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Journal articles on the topic 'Dynamic flexible job shop'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Zhang, Liping, Xinyu Li, Long Wen, and Guohui Zhang. "An Efficient Memetic Algorithm for Dynamic Flexible Job Shop Scheduling with Random Job Arrivals." International Journal of Software Science and Computational Intelligence 5, no. 1 (2013): 63–77. http://dx.doi.org/10.4018/ijssci.2013010105.

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Much of the research on flexible job shop scheduling problem has ignored dynamic events in dynamic environment where there are complex constraints and a variety of unexpected disruptions. This paper proposes an efficient memetic algorithm to solve the flexible job shop scheduling problem with random job arrivals. Firstly, a periodic policy is presented to update the problem condition and generate the rescheduling point. Secondly, the efficient memetic algorithm with a new local search procedure is proposed to optimize the problem at each rescheduling point. Five kinds of neighborhood structure

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2

Pan, Ying, Dong Juan Xue, Tian Yi Gao, Li Bin Zhou, and Xiao Yu Xie. "Modeling and Scheme Generation of Dynamic Flexible Job-Shop Scheduling." Applied Mechanics and Materials 423-426 (September 2013): 2232–36. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2232.

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Aiming at uncertain information and dynamic characteristic during flexible job-shop scheduling process, some kind of dynamic scheduling method for flexible job-shop scheduling problem (FJSP) is put forward based on real-time adjustment. A dynamic simulation solution mode framework is presented for FJSP. This framework is inspired by adaptive control, combined with the robust scheduling and foreseeing scheduling. It has both advantages of such two scheduling methods, and its stable and highly efficient. Preliminary scheme generation method based on foreseeing dynamics scheduling is introduced t

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3

Long, Xiaojun, Jingtao Zhang, Kai Zhou, and Tianguo Jin. "Dynamic Self-Learning Artificial Bee Colony Optimization Algorithm for Flexible Job-Shop Scheduling Problem with Job Insertion." Processes 10, no. 3 (2022): 571. http://dx.doi.org/10.3390/pr10030571.

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To solve the problem of inserting new job into flexible job-shops, this paper proposes a dynamic self-learning artificial bee colony (DSLABC) optimization algorithm to solve dynamic flexible job-shop scheduling problem (DFJSP). Through the reasonable arrangement of the processing sequence of the jobs and the corresponding relationship between the operations and the machines, the makespan can be shortened, the economic benefit of the job-shop and the utilization rate of the processing machine can be improved. Firstly, the Q-learning algorithm and the traditional artificial bee colony (ABC) algo

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Wang, Chun, Jiapeng Chen, Binzi Xu, and Sheng Liu. "A Discrete Improved Gray Wolf Optimization Algorithm for Dynamic Distributed Flexible Job Shop Scheduling Considering Random Job Arrivals and Machine Breakdowns." Processes 13, no. 7 (2025): 1987. https://doi.org/10.3390/pr13071987.

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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 m

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Li, Juan, Xianghong Tian, and Jing Liu. "Dynamic Data Scheduling of a Flexible Industrial Job Shop Based on Digital Twin Technology." Discrete Dynamics in Nature and Society 2022 (August 3, 2022): 1–10. http://dx.doi.org/10.1155/2022/1009507.

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Aiming at the problems of premature convergence of existing workshop dynamic data scheduling methods and the decline in product output, a flexible industrial job shop dynamic data scheduling method based on digital twin technology is proposed. First, digital twin technology is proposed, which provides a design and theoretical basis for the simulation tour of a flexible industrial job shop, building the all-factor digital information fusion model of a flexible industrial workshop to comprehensively control the all-factor digital information of the workshops. A CGA algorithm is proposed by intro

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Wang, Yajun, Junyu Leng, Xiaoqi Liu, Jiajia Wang, and Qiunan Meng. "A Method for Dynamic Insertion Order Scheduling in Flexible Job Shops Based on Digital Twins." Applied Sciences 12, no. 23 (2022): 12430. http://dx.doi.org/10.3390/app122312430.

