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Keywords = production rescheduling

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17 pages, 1509 KiB  
Article
Objective Functions for Minimizing Rescheduling Changes in Production Control
by Gyula Kulcsár, Mónika Kulcsárné Forrai and Ákos Cservenák
Automation 2025, 6(3), 30; https://doi.org/10.3390/automation6030030 - 11 Jul 2025
Viewed by 225
Abstract
This paper presents an advanced rescheduling approach that jointly applies two sets of objective functions within a novel multi-objective search algorithm and a production simulation of the manufacturing system. The role of the first set of objective functions is to optimize the performance [...] Read more.
This paper presents an advanced rescheduling approach that jointly applies two sets of objective functions within a novel multi-objective search algorithm and a production simulation of the manufacturing system. The role of the first set of objective functions is to optimize the performance of production systems, while the second newly proposed set of objective functions aims to minimize the intervention changes from the original schedule, thereby supporting schedule stability and smooth manufacturing processes. The combined use of these two objective sets is ensured by a flexible candidate-qualification method, which allows for priorities to be assigned to each objective function, offering precise control over the rescheduling process. The applicability of this approach is presented through an example of an extended flexible flow shop manufacturing system. A new test problem containing 16 objective functions has been developed. The effectiveness of the proposed new objective functions and rescheduling method is validated by a simulation model. The obtained numerical results are also presented in this paper. The aim of this study is not to compare different search algorithms but rather to demonstrate the beneficial impact of change-minimizing objective functions within a given search framework. Full article
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34 pages, 1253 KiB  
Article
A Discrete Improved Gray Wolf Optimization Algorithm for Dynamic Distributed Flexible Job Shop Scheduling Considering Random Job Arrivals and Machine Breakdowns
by Chun Wang, Jiapeng Chen, Binzi Xu and Sheng Liu
Processes 2025, 13(7), 1987; https://doi.org/10.3390/pr13071987 - 24 Jun 2025
Viewed by 425
Abstract
Dueto uncertainties in real-world production, dynamic factors have become increasingly critical in the research of distributed flexible job shop scheduling problems. Effectively responding to dynamic events can significantly enhance the adaptability and quality of scheduling solutions, thereby improving the resilience of manufacturing systems. [...] Read more.
Dueto uncertainties in real-world production, dynamic factors have become increasingly critical in the research of distributed flexible job shop scheduling problems. Effectively responding to dynamic events can significantly enhance the adaptability and quality of scheduling solutions, thereby improving the resilience of manufacturing systems. This study addresses the dynamic distributed flexible job shop scheduling problem, which involves random job arrivals and machine breakdowns, and proposes an effective discrete improved gray wolf optimization (DIGWO) algorithm-based predictive–reactive method. The first contribution of our work lies in its dynamic scheduling strategy: a periodic- and event-driven approach is used to capture the dynamic nature of the problem, and a static scheduling window is constructed based on updated factory and workshop statuses to convert dynamic scheduling into static scheduling at each rescheduling point. Second, a mathematical model of multi-objective distributed flexible job shop scheduling (MODDFJSP) is established, optimizing makespan, tardiness, maximal factory load, and stability. The novelty of the model is that it is capable of optimizing both production efficiency and operational stability in the workshop. Third, by designing an efficacious initialization mechanism, prey search, and an external archive, the DIGWO algorithm is developed to solve conflicting objectives and search for a set of trade-off solutions. Experimental results in a simulated dynamic distributed flexible job shop demonstrate that DIGWO outperforms three well-known algorithms (NSGA-II, SPEA2, and MOEA/D). The proposed method also surpasses completely reactive scheduling approaches based on rule combinations. This study provides a reference for distributed manufacturing systems facing random job arrivals and machine breakdowns. Full article
(This article belongs to the Section AI-Enabled Process Engineering)
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21 pages, 1748 KiB  
Article
Energy-Efficient Scheduling for Resilient Container-Supply Hybrid Flow Shops Under Transportation Constraints and Stochastic Arrivals
by Huaixia Shi, Huaqiang Si and Jiyun Qin
J. Mar. Sci. Eng. 2025, 13(6), 1153; https://doi.org/10.3390/jmse13061153 - 11 Jun 2025
Viewed by 290
Abstract
Although dynamic, energy-efficient container-supply hybrid flow shops have attracted increasing attention, most existing research overlooks how transportation within container production affects makespan, resilience, and sustainability. To bridge this gap, we frame a resilient, energy-efficient container-supply hybrid flow shop (TDEHFSP) scheduling model that utilizes [...] Read more.
