addresses a critical research gap by evaluating the automation levels of individual construction planning processes, an area previously underexplored. Employing a systematic literature review, this study investigates the state of the art in automated, dynamic and adaptive scheduling techniques. The review examined proposed planning procedures, assessing the extent of automation in key aspects of construction scheduling, including task sequencing, resource allocation and task duration estimation, with a focus on building information modelling (BIM) integration. The analysis reveals limited adoption of automated scheduling, BIM technologies and adaptive scheduling methods. Future research should explore advanced automation approaches, enhance BIM integration and develop adaptive scheduling solutions to improve efficiency and responsiveness in construction management.

Given the dynamic and uncertain production environment of job shops, a scheduling strategy with adaptive features must be developed to fit variational production factors. Therefore, a dynamic scheduling system model based on multi-agent technology, including machine, buffer, state, and job agents, was built. A weighted Q-learning algorithm based on clustering and dynamic search was used to determine the most suitable operation and to optimize production. To address the large state space problem caused by changes in the system state, four state features were extracted. The dimension of the system state was decreased through the clustering method. To reduce the error between the actual system states and clustering ones, the state difference degree was defined and integrated with the iteration formula of the Q function. To select the optimal state-action pair, improved search and iteration update strategies were proposed. Convergence analysis of the proposed algorithm and simulation experiments indicated that the proposed adaptive strategy is well adaptable and effective in different scheduling environments, and shows better performance in complex environments. The two contributions of this research are as follows: (1) a dynamic greedy search strategy was developed to avoid blind searching in traditional strategy. (2) Weighted iteration update of the Q function, including the weighted mean of the maximum fuzzy earning, was designed to improve the speed and accuracy of the improved learning algorithm.

Modern manufacturing systems build on an effective scheduling scheme that makes full use of the system resource to increase the production, in which an important aspect is how to minimize the makespan for a certain production task (i.e., the time that elapses from the start of work to the end) in order to achieve the economic profit. This can be a difficult problem, especially when the production flow is complicated and production tasks may suddenly change. As a consequence, exact approaches are not able to schedule the production in a short time. In this paper, an adaptive scheduling algorithm is proposed to address the makespan minimization in the dynamic job shop scheduling problem. Instead of a linear order, the directed acyclic graph is used to represent the complex precedence constraints among operations in jobs. Inspired by the heterogeneous earliest finish time (HEFT) algorithm, the adaptive scheduling algorithm can make some fast adaptations on the fly to accommodate new jobs which continuously arrive in a manufacturing system. The performance of the proposed adaptive HEFT algorithm is compared with other state-of-the-art algorithms and further heuristic methods for minimizing the makespan. Extensive experimental results demonstrate the high efficiency of the proposed approach.

The evolution of digital education necessitates robust computational frameworks to address the complexities inherent in remote learning environments. Traditional scheduling mechanisms often fall short in accommodating the dynamic nature of learner engagement and the asynchronous delivery of content. To bridge this gap, we introduce a novel computational model that leverages reinforcement learning to optimize content delivery schedules. Central to our approach is the Attentive Stochastic Transition Estimation Network (ASTEN), which models the probabilistic transitions of learner states, accounting for factors such as attention variability and feedback delays. Complementing ASTEN is the Selective Informative Delivery Strategy (SIDS), a decision-theoretic framework that determines optimal content emission based on real-time uncertainty assessments and pedagogical utility. Our approach captures nuanced behavioral trends, such as sporadic learner interaction, temporal learning decay, and individualized attention cycles, thereby enabling a more responsive and tailored instructional strategy. By explicitly integrating cognitive and behavioral signals within the scheduling framework, our model facilitates the delivery of content that aligns with each learner’s evolving state. Empirical evaluations demonstrate that our integrated model significantly enhances learning outcomes by adapting to individual learner trajectories and mitigating the challenges posed by sparse feedback. This research contributes to the theoretical foundations of computational learning models and offers practical insights for the development of adaptive educational technologies, particularly in environments where traditional one-size-fits-all approaches prove inadequate.

This paper presents an approach for adaptive scheduling and robustness optimization in global logistics networks by integrating multimodal deep reinforcement learning with Internet of Things (IoT) technologies. We propose an integrated framework comprising a multimodal data fusion mechanism that synthesizes heterogeneous IoT sensor data, historical records, and contextual information; an adaptive deep reinforcement learning architecture that generates dynamic scheduling policies; and a multi-objective robust optimization method that balances operational efficiency with system resilience. The framework addresses key challenges in global logistics including demand volatility, transportation disruptions, and environmental uncertainties. Comprehensive experiments conducted on real-world logistics datasets demonstrate that our approach outperforms traditional methods with an 18.7% reduction in operational costs, 12.4% improvement in service levels, and significantly enhanced robustness under various disruption scenarios. The proposed method maintains 83% performance stability during complex disruptions compared to 51–72% for alternative approaches, while keeping computational requirements feasible for practical deployment. This research demonstrates potential contributions to AI-driven logistics operations management by showing improved supply chain performance through multimodal learning and robust optimization techniques.

