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Journal articles on the topic 'Flexible job-shop scheduling'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Liu, Manxing, and Xiufeng Xia. "Research on Priority-based Flexible Job Shop Scheduling Algorithm." E3S Web of Conferences 251 (2021): 03086. http://dx.doi.org/10.1051/e3sconf/202125103086.

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Research on the Flexible Job Shop Scheduling (FJSP) problem in the manufacturing process of aircraft engines has been carried out. It is found that there are real requirements such as order mandatory nodes and equipment selection in the current job scheduling process. Order mandatory node means that some important orders must be delivered on time at a certain point in time, which often affects the quality of workshop scheduling results. In addition, equipment selection is also an important factor affecting the quality of job shop scheduling results. On this basis, an order priority calculation

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Prathipati, Nikhil, K.N.Phanindra, Chalapathi P.Venkata, Ikram Mohammed, and B.Akhil. "Flexible Job Shop Scheduling using Software." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 67–70. https://doi.org/10.35940/ijeat.B3430.029320.

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This present paper speaks about Flexible Job Shop scheduling Problem using a software. Flexible Job shop scheduling is most typical and complex manufacturing environments in production planning issues. A case study was conducted on a manufacturing business set in Vijayawada. Information collected from the producing company to get the time taken by the conventional scheduling method and gathered data for 5 jobs requiring 8 machines that arrived during the analysis period. The Project additionally speaks about the study of varied programming strategies, learning of LEKIN programming package is a

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Hong, Tzung-Pei, and Wei-Chou Chen. "Fuzzy Flexible-Flow Shops at Two Machine Centers." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 4 (1998): 142–49. http://dx.doi.org/10.20965/jaciii.1998.p0142.

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In simple-flow shop problems, each machine operation center has one machine. If a machine center has more than one machine, scheduling becomes a flexible shop flow problem. Flexible shop flows are thus generalizations of simple shop flow. Processing time for each job was previously assumed to be known exactly, but in many real-world applications, processing times vary dynamically due to human or operating factors. We apply fuzzy concepts to represent uncertainty in processing times for flexible-flow shops with two machine centers, then propose a fuzzy heuristic algorithm for job scheduling. We

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Hansmann, R. S., T. Rieger, and U. T. Zimmermann. "Flexible job shop scheduling with blockages." Mathematical Methods of Operations Research 79, no. 2 (2013): 135–61. http://dx.doi.org/10.1007/s00186-013-0456-3.

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Xie, Jin, Liang Gao, Kunkun Peng, Xinyu Li, and Haoran Li. "Review on flexible job shop scheduling." IET Collaborative Intelligent Manufacturing 1, no. 3 (2019): 67–77. http://dx.doi.org/10.1049/iet-cim.2018.0009.

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Kavitha, S., and P. Venkumar. "A vibrant crossbreed social spider optimization with genetic algorithm tactic for flexible job shop scheduling problem." Measurement and Control 53, no. 1-2 (2020): 93–103. http://dx.doi.org/10.1177/0020294019889085.

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Job shop scheduling is one of the major issues in which the scheduling process is associated with the real-time manufacturing industry. A flexible job shop scheduling problem is one of the most important issues among the hardest combinatorial advancement issues. Flexible job shop scheduling is extremely a nondeterministic polynomial combinatorial problem. In this paper, it is proposed that a mixture of improvement demonstrates to make makespan minimization in the flexible job shop scheduling problem issue. This paper includes the hybridization of social spider optimization and genetic algorith

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Wu, Mingliang, Dongsheng Yang, Bowen Zhou, et al. "Adaptive Population NSGA-III with Dual Control Strategy for Flexible Job Shop Scheduling Problem with the Consideration of Energy Consumption and Weight." Machines 9, no. 12 (2021): 344. http://dx.doi.org/10.3390/machines9120344.

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The flexible job shop scheduling problem has always been the focus of research in the manufacturing field. However, most of the previous studies focused more on efficiency and ignored energy consumption. Energy, especially non-renewable energy, is an essential factor affecting the sustainable development of a country. To this end, this paper designs a flexible job shop scheduling problem model with energy consideration more in line with the production field. Except for the processing stage, the energy consumption of the transport, set up, unload, and idle stage are also included in our model.

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R Tampubolon, Ferdinan, Sinta Siagian, Samaria Chrisna HS, and Rischa Devita. "Pengembangan Algoritma Tabu Search Berbasis Bilangan Grey Untuk Optimalisasi Flexible Job Shop Dengan Ketidakpastian." Jurnal Syntax Fusion 3, no. 10 (2023): 1084–90. http://dx.doi.org/10.54543/fusion.v3i10.370.