Although dynamic, energy-efficient container-supply hybrid flow shops have attracted increasing attention, most existing research overlooks how transportation within container production affects makespan, resilience, and sustainability. To bridge this gap, we frame a resilient, energy-efficient container-supply hybrid flow shop (TDEHFSP) scheduling model that utilizes vehicle transportation to maximize operational efficiency. To address the TDEHFSP model, the study proposes a Q-learning-based multi-swarm collaborative optimization algorithm (Q-MGCOA). The algorithm integrates a time gap left-shift scheduling strategy with a machine on–off control mechanism to construct an energy-saving optimization framework. Additionally, a predictive–reactive dynamic rescheduling model is introduced to address unexpected task disturbances. To validate the algorithm’s effectiveness, 36 benchmark test cases with varying scales are designed for horizontal comparison. Results show that the proposed Q-MGCOA outperforms benchmarks on convergence, diversity, and supply-chain resilience while lowering energy utilization. Moreover, it achieves about an 8% reduction in energy consumption compared to traditional algorithms. These findings reveal actionable insights for next-generation intelligent, low-carbon container production. Full article
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20 pages, 3536 KiB  
Article
A Multi-Trigger Mechanism Design for Rescheduling Decision Assistance in Smart Job Shops Based on Machine Learning
by Rong Duan, Siqi Wang, Ya Liu, Wei Yan, Zhigang Jiang and Zhiqiang Pan
Sustainability 2025, 17(5), 2198; https://doi.org/10.3390/su17052198 - 3 Mar 2025
Viewed by 755
Abstract
The empowerment of lean intelligent manufacturing technologies has provided a solid foundation for enterprises to achieve a balance between economic benefits and sustainable development. In production workshops, various disruptive factors, especially in multi-variety small-batch production environments, often lead to deviations from the planned [...] Read more.
The empowerment of lean intelligent manufacturing technologies has provided a solid foundation for enterprises to achieve a balance between economic benefits and sustainable development. In production workshops, various disruptive factors, especially in multi-variety small-batch production environments, often lead to deviations from the planned schedule. This creates an urgent need to enhance the workshop’s dynamic responsiveness and self-regulation capabilities. Existing single-trigger mechanisms in job shops focus on changes in overall performance or deviations from production goals but lack a representation of the varying degrees of impact on different equipment under multiple disturbances. This results in either over-scheduling or under-scheduling in terms of scope, thereby impacting the optimization of production efficiency and resource utilization. To address this, this paper proposes a method for coordinated decision-making on rescheduling timing and location in intelligent job shops under disturbance environments. First, by analyzing the relationship between disturbance impact and the scope of rescheduling implementation, a mapping relationship is established between disturbance impact and disturbance response hierarchy. A trigger is set up on each piece of equipment to characterize the differences in the degree of impact on different equipment, which not only reduces the complexity of disturbance information processing but also provides support for specific location decisions for disturbance response. Second, a decision module for the triggers is constructed using a multilayer perceptron, establishing a mapping relationship between process and workpiece data attributes and response categories. Based on the basic processing units of the manufacturing process and the relevant quantitative indicators of the processed objects, disturbance response strategies are generated. Finally, through a case study, the proposed method is evaluated and validated in an intelligent factory setting. The new rescheduling decision support method can effectively make timing and location decisions for disturbance events. Full article
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30 pages, 4768 KiB  
Article
Dynamic Scheduling in Identical Parallel-Machine Environments: A Multi-Purpose Intelligent Utility Approach
by Mahmut İbrahim Ulucak and Hadi Gökçen
Appl. Sci. 2025, 15(5), 2483; https://doi.org/10.3390/app15052483 - 25 Feb 2025
Viewed by 970
Abstract
This paper presents a robust and adaptable framework for predictive–reactive rescheduling in identical parallel-machine environments. The proposed Multi-Purpose Intelligent Utility (MIU) methodology utilizes heuristic methods to efficiently address the computational challenges associated with NP-hard scheduling problems. By incorporating 13 diverse dispatching rules, the [...] Read more.