Human memory is imperfect; thus, periodic review is required for the long-term preservation of knowledge and skills. However, students at every educational level are challenged by an ever-growing amount of material to review and an ongoing imperative to master new material. We developed a method for efficient, systematic, personalized review that combines statistical techniques for inferring individual differences with a psychological theory of memory. The method was integrated into a semester-long middle-school foreign-language course via retrieval-practice software. Using a cumulative exam administered after the semester's end, we compared time-matched review strategies and found that personalized review yielded a 16.5% boost in course retention over current educational practice (massed study) and a 10.0% improvement over a one-size-fits-all strategy for spaced study.

The modern complicated manufacturing industry and smart manufacturing tendency have imposed new requirements on the scheduling method, such as self-regulation and self-learning capabilities. While traditional scheduling methods cannot meet these needs due to their rigidity. Self-learning is an inherent ability of reinforcement learning (RL) algorithm inhered from its continuous learning and trial-and-error characteristics. Self-regulation of scheduling could be enabled by the emerging digital twin (DT) technology because of its virtual-real mapping and mutual control characteristics. This paper proposed a DT-enabled adaptive scheduling based on the improved proximal policy optimization RL algorithm, which was called explicit exploration and asynchronous update proximal policy optimization algorithm (E2APPO). Firstly, the DT-enabled scheduling system framework was designed to enhance the interaction between the virtual and the physical job shops, strengthening the self-regulation of the scheduling model. Secondly, an innovative action selection strategy and an asynchronous update mechanism were proposed to improve the optimization algorithm to strengthen the self-learning ability of the scheduling model. Lastly, the proposed scheduling model was extensively tested in comparison with heuristic and meta-heuristic algorithms, such as well-known scheduling rules and genetic algorithms, as well as other existing scheduling methods based on reinforcement learning. The comparisons have proved both the effectiveness and advancement of the proposed DT-enabled adaptive scheduling strategy.

Multi-numerology waveform-based 5G new radio (NR) systems offer great flexibility for different requirements of users and services. However, there is a new type of problem that is defined as inter-numerology interference (INI) between multiple numerologies. This paper proposes novel scheduling and resource allocation techniques to enhance the overall reliability and also provide extra protection for ultra-reliable and low-latency communications (uRLLC) users and cell edge users against INI. Proposed methods are useful for Internet of Things (IoT) communications, and they do not cause additional spectral usage, computational complexity, and latency. Practical INI-aware schemes in this paper include fractional numerology domain (FND) scheduling, power difference-based (PDB) scheduling, and machine learning-based (MLB) scheduling algorithms. INI and signal-to-interference ratio (SIR) results for multi-numerology systems are obtained through computer simulations to show trade-offs between different scenarios and success of the proposed algorithms.

This study presents a novel LSTM–CNN-based adaptive scheduling framework (LSTM-CNN–AS) designed to improve real-time energy management and extend the lifespan of lithium-ion Battery Energy Storage Systems (BESS) in rural and resource-constrained microgrids. In contrast to conventional methods that prioritize economic optimization, the proposed framework incorporates state of health (SOH) aware control and adaptive closed-loop scheduling to enhance operational reliability and battery longevity. The architecture combines Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for accurate SOH estimation, with lightweight Multi-Layer Perceptron (MLP) models supporting real-time scheduling and state of charge (SOC) regulation. Operational safety is maintained by keeping SOC within 20–80% and SOH above 70%. The proposed model training and validation are conducted using two real-world datasets: the Mendeley Lithium-Ion SOH Test Dataset and the DKA Solar System Dataset from Alice Springs, both sampled at 5-min intervals. Performance is evaluated across three operational scenarios, which are 2C charging with random discharge; random charging with 3C discharge; and fully random profiles, achieving up to 44% reduction in MAE and an R2; score of 0.9767. A one-month deployment demonstrates a 30% reduction in charging time and 40% lower operational costs, confirming the framework’s effectiveness and scalability for rural microgrid applications.

The development of industrial-enabling technology, such as the industrial Internet of Things and physical network system, makes it possible to use real-time information in production-logistics scheduling. Real-time information in an intelligent factory is random, such as the arrival of customers’ jobs, and fuzzy, such as the processing time of Production-Logistics Resources. Besides, the coordination of production and logistic resources in a flexible workshop is also a hot issue. The availability of this information will enhance the quality of making scheduling decisions. However, when and how to use this information to realize the adaptive collaboration of Production-Logistics Resources are vital issues. Therefore, this paper studies the above problems by establishing a real-time reaction scheduling framework of Production-Logistics Resources dynamic cooperation. Firstly, a real-time task triggering strategy to maximize information utilization is proposed to explore when to use real-time information. Secondly, a collaborative method for Production-Logistics Resources is studied to explore how to use real-time information. Thirdly, a real-time self-adaptive scheduling algorithm based on information entropy is utilized to obtain a stable and feasible solution. Finally, the effectiveness and advancement of the proposed method are verified by a practical case.