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Various production disturbances occurring in the flexible job shop production process may affect the production of the workshop, some of which may lead to the prolongation of production completion time. Therefore, a flexible job shop dynamic scheduling method based on digital twins is proposed and a dynamic scheduling framework is constructed. Compared with the traditional workshop, the digital twin-based flexible job shop can upload the relevant production data of the physical workshop to the data management center in real time, and after fusion processing the data can work cooperatively with

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Wang, Yanjiao, and Jieru Han. "A FJSSP Method Based on Dynamic Multi-Objective Squirrel Search Algorithm." International Journal of Antennas and Propagation 2021 (October 15, 2021): 1–19. http://dx.doi.org/10.1155/2021/6062689.

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This paper comprehensively analyzes the characteristics of flexible job shop scheduling problem (FJSSP), takes the dynamic factors in the actual scheduling process such as the arrival and departure of jobs, the breakdown and recovery of machines into account at the same time, and establishes a new dynamic multi-objective mathematical model. Take the Squirrel Search Algorithm (SSA) as the core evolution strategy, and combine the multi-objective framework and the dynamic processing technology to solve the established mathematical model. Experimental results show that the mathematical model propo

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8

M., Sanatizadeh M.A. Badamchizadeh S. Khanmohammadi D. Ashtiani Haghighi. "A New Method for Solving Dynamic Flexible Job Shop Scheduling Problems Integrating Genetic Algorithm and Priority Rules." International Journal of Engineering Works 1, no. 2 (2014): 26–31. https://doi.org/10.5281/zenodo.15746.

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Dynamic flexible job shop scheduling problems has been one of the important and strongly NP-hard problem of manufacturing systems for many years. Most of the proposed algorithms are based on priority rules; By using these rules, the arrived jobs go to a long queue of waited jobs and sometimes it takes a long time for a job to be processed. In this paper a new approach, integrating of priority rules and genetic algorithm is presented, by decomposition of a dynamic problem to smaller dynamic and static problems. A module converts the queue of dynamic jobs to static, and then a genetic algorithm

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9

Pan, Ying, Dong Juan Xue, Tian Yi Gao, Li Bin Zhou, and Xiao Yu Xie. "Research on Strategy of Dynamic Flexible Job-Shop Scheduling." Applied Mechanics and Materials 423-426 (September 2013): 2237–43. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2237.

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A dynamic scheduling method for FJSP is put forward based on real-time adjustment of controllable margin, aiming at uncertain information and dynamic characteristic during practical job-shop scheduling process. Strategy and methods of dynamic scheduling adjustment are presented, which respond to real-time event. In view of practical operation condition and accuracy demands, two methods are proposed. One is rescheduling that condenses domain, the other is scheduling updating based on adjustment. Such strategy obtains dynamic robust scheduling of FJSP to ensure the real-time of algorithm, contin

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10

Xu, Xiang Min, and Xi Fan Yao. "Application of Ontology in Event-Driven Job-Shop Scheduling Problems." Applied Mechanics and Materials 775 (July 2015): 458–63. http://dx.doi.org/10.4028/www.scientific.net/amm.775.458.

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Aiming at the flexible flow-shop scheduling problem of cloud manufacturing, this paper introduces event driven concept and apply ontologies to Job-Shop scheduling problem FT46. The inference of ontology models allows the system to gain the dynamic information of workshop, and then rule engine is used to match event patterns to optimize the job shop scheduling problem.

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11

Chang, Jingru, Dong Yu, Yi Hu, Wuwei He, and Haoyu Yu. "Deep Reinforcement Learning for Dynamic Flexible Job Shop Scheduling with Random Job Arrival." Processes 10, no. 4 (2022): 760. http://dx.doi.org/10.3390/pr10040760.