This paper presents a robust and adaptable framework for predictive–reactive rescheduling in identical parallel-machine environments. The proposed Multi-Purpose Intelligent Utility (MIU) methodology utilizes heuristic methods to efficiently address the computational challenges associated with NP-hard scheduling problems. By incorporating 13 diverse dispatching rules, the MIU framework provides a flexible and adaptive approach to balancing critical production objectives. It effectively minimizes total weighted tardiness and the number of tardy jobs while maintaining key performance metrics like stability, robustness, and nervousness. In dynamic manufacturing environments, schedule congestion and unforeseen disruptions often lead to inefficiencies and delays. Unlike traditional event-driven approaches, MIU adopts a periodic rescheduling strategy, enabling proactive adaptation to evolving production conditions. Comprehensive rescheduling ensures system-wide adjustments to disruptions, such as stochastic changes in processing times and rework requirements, without sacrificing overall performance. Empirical evaluations show that MIU significantly outperforms conventional methods, reducing total weighted tardiness by 50% and the number of tardy jobs by 27% on average across various scenarios. Furthermore, this study introduces novel quantifications for nervousness, expanding the scope of stability and robustness evaluations in scheduling research. This work contributes to the ongoing discourse on scheduling methodologies by bridging theoretical research with practical industrial applications, particularly in high-stakes production settings. By addressing the trade-offs between improving the objective function or improving the rescheduling performance, MIU provides a comprehensive solution framework that enhances operational performance and adaptability in complex manufacturing environments. Full article
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23 pages, 3835 KiB  
Article
Discrete Multi-Objective Grey Wolf Algorithm Applied to Dynamic Distributed Flexible Job Shop Scheduling Problem with Variable Processing Times
by Jiapeng Chen, Chun Wang, Binzi Xu and Sheng Liu
Appl. Sci. 2025, 15(5), 2281; https://doi.org/10.3390/app15052281 - 20 Feb 2025
Viewed by 721
Abstract
Uncertainty in processing times is a key issue in distributed production; it severely affects scheduling accuracy. In this study, we investigate a dynamic distributed flexible job shop scheduling problem with variable processing times (DDFJSP-VPT), in which the processing time follows a normal distribution. [...] Read more.
Uncertainty in processing times is a key issue in distributed production; it severely affects scheduling accuracy. In this study, we investigate a dynamic distributed flexible job shop scheduling problem with variable processing times (DDFJSP-VPT), in which the processing time follows a normal distribution. First, the mathematical model is established by simultaneously considering the makespan, tardiness, and total factory load. Second, a chance-constrained approach is employed to predict uncertain processing times to generate a robust initial schedule. Then, a heuristic scheduling method which involves a left-shift strategy, an insertion-based local adjustment strategy, and a DMOGWO-based global rescheduling strategy is developed to dynamically adjust the scheduling plan in response to the context of uncertainty. Moreover, a hybrid initialization scheme, discrete crossover, and mutation operations are designed to generate a high-quality initial population and update the wolf pack, enabling GWO to effectively solve the distributed flexible job shop scheduling problem. Based on the parameter sensitivity study and a comparison with four algorithms, the algorithm’s stability and effectiveness in both static and dynamic environments are demonstrated. Finally, the experimental results show that our method can achieve much better performance than other rules-based reactive scheduling methods and the hybrid-shift strategy. The utility of the prediction strategy is also validated. Full article
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39 pages, 6324 KiB  
Article
Solving Dynamic Multi-Objective Flexible Job Shop Scheduling Problems Using a Dual-Level Integrated Deep Q-Network Approach
by Hua Xu, Jianlu Zheng, Lingxiang Huang, Juntai Tao and Chenjie Zhang
Processes 2025, 13(2), 386; https://doi.org/10.3390/pr13020386 - 31 Jan 2025
Cited by 1 | Viewed by 1379
Abstract
Economic performance in modern manufacturing enterprises is often influenced by random dynamic events, requiring real-time scheduling to manage multiple conflicting production objectives simultaneously. However, traditional scheduling methods often fall short due to their limited responsiveness in dynamic environments. To address this challenge, this [...] Read more.