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This research aims to make a step-by-step process to determine the most optimal completion time for the Flexible Job Shop Scheduling case. Flexible Job Shop Scheduling which not only aims to sequence operations on jobs but also chooses the machine to be used in processing an operation makes this scheduling more complicated than ordinary Job Shop Scheduling. This scheduling involves interval-shaped gray numbers to accommodate uncertainty in job ordering time. This further extends the complexity of scheduling. To solve this case a metaheuristic algorithm namely Tabu Search will be used. This alg

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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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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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LIU, S. Q., and H. L. ONG. "METAHEURISTICS FOR THE MIXED SHOP SCHEDULING PROBLEM." Asia-Pacific Journal of Operational Research 21, no. 01 (2004): 97–115. http://dx.doi.org/10.1142/s0217595904000072.

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In this paper, three metaheuristics are proposed for solving a class of job shop, open shop, and mixed shop scheduling problems. We evaluate the performance of the proposed algorithms by means of a set of Lawrence's benchmark instances for the job shop problem, a set of randomly generated instances for the open shop problem, and a combined job shop and open shop test data for the mixed shop problem. The computational results show that the proposed algorithms perform extremely well on all these three types of shop scheduling problems. The results also reveal that the mixed shop problem is relat

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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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Marrwa Abd-AlKareem Alabajee, Anfal A. Fadhil, and Rasha Gh. Alsarraj. "job shop scheduling problem:literature review." Tikrit Journal of Pure Science 25, no. 4 (2020): 91–100. http://dx.doi.org/10.25130/tjps.v25i4.277.

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As an extension of the Job Shop Scheduling (JSP), Flexible job-shop Scheduling Problem (FJSP), can be defined as one of the most significant problems in up-to-date manufacturing systems, recently a lot of studies have been conducted to address FJSP. Initially, the problem can be defined, after that, literature can be categorized based on different methods that have used from the year 2010 for the resolution of this problem. Lastly, certain conclusions have been provided based on the results of the conducted survey.

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Huang, Xiabao, Zailin Guan, and Lixi Yang. "An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem." Advances in Mechanical Engineering 10, no. 9 (2018): 168781401880144. http://dx.doi.org/10.1177/1687814018801442.

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Genetic algorithm is one of primary algorithms extensively used to address the multi-objective flexible job-shop scheduling problem. However, genetic algorithm converges at a relatively slow speed. By hybridizing genetic algorithm with particle swarm optimization, this article proposes a teaching-and-learning-based hybrid genetic-particle swarm optimization algorithm to address multi-objective flexible job-shop scheduling problem. The proposed algorithm comprises three modules: genetic algorithm, bi-memory learning, and particle swarm optimization. A learning mechanism is incorporated into gen

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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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Wang, Ji, Yujun Liu, Zhuoshang Ji, Yanping Deng, and Yuanyuan Zheng. "Study of Flexible Scheduling System of the Hull Job Shop Based on OOCPN." Journal of Ship Production 25, no. 03 (2009): 168–73. http://dx.doi.org/10.5957/jsp.2009.25.3.168.

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The hull job shop in a shipyard is a typical flexible manufacturing system (FMS), the flexibility and efficiency of which largely depends on the level of FMS scheduling. In this paper, the Object-Oriented Colored Petri Net (OOCPN) is used to build the FMS model for the hull job shop. A four-step modeling method of FMS has been developed to successfully simulate the scheduling of the hull job shop.

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Boyer, Vincent, Jobish Vallikavungal, Xavier Cantú Rodríguez, and M. Angélica Salazar-Aguilar. "The generalized flexible job shop scheduling problem." Computers & Industrial Engineering 160 (October 2021): 107542. http://dx.doi.org/10.1016/j.cie.2021.107542.

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Gomes *, M. C., A. P. Barbosa-Póvoa, and A. Q. Novais. "Optimal scheduling for flexible job shop operation." International Journal of Production Research 43, no. 11 (2005): 2323–53. http://dx.doi.org/10.1080/00207540412331330101.

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19

Li, Xiangnan, and Xu Liang. "Research on Flexible Job Shop Scheduling Problem." Journal of Physics: Conference Series 1549 (June 2020): 032126. http://dx.doi.org/10.1088/1742-6596/1549/3/032126.