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The production process of a smart factory is complex and dynamic. As the core of manufacturing management, the research into the flexible job shop scheduling problem (FJSP) focuses on optimizing scheduling decisions in real time, according to the changes in the production environment. In this paper, deep reinforcement learning (DRL) is proposed to solve the dynamic FJSP (DFJSP) with random job arrival, with the goal of minimizing penalties for earliness and tardiness. A double deep Q-networks (DDQN) architecture is proposed and state features, actions and rewards are designed. A soft ε-greedy

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12

Yu, Jian Jun, Shu Dong Sun, Shu Bin Si, Hong An Yang, and Xiu Li Wu. "A Study on the Aviation Manufacture Cell Scheduling Based on Adaptive Ant Colony Algorithm." Materials Science Forum 532-533 (December 2006): 1060–63. http://dx.doi.org/10.4028/www.scientific.net/msf.532-533.1060.

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Aviation manufacture cell scheduling is generally multi-constraint and multi-objective flexible job-shop scheduling, and is more complicated than classical job-shop because of flexible processing route, multi-restriction, multi-objective, dynamic disturb and complex model. The model of aviation manufacture cell scheduling is set up, and accordingly adaptive ant colony algorithm is brought forward. The adaptive mechanism and the genetic principle are introduced into the algorithm to accelerate convergence and avoid stagnation. Finally, the aviation manufacture cell scheduling of Xi’an Aero-Engi

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13

Gayathri Devi, K., R. S. Mishra, and A. K. Madan. "A Dynamic Adaptive Firefly Algorithm for Flexible Job Shop Scheduling." Intelligent Automation & Soft Computing 31, no. 1 (2022): 429–48. http://dx.doi.org/10.32604/iasc.2022.019330.

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14

Zhong, Hongyang, Jianjun Liu, Qingxin Chen, Ning Mao, and Xiaojia Yang. "Performance Assessment of Dynamic Flexible Assembly Job Shop Control Methods." IEEE Access 8 (2020): 226042–58. http://dx.doi.org/10.1109/access.2020.3043880.

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15

Baykasoğlu, Adil, Fatma S. Madenoğlu, and Alper Hamzadayı. "Greedy randomized adaptive search for dynamic flexible job-shop scheduling." Journal of Manufacturing Systems 56 (July 2020): 425–51. http://dx.doi.org/10.1016/j.jmsy.2020.06.005.

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16

Liu, Lilan, Kai Guo, Zenggui Gao, Jiaying Li, and Jiachen Sun. "Digital Twin-Driven Adaptive Scheduling for Flexible Job Shops." Sustainability 14, no. 9 (2022): 5340. http://dx.doi.org/10.3390/su14095340.

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The traditional shop floor scheduling problem mainly focuses on the static environment, which is unrealistic in actual production. To solve this problem, this paper proposes a digital twin-driven shop floor adaptive scheduling method. Firstly, a digital twin model of the actual production line is established to monitor the operation of the actual production line in real time and provide a real-time data source for subsequent scheduling; secondly, to address the problem that the solution quality and efficiency of the traditional genetic algorithm cannot meet the actual production demand, the ke

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17

Chen, Yi, Xiaojuan Liao, Guangzhu Chen, and Yingjie Hou. "Dynamic Intelligent Scheduling in Low-Carbon Heterogeneous Distributed Flexible Job Shops with Job Insertions and Transfers." Sensors 24, no. 7 (2024): 2251. http://dx.doi.org/10.3390/s24072251.

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With the rapid development of economic globalization and green manufacturing, traditional flexible job shop scheduling has evolved into the low-carbon heterogeneous distributed flexible job shop scheduling problem (LHDFJSP). Additionally, modern smart manufacturing processes encounter complex and diverse contingencies, necessitating the ability to address dynamic events in real-world production activities. To date, there are limited studies that comprehensively address the intricate factors associated with the LHDFJSP, including workshop heterogeneity, job insertions and transfers, and conside

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18

He, Yingmei, Bin Xin, Sai Lu, Qing Wang, and Yulong Ding. "Memetic Algorithm for Dynamic Joint Flexible Job Shop Scheduling with Machines and Transportation Robots." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 6 (2022): 974–82. http://dx.doi.org/10.20965/jaciii.2022.p0974.