Economic performance in modern manufacturing enterprises is often influenced by random dynamic events, requiring real-time scheduling to manage multiple conflicting production objectives simultaneously. However, traditional scheduling methods often fall short due to their limited responsiveness in dynamic environments. To address this challenge, this paper proposes an innovative online rescheduling framework called the Dual-Level Integrated Deep Q-Network (DLIDQN). This framework is designed to solve the dynamic multi-objective flexible job shop scheduling problem (DMOFJSP), which is affected by six types of dynamic events: new job insertion, job operation modification, job deletion, machine addition, machine tool replacement, and machine breakdown. The optimization focuses on three key objectives: minimizing makespan, maximizing average machine utilization (Uave), and minimizing average job tardiness rate (TRave). The DLIDQN framework leverages a hierarchical reinforcement learning approach and consists of two integrated IDQN-based agents. The high-level IDQN serves as the decision-maker during rescheduling, implementing dual-level decision-making by dynamically selecting optimization objectives based on the current system state and guiding the low-level IDQN’s actions. To meet diverse optimization requirements, two reward mechanisms are designed, focusing on job tardiness and machine utilization, respectively. The low-level IDQN acts as the executor, selecting the best scheduling rules to achieve the optimization goals determined by the high-level agent. To improve scheduling adaptability, nine composite scheduling rules are introduced, enabling the low-level IDQN to flexibly choose strategies for job sequencing and machine assignment, effectively addressing both sub-tasks to achieve optimal scheduling performance. Additionally, a local search algorithm is incorporated to further enhance efficiency by optimizing idle time between jobs. The numerical experimental results show that in 27 test scenarios, the DLIDQN framework consistently outperforms all proposed composite scheduling rules in terms of makespan, surpasses the widely used single scheduling rules in 26 instances, and always exceeds other reinforcement learning-based methods. Regarding the Uave metric, the framework demonstrates superiority in 21 instances over all composite scheduling rules and maintains a consistent advantage over single scheduling rules and other RL-based strategies. For the TRave metric, DLIDQN outperforms composite and single scheduling rules in 20 instances and surpasses other RL-based methods in 25 instances. Specifically, compared to the baseline methods, our model achieves maximum performance improvements of approximately 37%, 34%, and 30% for the three objectives, respectively. These results validate the robustness and adaptability of the proposed framework in dynamic manufacturing environments and highlight its significant potential to enhance scheduling efficiency and economic benefits. Full article
(This article belongs to the Section Automation Control Systems)
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18 pages, 2560 KiB  
Article
A Mixed Integer Linear Programming Model for Rapid Rescheduling in Ship and Offshore Unit Design Projects
by Kyeongho Kim, Minjoo Choi, Haram Seo, Jaekyeong Lee, Jihong Kim and Shinhyo Kim
J. Mar. Sci. Eng. 2025, 13(2), 222; https://doi.org/10.3390/jmse13020222 - 24 Jan 2025
Viewed by 939
Abstract
Shipbuilding and offshore projects frequently require schedule adjustments due to unforeseen factors such as material supply delays, technical issues, and adverse weather conditions. These adjustments are often managed manually, resulting in significant time consumption and an increased risk of human error. Unlike production [...] Read more.