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Gutierrez, Celia. "Overlap Algorithms in Flexible Job-shop Scheduling." International Journal of Interactive Multimedia and Artificial Intelligence 2, no. 6 (2014): 41. http://dx.doi.org/10.9781/ijimai.2014.265.

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Bharti, Puja, and Sushma Jain. "Hybrid frameworks for flexible job shop scheduling." International Journal of Advanced Manufacturing Technology 108, no. 5-6 (2020): 1563–85. http://dx.doi.org/10.1007/s00170-020-05398-4.

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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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Zhang, Lei. "Multi-objective flexible job-shop scheduling problem researching based on improved adaptive NSGA-II." Journal of Physics: Conference Series 2235, no. 1 (2022): 012093. http://dx.doi.org/10.1088/1742-6596/2235/1/012093.

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Abstract To solve the scheduling problem of products manufacturing in the military electronic equipment enterprises, a multi-objective flexible job-shop scheduling model was established with the objective of minimum the make span, production costs and equipment load. According to the characteristics of flexible job-shop scheduling problem, an adaptive NSGA-II algorithm was proposed in this paper, which could change the probabilities of crossover and mutation operation at different stages of genetic processes. To improve the diversity of population, the algorithm performed independent crossover

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Zhang, Zhongwei, Lihui Wu, Tao Peng, and Shun Jia. "An Improved Scheduling Approach for Minimizing Total Energy Consumption and Makespan in a Flexible Job Shop Environment." Sustainability 11, no. 1 (2018): 179. http://dx.doi.org/10.3390/su11010179.

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Nowadays, manufacturing industry is under increasing pressure to save energy and reduce emissions, and thereby enhancing the energy efficiency of the machining system (MS) through operational methods on the system-level has attracted more attention. Energy-efficient scheduling (ES) has proved to be a typical measure suitable for all shop types, and an energy-efficient mechanism that a machine can be switched off and back on if it waits for a new job for a relatively long period is another proven effective energy-saving measure. Furthermore, their combination has been fully investigated in a si

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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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Ali, Zainab, and Luma S. Hassan. "Evaluate Flexible Job Scheduling Problem Using Elitist Ant." BIO Web of Conferences 97 (2024): 00060. http://dx.doi.org/10.1051/bioconf/20249700060.

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One significant approach that is frequently utilized in many applications, particularly in industry, is scheduling .An extension of job scheduling, the Flexible Job Shop Scheduling Problem (FJSP), has a number of real-world uses .This subject has received a lot of attention due to its significance and complexity .This research discusses the importance of scheduling in real-life operations, particularly in production scheduling. It presents a model using Elitist Ant (EAS) to compute the maximum value (makspane) of the flexible job shop scheduling problem(FJSP). The model programmed by Python ve

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HUANG, HEJIAO, and TAIPING LU. "SOLVING A MULTI-OBJECTIVE FLEXIBLE JOB SHOP SCHEDULING PROBLEM WITH TIMED PETRI NETS AND GENETIC ALGORITHM." Discrete Mathematics, Algorithms and Applications 02, no. 02 (2010): 221–37. http://dx.doi.org/10.1142/s1793830910000607.

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The method presented in this paper is used to solve flexible job shop scheduling problem (JSP) with multiple objectives, which is much more complex than the classical JSP. Based on timed Petri net model, genetic algorithm is applied to solve the scheduling problems. The chromosomes are composed by sequences of transitions, the crossover and mutation operations are based on transition sequences. The experiment result shows that a definite solution to a specific flexible job shop scheduling problem can be found.

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JANG, Yang-Ja, Ki-Dong KIM, Seong-Yong JANG, and Jinwoo PARK. "Flexible Job Shop Scheduling with Multi-level Job Structures." JSME International Journal Series C 46, no. 1 (2003): 33–38. http://dx.doi.org/10.1299/jsmec.46.33.

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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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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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Liu, Xiao Xia, Chun Bo Liu, and Ze Tao. "Study on Scheduling Optimization for Flexible Job Shop." Applied Mechanics and Materials 26-28 (June 2010): 821–25. http://dx.doi.org/10.4028/www.scientific.net/amm.26-28.821.

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A hybrid genetic algorithm based on Pareto was proposed and applied to flexible job shop scheduling problem (FJSP) with multi-objective, and the multi-objective FJSP optimization model was built, where the make-span and the machine utilization rate were concerned. The algorithm embeds Pareto ranking strategy into Pareto competition method. The operation-based encoding and an active scheduling decoding method are employed. In order to promote solution diversity, the niche technology and many kinds of crossover operations are used. Pareto filter saves the optimum individual occurring in the cour

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Li, Mengxue Li,, Tao Wang, and Yuxuan Li. "Research on Flexible workshop Scheduling Based on harmony search Algorithm." Frontiers in Humanities and Social Sciences 4, no. 4 (2024): 125–34. http://dx.doi.org/10.54691/ds2w4m92.