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In this study, the dynamic joint scheduling problem for processing machines and transportation robots in a flexible job shop is investigated. The study aims to minimize the order completion time (makespan) of a job shop manufacturing system. Considering breakdowns, order insertion and battery charging maintenance of robots, an event-driven global rescheduling strategy is adopted. A novel memetic algorithm combining genetic algorithm and variable neighborhood search is designed to handle dynamic events and obtain a new scheduling plan. Finally, numerical experiments are conducted to test the ef

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19

Ding, Linshan, Zailin Guan, Dan Luo, Mudassar Rauf, and Weikang Fang. "An Adaptive Search Algorithm for Multiplicity Dynamic Flexible Job Shop Scheduling with New Order Arrivals." Symmetry 16, no. 6 (2024): 641. http://dx.doi.org/10.3390/sym16060641.

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In today’s customer-centric economy, the demand for personalized products has compelled corporations to develop manufacturing processes that are more flexible, efficient, and cost-effective. Flexible job shops offer organizations the agility and cost-efficiency that traditional manufacturing processes lack. However, the dynamics of modern manufacturing, including machine breakdown and new order arrivals, introduce unpredictability and complexity. This study investigates the multiplicity dynamic flexible job shop scheduling problem (MDFJSP) with new order arrivals. To address this problem, we i

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20

Olanrewaju, Oludolapo Akanni, Fabio Luiz Peres Krykhtine, and Felix Mora-Camino. "Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic." Algorithms 17, no. 11 (2024): 520. http://dx.doi.org/10.3390/a17110520.

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This study considers a flexible job-shop scheduling problem where energy cost savings are the primary objective and where the classical objective of the minimization of the make-span is replaced by the satisfaction of due times for each job. An original two-level mixed-integer formulation of this optimization problem is proposed, where the processed flows of material and their timing are explicitly considered. Its exact solution is discussed, and, considering its computational complexity, a comprehensive heuristic, balancing energy performance and due time constraint satisfaction, is developed

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21

Naimi, Rami, Maroua Nouiri, and Olivier Cardin. "A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives." Sustainability 13, no. 23 (2021): 13016. http://dx.doi.org/10.3390/su132313016.

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The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling

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22

Jimenez, Jose-Fernando, Abdelghani Bekrar, Gabriel Zambrano-Rey, Damien Trentesaux, and Paulo Leitão. "Pollux: a dynamic hybrid control architecture for flexible job shop systems." International Journal of Production Research 55, no. 15 (2016): 4229–47. http://dx.doi.org/10.1080/00207543.2016.1218087.

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23

Ning, Tao, Ming Huang, Xu Liang, and Hua Jin. "A novel dynamic scheduling strategy for solving flexible job-shop problems." Journal of Ambient Intelligence and Humanized Computing 7, no. 5 (2016): 721–29. http://dx.doi.org/10.1007/s12652-016-0370-7.

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24

Xu, Binzi, Xinyu Cao, Shuwen Zhang, et al. "Terminal normalization in genetic programming for dynamic flexible job shop scheduling." Swarm and Evolutionary Computation 96 (July 2025): 101970. https://doi.org/10.1016/j.swevo.2025.101970.

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25

Feng, Yi, Mengru Liu, Yuqian Zhang, and Jinglin Wang. "A Dynamic Opposite Learning Assisted Grasshopper Optimization Algorithm for the Flexible JobScheduling Problem." Complexity 2020 (December 30, 2020): 1–19. http://dx.doi.org/10.1155/2020/8870783.

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Job shop scheduling problem (JSP) is one of the most difficult optimization problems in manufacturing industry, and flexible job shop scheduling problem (FJSP) is an extension of the classical JSP, which further challenges the algorithm performance. In FJSP, a machine should be selected for each process from a given set, which introduces another decision element within the job path, making FJSP be more difficult than traditional JSP. In this paper, a variant of grasshopper optimization algorithm (GOA) named dynamic opposite learning assisted GOA (DOLGOA) is proposed to solve FJSP. The recently

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26

Sangaiah, Arun, Mohsen Suraki, Mehdi Sadeghilalimi, Seyed Bozorgi, Ali Hosseinabadi, and Jin Wang. "A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines." Symmetry 11, no. 2 (2019): 165. http://dx.doi.org/10.3390/sym11020165.