Shipbuilding and offshore projects frequently require schedule adjustments due to unforeseen factors such as material supply delays, technical issues, and adverse weather conditions. These adjustments are often managed manually, resulting in significant time consumption and an increased risk of human error. Unlike production scheduling, little attention has been given to design scheduling, particularly in the context of rescheduling. To address this gap, this paper presents an optimization model that automates the rescheduling process for shipbuilding and offshore unit design projects. The model generates updated schedules that accommodate necessary changes while minimizing deviations from the initial schedule. In a real-world case involving 857 tasks, the model generated a schedule in under one second, preserving approximately 80% of the original schedule and achieving a 20% improvement in adherence compared to the original scheduling method. Furthermore, the model demonstrated exceptional scalability by efficiently generating optimized schedules for 108,700 tasks in under three minutes. These results demonstrate the model’s capability to provide rapid, efficient, and scalable rescheduling solutions, enabling quick and iterative refinement processes. Full article
(This article belongs to the Section Ocean Engineering)
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28 pages, 10210 KiB  
Article
A Dynamic Scheduling Method Combining Iterative Optimization and Deep Reinforcement Learning to Solve Sudden Disturbance Events in a Flexible Manufacturing Process
by Jun Yan, Tianzuo Zhao, Tao Zhang, Hongyan Chu, Congbin Yang and Yueze Zhang
Mathematics 2025, 13(1), 4; https://doi.org/10.3390/math13010004 - 24 Dec 2024
Viewed by 1399
Abstract
Unpredictable sudden disturbances such as machine failure, processing time lag, and order changes increase the deviation between actual production and the planned schedule, seriously affecting production efficiency. This phenomenon is particularly severe in flexible manufacturing. In this paper, a dynamic scheduling method combining [...] Read more.
Unpredictable sudden disturbances such as machine failure, processing time lag, and order changes increase the deviation between actual production and the planned schedule, seriously affecting production efficiency. This phenomenon is particularly severe in flexible manufacturing. In this paper, a dynamic scheduling method combining iterative optimization and deep reinforcement learning (DRL) is proposed to address the impact of uncertain disturbances. A real-time DRL production environment model is established for the flexible job scheduling problem. Based on the DRL model, an agent training strategy and an autonomous decision-making method are proposed. An event-driven and period-driven hybrid dynamic rescheduling trigger strategy (HDRS) with four judgment mechanisms has been developed. The decision-making method and rescheduling trigger strategy solve the problem of how and when to reschedule for the dynamic scheduling problem. The data experiment results show that the trained DRL decision-making model can provide timely feedback on the adjusted scheduling arrangements for different-scale order problems. The proposed dynamic-scheduling decision-making method and rescheduling trigger strategy can achieve high responsiveness, quick feedback, high quality, and high stability for flexible manufacturing process scheduling decision making under sudden disturbance. Full article
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26 pages, 13651 KiB  
Article
Dense In Situ Underwater 3D Reconstruction by Aggregation of Successive Partial Local Clouds
by Loïca Avanthey and Laurent Beaudoin
Remote Sens. 2024, 16(24), 4737; https://doi.org/10.3390/rs16244737 - 19 Dec 2024
Cited by 2 | Viewed by 1165
Abstract
Assessing the completeness of an underwater 3D reconstruction on-site is crucial as it allows for rescheduling acquisitions, which capture missing data during a mission, avoiding additional costs of a subsequent mission. This assessment needs to rely on a dense point cloud since a [...] Read more.
Assessing the completeness of an underwater 3D reconstruction on-site is crucial as it allows for rescheduling acquisitions, which capture missing data during a mission, avoiding additional costs of a subsequent mission. This assessment needs to rely on a dense point cloud since a sparse cloud lacks detail and a triangulated model can hide gaps. The challenge is to generate a dense cloud with field-deployable tools. Traditional dense reconstruction methods can take several dozen hours on low-capacity systems like laptops or embedded units. To speed up this process, we propose building the dense cloud incrementally within an SfM framework while incorporating data redundancy management to eliminate recalculations and filtering already-processed data. The method evaluates overlap area limits and computes depths by propagating the matching around SeaPoints—the keypoints we design for identifying reliable areas regardless of the quality of the processed underwater images. This produces local partial dense clouds, which are aggregated into a common frame via the SfM pipeline to produce the global dense cloud. Compared to the production of complete dense local clouds, this approach reduces the computation time by about 70% while maintaining a comparable final density. The underlying prospect of this work is to enable real-time completeness estimation directly on board, allowing for the dynamic re-planning of the acquisition trajectory. Full article
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11 pages, 284 KiB  
Article
Single-Machine Rescheduling with Rejection and an Operator No-Availability Period
by Guanghua Wu and Hongli Zhu
Mathematics 2024, 12(23), 3678; https://doi.org/10.3390/math12233678 - 24 Nov 2024
Viewed by 628
Abstract
In this paper, we investigate a rescheduling problem with rejection and an operator non-availability period on a single machine. An optimal original schedule with the objective of minimizing the total weighted completion time has been made in a deterministic production scheduling system without [...] Read more.
