Digital twin oriented multi-objective flexible job shop scheduling model and its hybrid particle swarm optimization

Chen, Zhaoming; Zou, Jinsong; Wang, Wei

doi:10.1177/09544054221121921

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…On the other hand, the single-objective scheduling is usually insufficient for real-world manufacturing that often requires the simultaneous performance of dual or more objectives, which has been studied in academia. [20][21][22] Given the above issues, a robust pro-active for dualobjective scheduling scheme is proposed, where the repair time for unexpected machine breakdown is inserted into a disjunctive graph for reinforcement learning (IRDRL) to enhance the generalization adaptability of the scheduling scheme. The primary work of this paper is as follows.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic scheduling for dual-objective job shop with machine breakdown by reinforcement learning

Gan

Zuo

Yang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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In modern complicated and changing manufacturing environments, unforeseen dynamic events such as machine breakdown or unexpected job arrival make required production resources unpredictable. The scheduling scheme is desired to maintain high stability in dynamic manufacturing environments. To cope with the classic disturbance of machine breakdown, a robust pro-active scheduling scheme is proposed by inserting the repair time into a disjunctive graph for reinforcement learning (IRDRL) in this paper. Firstly, a new mathematical model is developed to predict the machine fault which is assumed to be determined by service time and bearing load. Secondly, a disjunctive graph with breakdown information is designed to express the dynamic scheduling status. Then, an online scheduling framework is built based on the well-trained model through the proximal policy optimization (PPO) algorithm. Finally, compared with the classical methods such as the right-shift strategy and static model of reinforcement learning (RL), the proposed robust pro-active scheduling scheme is verified with high robustness, stability, and short running time.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic scheduling for dual-objective job shop with machine breakdown by reinforcement learning

Gan

Zuo

Yang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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Assembly feature construction method of equipment mesh model for digital twin workshops

Luo,

Sheng,

Song

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part B:

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The digital twin workshop is the basic unit for realizing smart manufacturing. Its model is the prerequisite for applying digital twins in workshops. Geometric model assembly is the primary concern in workshop-level digital twin modeling. However, current research lacks sufficient focus on the efficiency and application modes of equipment mesh model assembly features in geometric model assembly, hindering the construction of the workshop-level digital twin model. The equipment-level digital twin model typically only contains assembly information between individual parts and is generally represented by polygonal meshes; the lack of assembly information between equipment models impedes the assembly of the workshop-level digital twin model. To improve the efficiency of workshop-level digital twin modeling and the reusability of the original 3D assembly information, this paper proposes an assembly feature construction method of equipment mesh model for digital twin workshops. A 3D assembly information model is constructed to describe the information related to the equipment geometric model and the assembly features. Then, the assembly features of the equipment mesh model are gradually positioned preliminarily and precisely, achieving precise mapping of the assembly features in the 3D information model to the equipment mesh model and efficient construction of equipment mesh model assembly features. Finally, with the construction and application of equipment mesh model assembly features in an arc welding workshop and storage workshop as an example, the feasibility of the method in improving the modeling efficiency is verified through relevant comparative experiments.

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Dynamic flexible job shop scheduling based on deep reinforcement learning

Yang,

Shu,

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part B:

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The unpredictable dynamic events in smart factory seriously influence the scheduling schemes and production efficiency. To minimize the total tardiness of orders, this paper proposes a Deep Reinforcement Learning (DRL) method to solve the Dynamic Flexible Job Shop Scheduling Problem (DFJSP) with random job arrival. In the scheduling process, the intelligent agent can select the operations to be processed on the available machines according to the job shop state at each scheduling point by transforming DFJSP into a Markov Decision Process (MDP). For reflecting the job shop environment, eight state features are extracted, and six composite dispatching rules are designed as the action space. Moreover, a reward function is developed to enhance the learning efficiency and solution quality of the intelligent agent, the Soft-max selection strategy is applied to balance exploration and exploitation. The Dueling Double Deep Q-Network (Dueling DDQN) algorithm is introduced to train the intelligent agent, which effectively alleviates the overestimation problem. Numerical simulation results show that the proposed Dueling DDQN method effectively solves the DFJSP. Compared with existing scheduling strategies, the proposed approach not only exhibits superior performance but also maintains its advantages among different scale instances.

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Digital twin oriented multi-objective flexible job shop scheduling model and its hybrid particle swarm optimization

Cited by 3 publications

References 34 publications

Dynamic scheduling for dual-objective job shop with machine breakdown by reinforcement learning

Dynamic scheduling for dual-objective job shop with machine breakdown by reinforcement learning

Assembly feature construction method of equipment mesh model for digital twin workshops

Dynamic flexible job shop scheduling based on deep reinforcement learning

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