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In a real manufacturing environment, the set of tasks that should be scheduled is changing over the time, which means that scheduling problems are dynamic. Also, in order to adapt the manufacturing systems with fluctuations, such as machine failure and create bottleneck machines, various flexibilities are considered in this system. For the first time, in this research, we consider the operational flexibility and flexibility due to Parallel Machines (PM) with non-uniform speed in Dynamic Job Shop (DJS) and in the field of Flexible Dynamic Job-Shop with Parallel Machines (FDJSPM) model. After mo

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27

Luo, Shu. "Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning." Applied Soft Computing 91 (June 2020): 106208. http://dx.doi.org/10.1016/j.asoc.2020.106208.

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28

Gao, Li, Ke Lin Xu, Wei Zhu, and Na Na Yang. "A Two-Stage Hybrid Algorithm for Flexible Job-Shop Scheduling." Advanced Materials Research 268-270 (July 2011): 476–81. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.476.

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A mathematical model was constructed with two objectives. A two-stage hybrid algorithm was developed for solving this problem. At first, the man-hour optimization based on genetic algorithm and dynamic programming method, the model decomposes the flow shop into two layers: sub-layer and patrilineal layer. On the basis of the man-hour optimization，A simulated annealing genetic algorithm was proposed to optimize the sequence of operations. A new selection procedure was proposed and hybrid crossover operators and mutation operators were adopted. A benchmark problem solving result indicates that t

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29

Yin, Yue, Xiao Kong, Changqing Xia, Chi Xu, and Xi Jin. "Low-Cost Emergent Dynamic Scheduling for Flexible Job Shops." Mathematics 10, no. 11 (2022): 1873. http://dx.doi.org/10.3390/math10111873.

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Flexible production is a typical representative of high-end manufacturing and is also a manifestation of a country’s national production capability. Compared with other production modes, the key distinction of flexible production is that all four dimensions of production (i.e., machines, operations, products, and orders) can be scheduled dynamically. Although many studies have investigated the flexible job shop scheduling problem, most have limited dynamic support and cannot deal with multidimensional dynamic production. This study, therefore, proposed a fine-grained system state description m

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30

Zhang, Fangfang, Yi Mei, Su Nguyen, Mengjie Zhang, and Kay Chen Tan. "Surrogate-Assisted Evolutionary Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling." IEEE Transactions on Evolutionary Computation 25, no. 4 (2021): 651–65. http://dx.doi.org/10.1109/tevc.2021.3065707.

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31

Burggräf, Peter, Johannes Wagner, Till Saßmannshausen, Dennis Ohrndorf, and Karthik Subramani. "Multi-agent-based deep reinforcement learning for dynamic flexible job shop scheduling." Procedia CIRP 112 (2022): 57–62. http://dx.doi.org/10.1016/j.procir.2022.09.024.

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32

Luo, Shu, Linxuan Zhang, and Yushun Fan. "Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning." Computers & Industrial Engineering 159 (September 2021): 107489. http://dx.doi.org/10.1016/j.cie.2021.107489.

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33

Yuan, Erdong, Liejun Wang, Shiji Song, Shuli Cheng, and Wei Fan. "Dynamic scheduling for multi-objective flexible job shop via deep reinforcement learning." Applied Soft Computing 171 (March 2025): 112787. https://doi.org/10.1016/j.asoc.2025.112787.

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34

Gholami, M., and M. Zandieh. "Integrating simulation and genetic algorithm to schedule a dynamic flexible job shop." Journal of Intelligent Manufacturing 20, no. 4 (2008): 481–98. http://dx.doi.org/10.1007/s10845-008-0150-0.

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35

Nouiri, M., A. Bekrar, and D. Trentesaux. "Towards Energy Efficient Scheduling and Rescheduling for Dynamic Flexible Job Shop Problem." IFAC-PapersOnLine 51, no. 11 (2018): 1275–80. http://dx.doi.org/10.1016/j.ifacol.2018.08.357.