In this paper, we investigate a rescheduling problem with rejection and an operator non-availability period on a single machine. An optimal original schedule with the objective of minimizing the total weighted completion time has been made in a deterministic production scheduling system without an unavailable interval. However, prior to the start of formal job processing, a time interval becomes unavailable due to the operator. No jobs can start or complete in the interval; nonetheless, a job that begins prior to this interval and finishes afterward is possible (if there is such a job, we call it a crossover job). In order to deal with the operator non-availability period, job rejection is allowed. Each job is either accepted for processing or rejected by paying a rejection cost. The planned original schedule is required to be rescheduled. The objective is to minimize the total weighted completion time of the accepted jobs plus the total penalty of the rejected jobs plus the weighted maximum tardiness penalty between the original schedule and the new reschedule. We present a pseudo-polynomial time dynamic programming exact algorithm and subsequently develop it into a fully polynomial time approximation scheme. Full article
22 pages, 780 KiB  
Article
Adaptive Production Rescheduling System for Managing Unforeseen Disruptions
by Andy J. Figueroa, Raul Poler and Beatriz Andres
Mathematics 2024, 12(22), 3478; https://doi.org/10.3390/math12223478 - 7 Nov 2024
Cited by 3 | Viewed by 2022
Abstract
This work presents a mixed-integer linear programming (MILP) model to solve the production rescheduling problem in a job shop manufacturing system impacted by unexpected events, aiming to minimize production costs and disruptions to the initial schedule. The approach begins by generating an optimal [...] Read more.
This work presents a mixed-integer linear programming (MILP) model to solve the production rescheduling problem in a job shop manufacturing system impacted by unexpected events, aiming to minimize production costs and disruptions to the initial schedule. The approach begins by generating an optimal production plan through batch assignments to machines. When unforeseen events, such as machine breakdowns or raw material shortages, occur, a dynamic rescheduling process is triggered, employing an iterative and reactive algorithm to adapt the plan to the real-time conditions on the shop floor. The results demonstrate that this rescheduling method efficiently adjusts to the new conditions while minimizing deviations from the original schedule, achieving solutions within acceptable computational times. Full article
(This article belongs to the Section E: Applied Mathematics)
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22 pages, 10336 KiB  
Article
Construction of a Digital Twin System and Dynamic Scheduling Simulation Analysis of a Flexible Assembly Workshops with Island Layout
by Junli Liu, Deyu Zhang, Zhongpeng Liu, Tianyu Guo and Yanyan Yan
Sustainability 2024, 16(20), 8851; https://doi.org/10.3390/su16208851 - 12 Oct 2024
Cited by 2 | Viewed by 2110
Abstract
Assembly Workshops with Island Layout (AWIL) possess flexible production capabilities that realize product diversification. To cope with the complex scheduling challenges in flexible workshops, improve resource utilization, reduce waste, and enhance production efficiency, this paper proposes a production scheduling method for flexible assembly [...] Read more.
Assembly Workshops with Island Layout (AWIL) possess flexible production capabilities that realize product diversification. To cope with the complex scheduling challenges in flexible workshops, improve resource utilization, reduce waste, and enhance production efficiency, this paper proposes a production scheduling method for flexible assembly workshops with an island layout based on digital twin technology. A digital twin model of the workshop is established according to production demands to simulate scheduling operations and deal with complex scheduling issues. A workshop monitoring system is developed to quickly identify abnormal events. By employing an event-driven rolling-window rescheduling technique, a dynamic scheduling service system is constructed. The rolling window decomposes scheduling problems into consecutive static scheduling intervals based on abnormal events, and a genetic algorithm is used to optimize each interval in real time. This approach provides accurate, real-time scheduling decisions to manage disturbances in workshop production, which can enhance flexibility in the production process, and allows rapid adjustments to production plans. Therefore, the digital twin system improves the sustainability of the production system, which will provide a theoretical research foundation for the real-time and unmanned production scheduling process, thereby achieving sustainable development of production. Full article
(This article belongs to the Section Sustainable Engineering and Science)
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31 pages, 1259 KiB  
Review
Multi-Objective Production Rescheduling: A Systematic Literature Review
by Sofia Holguin Jimenez, Wajdi Trabelsi and Christophe Sauvey
Mathematics 2024, 12(20), 3176; https://doi.org/10.3390/math12203176 - 11 Oct 2024
Cited by 4 | Viewed by 2276
Abstract
Production rescheduling involves re-optimizing production schedules in response to disruptions that render the initial schedule inefficient or unfeasible. This process requires simultaneous consideration of multiple objectives to develop new schedules that are both efficient and stable. However, existing review papers have paid limited [...] Read more.
