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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.

This paper studied the FMS dynamic scheduling problem which was based on Petri net FMS static scheduling optimization algorithm, which in accorder to solve the FMS actual production scheduling problems. A rolling window dynamic re-scheduling strategy was proposed which based on event driven and cycle driven. Then take the emergency machine failure often appearing in the actual workshop for example, this scheduling strategy was analyzed and applied to dynamic simulation and finally the effectiveness of the dynamic scheduling strategy was verified.

In most real-world environments, scheduling is an ongoing reactive process where the presence of a variety of unexpected disruptions is usually inevitable, and continually forces reconsideration and revision of pre-established schedules. Many of the approaches developed to solve the problem of static scheduling are often impractical in real-world environments, and the near-optimal schedules with respect to the estimated data may become obsolete when they are released to the shop floor. This paper outlines the limitations of the static approaches to scheduling in the presence of real-time information and presents a number of issues that have come up in recent years on dynamic scheduling. The paper defines the problem of dynamic scheduling and provides a review of the state-of-the-art of currently developing research on dynamic scheduling. The principles of several dynamic scheduling techniques, namely, heuristics, meta-heuristics, multi-agent systems, and other artificial intelligence techniques are described in detail, followed by a discussion and comparison of their potential.

Dynamic scheduling problems have been receiving increasing attention in recent years due to their practical implications. To realize real-time and the intelligent decision-making of dynamic scheduling, we studied dynamic permutation flowshop scheduling problem (PFSP) with new job arrival using deep reinforcement learning (DRL). A system architecture for solving dynamic PFSP using DRL is proposed, and the mathematical model to minimize total tardiness cost is established. Additionally, the intelligent scheduling system based on DRL is modeled, with state features, actions, and reward designed. Moreover, the advantage actor-critic (A2C) algorithm is adapted to train the scheduling agent. The learning curve indicates that the scheduling agent learned to generate better solutions efficiently during training. Extensive experiments are carried out to compare the A2C-based scheduling agent with every single action, other DRL algorithms, and meta-heuristics. The results show the well performance of the A2C-based scheduling agent considering solution quality, CPU times, and generalization. Notably, the trained agent generates a scheduling action only in 2.16 ms on average, which is almost instantaneous and can be used for real-time scheduling. Our work can help to build a self-learning, real-time optimizing, and intelligent decision-making scheduling system.

Flexible job shop scheduling problem is a NP-hard combinatorial optimization problem, which has significant applications in the field of workshop scheduling and intelligent manufacturing. Due to its complexity and significance, lots of attention have been paid to tackle this problem. This paper reviews some of the researches on this problem, by presenting and classifying the different criteria, constraints, and solution approaches. The existing solution methods for the flexible job shop scheduling problem in this literature are classified into exact algorithms, heuristics, and meta-heuristics, which are thoroughly reviewed. Particularly, the paper highlights the flexible job shop scheduling problem in the context of dynamic events and preventive maintenance. These dynamic events, such as machine breakdowns and unexpected changes in job requirements, present additional challenges to the scheduling problem. Furthermore, this paper analyzes the development trends in the manufacturing industry and summarizes detailed future research opportunities for the flexible job shop scheduling problem.

Industries require, nowadays, to be more adaptable to unforeseen real-time events as well as to the rapid evolution of their market (e.g. multiplication of customers, increasingly personalized and unpredictable demand, etc.). To meet these challenges, manufacturers need new solutions to update their production plan when a change in the production system or its environment occurs. In this context, our research work deals with a dynamic scheduling problem of a real Hybrid Flow Shop considering the specific constraints of a perfume manufacturing company. This paper proposes a Digital Twin-driven dynamic scheduling approach based on the combination of both optimization and simulation. For the optimization, we have developed a mixed integer linear programming (MILP) scheduling model taking into account the main specific scheduling requirements of our case study. Regarding the simulation approach, a 3D shop floor model has been developed including the additional stochastic aspects and constraints which are difficult or impossible to model with a MILP approach. These two models are connected with the real shop floor to create a digital twin (DT). The developed DT allows the re-scheduling of production according to internal and external events. Finally, validation scenarios on a perfume case study have been designed and implemented in order to demonstrate the feasibility and the relevance of the proposed digital twin-driven dynamic scheduling approach.