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36

Tarigan, Masmur, Ford Lumban Gaol, Tuga Mauritsius, and Widodo Budiharto. "Flexible Job Shop Scheduling Optimization Using Genetic Algorithm For Handling Dynamic Factors." Journal of Applied Engineering and Technological Science (JAETS) 6, no. 2 (2025): 1280–96. https://doi.org/10.37385/jaets.v6i2.5784.

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This research introduces the Genetic Adaptive Scheduling System (GASS), a novel framework designed to optimize scheduling in Flexible Job Shop Scheduling Problems (FJSP). Due to its complexity, FJSP presents significant challenges stemming from machine flexibility, dynamic routing, and operation precedence constraints. GASS addresses these challenges by incorporating real-time, dynamic data, enabling the system to adapt to machine downtimes, fluctuating job priorities, and process variability. Leveraging advanced genetic algorithm techniques, GASS integrates enhanced mutation and selection pro

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Shi, Yuan, Yaoming Yang, Bingdong Li, Hong Qian, Hao Hao, and Aimin Zhou. "Diversity-enhanced hyper-heuristics for multi-objective dynamic flexible job shop scheduling." Swarm and Evolutionary Computation 96 (July 2025): 101994. https://doi.org/10.1016/j.swevo.2025.101994.

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Wang, Lei, Chaomin Luo, and Jingcao Cai. "A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm." Journal of Advanced Transportation 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/1527858.

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In real-world manufacturing systems, production scheduling systems are often implemented under random or dynamic events like machine failure, unexpected processing times, stochastic arrival of the urgent orders, cancellation of the orders, and so on. These dynamic events will lead the initial scheduling scheme to be nonoptimal and/or infeasible. Hence, appropriate dynamic rescheduling approaches are needed to overcome the dynamic events. In this paper, we propose a dynamic rescheduling method based on variable interval rescheduling strategy (VIRS) to deal with the dynamic flexible job shop sch

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Sharma, Ashish, and Anjani Rai. "An Energy Efficient Multi-Objective Optimization based Dynamic Scheduling for Flexible Job Shop Scheduling Problem with Transportation Constraints." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 1437–46. http://dx.doi.org/10.5373/jardcs/v11sp10/20192989.

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40

Ojstersek, R., M. Tang, and B. Buchmeister. "Due date optimization in multi-objective scheduling of flexible job shop production." Advances in Production Engineering & Management 15, no. 4 (2020): 481–92. http://dx.doi.org/10.14743/apem2020.4.380.

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The manuscript presents the importance of integrating mathematical methods for the determination of due date optimization parameter for maturity optimization in evolutionary computation (EC) methods in multi-objective flexible job shop scheduling problem (FJSSP). The use of mathematical modelling methods of due date optimization with slack (SLK) for low and total work content (TWK) for medium and high dimensional problems was presented with the integration into the multi-objective heuristic Kalman algorithm (MOHKA). The multi-objective optimization results of makespan, machine utilization and

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Fuladi, Shubhendu Kshitij, and Chang-Soo Kim. "Dynamic Events in the Flexible Job-Shop Scheduling Problem: Rescheduling with a Hybrid Metaheuristic Algorithm." Algorithms 17, no. 4 (2024): 142. http://dx.doi.org/10.3390/a17040142.

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In the real world of manufacturing systems, production planning is crucial for organizing and optimizing various manufacturing process components. The objective of this paper is to present a methodology for both static scheduling and dynamic scheduling. In the proposed method, a hybrid algorithm is utilized to optimize the static flexible job-shop scheduling problem (FJSP) and dynamic flexible job-shop scheduling problem (DFJSP). This algorithm integrates the genetic algorithm (GA) as a global optimization technique with a simulated annealing (SA) algorithm serving as a local search optimizati

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Song, Liyuan, Yuanyuan Li, and Jiacheng Xu. "Dynamic Job-Shop Scheduling Based on Transformer and Deep Reinforcement Learning." Processes 11, no. 12 (2023): 3434. http://dx.doi.org/10.3390/pr11123434.