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The purpose of this paper is to use the harmony search algorithm to solve the scheduling problem of flexible job shop, with the goal of minimizing the maximum completion time. The issue of flexible workshop scheduling is an important research content in the field of production operation management. It involves how to rationally arrange the processing sequence of different jobs on different machines to optimize production efficiency. As a heuristic optimization algorithm, the harmony search algorithm performs well in solving complex optimization problems with its good global search ability and

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Phanden, Rakesh Kumar, Ajai Jain, and Rajiv Verma. "A Genetic Algorithm-Based Approach for Flexible Job Shop Scheduling." Applied Mechanics and Materials 110-116 (October 2011): 3930–37. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.3930.

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Flexible job shop scheduling is a hard combinatorial optimization problem. This paper introduces a simulation-based Genetic Algorithm approach to solve flexible job shop scheduling problem. Four manufacturing scenarios have been considered to access the performance of a job shop with objective to minimize mean tardiness, mean flow time and makespan. Results show that multiple process plans performs better than single process plan for each job type and if only single process plan is made available, then process plan selected on the basis of minimum production time criterion yields better result

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Barzegar, Behnam, Homayun Motameni, and Hossein Bozorgi. "Solving Flexible Job-Shop Scheduling Problem Using Gravitational Search Algorithm and Colored Petri Net." Journal of Applied Mathematics 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/651310.

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Scheduled production system leads to avoiding stock accumulations, losses reduction, decreasing or even eliminating idol machines, and effort to better benefitting from machines for on time responding customer orders and supplying requested materials in suitable time. In flexible job-shop scheduling production systems, we could reduce time and costs by transferring and delivering operations on existing machines, that is, among NP-hard problems. The scheduling objective minimizes the maximal completion time of all the operations, which is denoted by Makespan. Different methods and algorithms ha

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Li, Li, Ke Qi Wang, and Qi Yue. "Research on Optimization Algorithm for MFJSSP." Applied Mechanics and Materials 55-57 (May 2011): 1318–21. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.1318.

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A method combined ant colony algorithm with particle swarm optimization algorithm was designed for solving multi-objective flexible job shop scheduling problem in this paper. In the combined algorithm the start position of ants was marked by particles optimum position obtained by particle swarm optimization algorithm. Then the traditional ant colony algorithm was improved and was used to search the global optimum scheduling. The combined algorithm was validated by practical instances. The results obtained have shown the proposed approach is feasible and effective for the multi-objective flexib

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Hui, Hong Jie. "Approach for Multi-Objective Flexible Job Shop Scheduling." Advanced Materials Research 542-543 (June 2012): 407–10. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.407.

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A multi-objective scheduling method based on the controlled Petri net and GA is proposed to the flexible job shop scheduling problem (FJSP). Function objectives of the proposed method are to minimize the completion time and the total expense and workload of machines. Firstly, a Parikh vector based approach for Petri net controller is introduced, and based on this method, the Petri net model is constructed for FSP with machine breaking down. Then, the genetic algorithm (GA) is applied based on the controlled Petri net model and Pareto. Finally, simulation results based on an example show that t

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Wang, Gui Cong, Chuan Peng Li, and Huan Yong Cui. "Simulation Optimization of Multi-Objective Flexible Job Shop Scheduling." Applied Mechanics and Materials 365-366 (August 2013): 602–5. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.602.

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Current scheduling approach for multiple objective flexible job shop problem (FJSP) cannot construct a precise scheduling model and obtain a satisfactory scheduling result at the same time. To deal with this problem, a simulation optimization scheduling approach was presented which was composed of two basic modules: the Fast non-dominated Sorting Genetic Algorithm (NSGA-II) module and Witness simulation module. Firstly, a multi-objective mathematical model was found for FJSP and NSGA-II was applied to solve. Then, a set of Pareto optimal solution was obtained by NSGA-II module. In order to sel

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Wei, Wei, Yi Xiong Feng, Jian Rong Tan, and Ichiro Hagiwara. "Flexible Job Shop Scheduling Multi-Objective Optimization Based on Improved Strength Pareto Evolutionary Algorithm." Advanced Materials Research 186 (January 2011): 546–51. http://dx.doi.org/10.4028/www.scientific.net/amr.186.546.