As a model of make-to-order production, we consider an admission control problem for a multiclass, single-server queue. The production system serves multiple demand streams, each having a rigid due-date lead time. To meet the due-date constraints, a system manager may reject orders when a backlog of work is judged to be excessive, thereby incurring lost revenues. The system manager strives to minimize long-run average lost revenues by dynamically making admission control and sequencing decisions. Under heavy-traffic conditions the scheduling problem is approximated by a Brownian control problem, which is solved explicitly. Interpreting this solution in the context of the original queueing system, a nested threshold policy is proposed. A simulation experiment is performed to demonstrate the effectiveness of this policy.

As large-scale data processing tasks continue to grow in volume and complexity, improving the efficiency of computational resource utilization and task execution performance has emerged as a central challenge in cloud computing environments. In response, this study proposes an adaptive parallel processing algorithm that incorporates a dynamic scheduling strategy, designed to optimize task allocation and execution workflows within distributed systems. To assess the algorithm's performance, experiments were conducted across three platforms—Amazon Web Services (AWS), Google Cloud, and a local computing cluster—using three representative large-scale public datasets. These tasks included a structured classification task using the Kaggle Titanic dataset, an image processing task using the Google Open Images dataset (which contains over 90 million images), and a text processing task based on the Common Crawl dataset, which comprises content from billions of web pages. On the Google Cloud platform, the integration of dynamic scheduling reduced execution time to 13.5 hours. It also demonstrated strong adaptability and overall system stability, especially when managing complex task distributions and largescale data. When paired with the adaptive parallel processing algorithm, the dynamic scheduling strategy achieved a 5.2× speedup compared to serial execution. This reduced the total processing time from 12 hours to 2.3 hours, while maintaining high resource utilization and stable task scheduling. These findings underscore the algorithm's substantial potential in enhancing the performance of large-scale data processing and offer practical implications for algorithmic optimization and resource management in cloud-based environments.

Automated guided vehicle (AGV) scheduling has become a hot topic in recent years as manufacturing systems become flexible and intelligent. However, little research regards dynamic AGV scheduling considering energy consumption, particularly battery replacement. This paper proposes a novel method that employs deep reinforcement learning to address the dynamic scheduling of energy-efficient AGVs with battery replacement in production logistics systems. The bi-objective joint optimization problem of AGV scheduling and battery replacement management is modeled as a Markov Decision Process, which supports data-driven decision-making. Then, this paper constructs a deep reinforcement learning-based optimization architecture and develops a novel dueling deep double Q network algorithm to maximize the long-term rewards for optimizing material handling’s tardiness and energy consumption. Numerical experiments and a case study demonstrate that the proposed algorithm is more efficient and cleaner than state-of-the-art methods. The proposed method can significantly improve customer satisfaction and reduce production costs within flexible manufacturing processes, particularly in Industry 4.0.

With the explosive growth of terminal devices, scheduling massive parallel task streams has become a core challenge for distributed platforms. For computing resource providers, enhancing reliability, shortening response times, and reducing costs are significant challenges, particularly in achieving energy efficiency through scheduling to realize green computing. This paper investigates the heterogeneous parallel task flow scheduling problem to minimize system energy consumption under response time constraints. First, for a set of independent tasks capable of parallel computation on heterogeneous terminals, the task scheduling is performed according to the computational resource capabilities of each terminal. The problem is modeled as a mixed-integer nonlinear programming problem using a Directed Acyclic Graph as the input model. Then, a dynamic scheduling method based on heuristic and reinforcement learning algorithms is proposed to schedule the task flows. Furthermore, dynamic redundancy is applied to certain tasks based on reliability analysis to enhance system fault tolerance and improve service quality. Experimental results show that our method can achieve significant improvements, reducing energy consumption by 14.3% compared to existing approaches on two practical workflow instances.