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The dynamic job-shop scheduling problem is a complex and uncertain task that involves optimizing production planning and resource allocation in a dynamic production environment. Traditional methods are limited in effectively handling dynamic events and quickly generating scheduling solutions; in order to solve this problem, this paper proposes a solution by transforming the dynamic job-shop scheduling problem into a Markov decision process and leveraging deep reinforcement learning techniques. The proposed framework introduces several innovative components, which make full use of human domain

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Xu, Hua, Jianlu Zheng, Lingxiang Huang, Juntai Tao, and Chenjie Zhang. "Solving Dynamic Multi-Objective Flexible Job Shop Scheduling Problems Using a Dual-Level Integrated Deep Q-Network Approach." Processes 13, no. 2 (2025): 386. https://doi.org/10.3390/pr13020386.

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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

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Sharma, Pankaj, and Ajai Jain. "Effect of routing flexibility and sequencing rules on performance of stochastic flexible job shop manufacturing system with setup times: Simulation approach." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231, no. 2 (2016): 329–45. http://dx.doi.org/10.1177/0954405415576060.

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Routing flexibility is a major contributor towards flexibility of a flexible job shop manufacturing system. This article focuses on a simulation-based experimental study on the effect of routing flexibility and sequencing rules on the performance of a stochastic flexible job shop manufacturing system with sequence-dependent setup times while considering dynamic arrival of job types. Six route flexibility levels and six sequencing rules are considered for detailed study. The performance of manufacturing system is evaluated in terms of flow time related and due date–related measures. Results rev

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45

Albayrak, Emrah, and Semih Önüt. "Energy-efficient scheduling for a flexible job shop problem considering rework processes and new job arrival." International Journal of Industrial Engineering Computations 15, no. 4 (2024): 871–86. http://dx.doi.org/10.5267/j.ijiec.2024.7.004.

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Sustainable production is not limited to environmental concerns only; It also provides economic benefits for businesses. Businesses that adopt sustainability principles can gain advantages in matters such as cost savings, competitive advantage, risk management, legal compliance and corporate reputation. Therefore, sustainability is no longer an option but a strategic imperative for businesses. For this reason, studies on energy-sensitive scheduling have started to increase recently. Another important factor in sustainable manufacturing is the reduction of scrap. Rework operations are required

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Chen, Chao, Zhicheng Ji, and Yan Wang. "NSGA-II applied to dynamic flexible job shop scheduling problems with machine breakdown." Modern Physics Letters B 32, no. 34n36 (2018): 1840111. http://dx.doi.org/10.1142/s0217984918401115.

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This paper focuses on multi-objective dynamic flexible job shop scheduling problem (MODFJSP) with machine breakdown. First, a multi-objective dynamic scheduling model is established, with objectives to minimize makespan and total machine workload. Second, according to the processing status of faulty machine, a hybrid rescheduling strategy including transfer rescheduling strategy and complete rescheduling strategy is proposed to react to stochastic machine breakdown. The performance of two rescheduling strategies is analyzed in terms of the scheduling efficiency and its stability, from the dela

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Zhang, Sicheng, and Tak Nam Wong. "Flexible job-shop scheduling/rescheduling in dynamic environment: a hybrid MAS/ACO approach." International Journal of Production Research 55, no. 11 (2016): 3173–96. http://dx.doi.org/10.1080/00207543.2016.1267414.

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Zhang, Lixiang, Yan Yan, Chen Yang, and Yaoguang Hu. "Dynamic flexible job-shop scheduling by multi-agent reinforcement learning with reward-shaping." Advanced Engineering Informatics 62 (October 2024): 102872. http://dx.doi.org/10.1016/j.aei.2024.102872.

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Wu, Xiuli, Jing Li, Xianli Shen, and Ning Zhao. "NSGA‐III for solving dynamic flexible job shop scheduling problem considering deterioration effect." IET Collaborative Intelligent Manufacturing 2, no. 1 (2020): 22–33. http://dx.doi.org/10.1049/iet-cim.2019.0056.

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Fattahi, Parviz, and Alireza Fallahi. "Dynamic scheduling in flexible job shop systems by considering simultaneously efficiency and stability." CIRP Journal of Manufacturing Science and Technology 2, no. 2 (2010): 114–23. http://dx.doi.org/10.1016/j.cirpj.2009.10.001.

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