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Scheduling for the flexible job shop is very important in fields of production management. To solve the multi–objective optimization in flexible job shop scheduling problem (FJSP), the FJSP multi-objective optimization model is constructed. The cost, quality and time are taken as the optimization objectives. An improved strength Pareto evolutionary algorithm (SPEA2+) is put forward to optimize the multi-objective optimization model parallelly. The algorithm uses a new model of a Multi-objective genetic algorithm that includes more effective crossover and could obtain diverse solutions in the o

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Lim, Che Han, and Seung Ki Moon. "A Two-Phase Iterative Mathematical Programming-Based Heuristic for a Flexible Job Shop Scheduling Problem with Transportation." Applied Sciences 13, no. 8 (2023): 5215. http://dx.doi.org/10.3390/app13085215.

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In a flexible job shop problem with transportation (FJSPT), a typical flexible manufacturing system comprises transporters that pick up and deliver jobs for processing at flexible job shops. This problem has grown in importance through the wide use of automated transporters in Industry 4.0. In this article, a two-phase iterative mathematical programming-based heuristic is proposed to minimize makespan using a machine-operation assignment centric decomposition scheme. The first phase approximates the FJSPT through an augmented flexible job shop scheduling problem (FJSP + T) that reduces the sol

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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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Wang, Jun Liang, Jie Zhang, and Wei Qin. "GA-Based Proactive-Reactive Scheduling Mechanism for a Flexible Job Shop Problem under Multi-Uncertainty." Key Engineering Materials 620 (August 2014): 668–75. http://dx.doi.org/10.4028/www.scientific.net/kem.620.668.

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This article investigates the flexible job-shop scheduling problem (FJSP) with multi-uncertain. A proactive-reactive scheduling mechanism is put forward to against the fluctuating process time and equipment breakdowns. This mechanism consists of two stages, including proactive scheduling stage and reactive scheduling stage. In the proactive scheduling stage, the redundancy-based technique is used to generate robust baseline schedules; in the reactive scheduling stage, a reactive scheduling is adopted to rectify the predictive scheduling to adapt to the occurrence of machine failures. Based on

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Na, Hongbum, and Jinwoo Park. "Multi-level job scheduling in a flexible job shop environment." International Journal of Production Research 52, no. 13 (2013): 3877–87. http://dx.doi.org/10.1080/00207543.2013.848487.

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Ida, Kenichi, and Kensaku Oka. "Flexible Job-Shop Scheduling Problem by Genetic Algorithm." IEEJ Transactions on Electronics, Information and Systems 129, no. 3 (2009): 505–11. http://dx.doi.org/10.1541/ieejeiss.129.505.

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Chan, F. T. S., T. C. Wong, and L. Y. Chan. "Flexible job-shop scheduling problem under resource constraints." International Journal of Production Research 44, no. 11 (2006): 2071–89. http://dx.doi.org/10.1080/00207540500386012.

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Ham, Andy. "Transfer-robot task scheduling in flexible job shop." Journal of Intelligent Manufacturing 31, no. 7 (2020): 1783–93. http://dx.doi.org/10.1007/s10845-020-01537-6.

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Trentesaux, Damien, Cyrille Pach, Abdelghani Bekrar, et al. "Benchmarking flexible job-shop scheduling and control systems." Control Engineering Practice 21, no. 9 (2013): 1204–25. http://dx.doi.org/10.1016/j.conengprac.2013.05.004.

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Yuan, Yuan, and Hua Xu. "Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms." IEEE Transactions on Automation Science and Engineering 12, no. 1 (2015): 336–53. http://dx.doi.org/10.1109/tase.2013.2274517.

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Fattahi, Parviz, Fariborz Jolai, and Jamal Arkat. "Flexible job shop scheduling with overlapping in operations." Applied Mathematical Modelling 33, no. 7 (2009): 3076–87. http://dx.doi.org/10.1016/j.apm.2008.10.029.

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Saidi-Mehrabad, Mohammad, and Parviz Fattahi. "Flexible job shop scheduling with tabu search algorithms." International Journal of Advanced Manufacturing Technology 32, no. 5-6 (2006): 563–70. http://dx.doi.org/10.1007/s00170-005-0375-4.

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Ida, Kenichi, and Kensaku Oka. "Flexible job-shop scheduling problem by genetic algorithm." Electrical Engineering in Japan 177, no. 3 (2011): 28–35. http://dx.doi.org/10.1002/eej.21194.

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