Production rescheduling involves re-optimizing production schedules in response to disruptions that render the initial schedule inefficient or unfeasible. This process requires simultaneous consideration of multiple objectives to develop new schedules that are both efficient and stable. However, existing review papers have paid limited attention to the multi-objective optimization techniques employed in this context. To address this gap, this paper presents a systematic literature review on multi-objective production rescheduling, examining diverse shop-floor environments. Adhering to the PRISMA guidelines, a total of 291 papers were identified. From this pool, studies meeting the inclusion criteria were selected and analyzed to provide a comprehensive overview of the problems tackled, dynamic events managed, objectives considered, and optimization approaches discussed in the literature. This review highlights the primary multi-objective optimization methods used in relation to rescheduling strategies and the dynamic disruptive events studied. Findings reveal a growing interest in this research area, with “a priori” and “a posteriori” optimization methods being the most commonly implemented and a notable rise in the use of the latter. Hybridized algorithms have shown superior performance compared to standalone algorithms by leveraging combined strengths and mitigating individual weaknesses. Additionally, “interactive” and “Pareto pruning” methods, as well as the consideration of human factors in flexible production systems, remain under-explored. Full article
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22 pages, 6079 KiB  
Article
Simulation Model of a Steelmaking–Continuous Casting Process Based on Dynamic-Operation Rules
by Xin Shao, Qing Liu, Hongzhi Chen, Jiangshan Zhang, Shan Gao and Shaoshuai Li
Materials 2024, 17(17), 4352; https://doi.org/10.3390/ma17174352 - 3 Sep 2024
Cited by 1 | Viewed by 1784
Abstract
The steelmaking–continuous casting process (SCCP) is a complex manufacturing process which exhibits the distinct features of process manufacturing. The SCCP involves a variety of production elements, such as multiple process routes, a wide array of smelting and auxiliary devices, and a variety of [...] Read more.
The steelmaking–continuous casting process (SCCP) is a complex manufacturing process which exhibits the distinct features of process manufacturing. The SCCP involves a variety of production elements, such as multiple process routes, a wide array of smelting and auxiliary devices, and a variety of raw and auxiliary materials. The production-simulation of SCCP holds a natural advantage in being able to accurately depict the intricate production behavior involved, and this serves as a crucial tool for optimizing the production operation of the SCCP. This paper thoroughly considers the various production elements involved in the SCCP, such as the fluctuation of the converter smelting cycle, fluctuation of heat weight, and ladle operation. Based on the Plant Simulation software platform, a dynamic simulation model of the SCCP is established and detailed descriptions are provided regarding the design of an SCCP using dynamic-operation rules. Additionally, a dynamic operational control program for the SCCP is developed using the SimTalk language, one which ensures the continuous operation of the caster in the SCCP, using the discrete simulation platform. The effectiveness of the proposed dynamic simulation model is verified by the total completion time of the production plan, the transfer time of the heat among the different processes, and the frequency of ladle turnover. The simulation’s results indicate that the dynamic simulation model has a satisfactory effect in simulating the actual production process. On this basis, the application effects of different schedules are compared and analyzed. Compared with a heuristic schedule, the optimized schedule based on the “furnace–machine coordinating” mode reduces the weighted value of total completion time by 8.7 min, reduces the weighted value of transfer waiting time by 45.5 min, and the number of rescheduling times is also reduced, demonstrating a better application effect and verifying the optimizing effect of the “furnace–machine coordinating” mode on the schedule. Full article
(This article belongs to the Special Issue Metallurgical Process Simulation and Optimization2nd Volume)
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