Research on flexible job shop scheduling under finite transportation conditions for digital twin workshop

Yan, Jian Hua; Liu, Zhifeng; Zhang, Caixia; Zhang, Tao; Zhang, Yueze; Yang, Congbin

doi:10.1016/j.rcim.2021.102198

Cited by 70 publications

(12 citation statements)

References 38 publications

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“…With the increasing trend of small-batch and multi-variety production modes, the flexible job shop scheduling model has quickly gained research attentions in the field of production scheduling. Yan et al (2021) addressed the transportation constraint conflicts that limit the applicability of DT-based flexible workshop scheduling system in practise, while Nouiri et al (2020) focused on optimising energy consumption by introducing green rescheduling algorithm.…”

Section: Cluster 0: Dt In Job-shop Scheduling Problemsmentioning

confidence: 99%

Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Nguyen

Duong

Nguyen

et al. 2022

International Journal of Production Economics

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Section: Cluster 0: Dt In Job-shop Scheduling Problemsmentioning

confidence: 99%

Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Nguyen

Duong

Nguyen

et al. 2022

International Journal of Production Economics

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“…Villalonga et al 23 proposed a digital twin integrated framework to perform production scheduling optimization. Yan et al 24 designed improved genetic algorithm-based method to deal with flexible job shop scheduling problem (FJSP) for DTS, taking into account finite transportation conditions. Through the above literature analysis, it can conclude that many studies were devoted to DTS for realizing scheduling by using heuristic algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Digital twin and deep reinforcement learning enabled real-time scheduling for complex product flexible shop-floor

Chang

Jia

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Real-time scheduling methods are essential and critical to complex product flexible shop-floor due to the dynamic events in the production process, such as new job insertions, machine breakdowns and frequent rework. Recently, digital twin (DT) technology can help identify disturbances by continuously comparing physical space with virtual space, which enables real-time scheduling and greatly reduces the deviation between pre-schedule and actual schedule. However, the conventional scheduling models and algorithms cannot satisfy the adaptiveness and timeliness requirements of optimization in DT enabled shop-floor (DTS). To address above challenges, an overall framework of DT enabled real-time scheduling (DTE-RS) for complex product shop-floor is proposed to effectively reduce adverse impacts of the dynamic disturbances and minimize the makespan. Firstly, complex product flexible job shop scheduling problem (CPFJSP) is formulated as Markov Decision Process (MDP), taking into account machine breakdown and new job insertions. Then, deep Q-network (DQN) based solution is developed to achieve optimal task dispatching according to real-time production state. Finally, the case study for aircraft overhaul shop-floor is conducted to demonstrate effectiveness and feasibility of the proposed real-time scheduling method. Through experimental comparison, it is indicated that the proposed method could effectively respond to dynamic disturbances and outperform the dynamic scheduling method in terms of makespan.

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“…In previous research, the digital twin has been used to assist with a scheduling problems. Machine failure detection and performance evaluation [27], [29], analysis of transportation and production processing stages [60], process simulation and production scheduling [17], and production scheduling for defense weapon systems [83] are among the studies. The reason why the simulation package becomes better than the stochastic Petri-net package is, because of its convenience, timely, and easier to operate nature [102].…”

Section: Digital Twinmentioning

confidence: 99%

“…Based on the analysis, existing manufacturing paradigms have the following limitations. Interconnection between physical machines and virtual models, the interconnection between the virtual model and physical production, generation of accurate data by converging the data from virtual and physical spaces, a realization of intelligent production simulation and optimization [30]; and lack of consideration of actual transportation condition in shop floors [60] are among the challenges. Most of the existing studies on digital twins focus on individual machines [83] and remain a challenge on how to construct and when to apply them on the shop floor [102].…”

Section: Digital Twinmentioning

confidence: 99%

Scheduling Algorithms: Challenges Towards Smart Manufacturing

Workneh

Gmira

2022

Int. j. electr. comput. eng. syst. (Online)

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Collecting, processing, analyzing, and driving knowledge from large-scale real-time data is now realized with the emergence of Artificial Intelligence (AI) and Deep Learning (DL). The breakthrough of Industry 4.0 lays a foundation for intelligent manufacturing. However, implementation challenges of scheduling algorithms in the context of smart manufacturing are not yet comprehensively studied. The purpose of this study is to show the scheduling No.s that need to be considered in the smart manufacturing paradigm. To attain this objective, the literature review is conducted in five stages using publish or perish tools from different sources such as Scopus, Pubmed, Crossref, and Google Scholar. As a result, the first contribution of this study is a critical analysis of existing production scheduling algorithms' characteristics and limitations from the viewpoint of smart manufacturing. The other contribution is to suggest the best strategies for selecting scheduling algorithms in a real-world scenario.

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Research on flexible job shop scheduling under finite transportation conditions for digital twin workshop

Cited by 70 publications

References 38 publications

Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Digital twin and deep reinforcement learning enabled real-time scheduling for complex product flexible shop-floor

Scheduling Algorithms: Challenges Towards Smart Manufacturing

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