Dynamic distributed flexible job-shop scheduling problem considering operation inspection

Zhu, Ke‐Xue; Gong, Guiliang; Peng, Ningtao; Zhang, Liqiang; Huang, Dan; Luo, Qiang; Li, Xiaoqiang

doi:10.1016/j.eswa.2023.119840

Cited by 21 publications

(4 citation statements)

References 47 publications

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“…The majority of traditional DFJS methods mainly consider static scheduling issues, neglecting the importance of dynamic scheduling [21]. Since static schedules are fixed, assuming that all data are known beforehand, they are relatively easier to plan and manage, especially in stable and predictable environments.…”

Section: Dynamic Scheduling and Deep Reinforcement Learning Methodsmentioning

confidence: 99%

“…Improved DQN [29] Job shop ✗ ✗ ✓ ✗ S Proximal policy optimization [25] Flexible job shop ✓ ✗ ✓ ✗ M Hybrid DQN [30] Semiconductor fabrication ✗ ✗ ✓ ✗ M Improved DQN [21] Distributed flexible job shop ✓ ✗ ✓ ✓ M Metaheuristic algorithm tation resources. Based on the conjugate DQN of DRL algorithm, Lee and Lee [30] p posed a novel state, action, and reward optimization scheduling strategy to achieve s learning and self-optimizing semiconductor manufacturing systems.…”

Section: References Type Of Problem Low-carbon Heterogeneity Dynamics...mentioning

confidence: 99%

“…Metaheuristic algo rithm M Improved DQN [21] Distributed flexible job shop tation resources. Based on the conjugate DQN of DRL algorithm, Lee and Lee [30] proposed a novel state, action, and reward optimization scheduling strategy to achieve selflearning and self-optimizing semiconductor manufacturing systems.…”

Section: References Type Of Problem Low-carbon Heterogeneity Dynamics...mentioning

confidence: 99%

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Dynamic Intelligent Scheduling in Low-Carbon Heterogeneous Distributed Flexible Job Shops with Job Insertions and Transfers

Chen,

Liao,

Chen

et al. 2024

Sensors

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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 considerations of low-carbon objectives. This paper establishes a multi-objective mathematical model with the goal of minimizing the total weighted tardiness and total energy consumption. To effectively solve this problem, diverse composite scheduling rules are formulated, alongside the application of a deep reinforcement learning (DRL) framework, i.e., Rainbow deep-Q network (Rainbow DQN), to learn the optimal scheduling strategy at each decision point in a dynamic environment. To verify the effectiveness of the proposed method, this paper extends the standard dataset to adapt to the LHDFJSP. Evaluation results confirm the generalization and robustness of the presented Rainbow DQN-based method.

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Section: Dynamic Scheduling and Deep Reinforcement Learning Methodsmentioning

confidence: 99%

Section: References Type Of Problem Low-carbon Heterogeneity Dynamics...mentioning

confidence: 99%

Section: References Type Of Problem Low-carbon Heterogeneity Dynamics...mentioning

confidence: 99%

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Dynamic Intelligent Scheduling in Low-Carbon Heterogeneous Distributed Flexible Job Shops with Job Insertions and Transfers

Chen,

Liao,

Chen

et al. 2024

Sensors

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“…However, the actual manufacturing environment is usually dynamic, which means that unexpected random events (such as the arrival of new jobs, machine failures, reworking of jobs, etc.) often occur in the dynamic flexible job shop scheduling problem (DFJSSP) [15]. Traditional meta-heuristic methods typically use rescheduling mechanisms to tackle these dynamic events, but these approaches present challenges in terms of increased computational complexity and stability issues [16,17].…”

Section: Introductionmentioning

confidence: 99%

Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets

Xu,

Fei

et al. 2024

Mathematics

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Considering the requirements of the actual production scheduling process, the utilization of the genetic programming hyper-heuristic (GPHH) approach to automatically design dispatching rules (DRs) has recently emerged as a popular optimization approach. However, the decision objects and decision environments for routing and sequencing decisions are different in the dynamic flexible job shop scheduling problem (DFJSSP), leading to different required feature information. Traditional algorithms that allow these two types of scheduling decisions to share one common feature set are not conducive to the further optimization of the evolved DRs, but instead introduce redundant and unnecessary search attempts for algorithm optimization. To address this, some related studies have focused on customizing the feature sets for both routing and sequencing decisions through feature selection when solving single-objective problems. While being effective in reducing the search space, the selected feature sets also diminish the diversity of the obtained DRs, ultimately impacting the optimization performance. Consequently, this paper proposes an improved GPHH with dual feature weight sets for the multi-objective energy-efficient DFJSSP, which includes two novel feature weight measures and one novel hybrid population adjustment strategy. Instead of selecting suitable features, the proposed algorithm assigns appropriate weights to the features based on their multi-objective contribution, which could provide directional guidance to the GPHH while ensuring the search space. Experimental results demonstrate that, compared to existing studies, the proposed algorithm can significantly enhance the optimization performance and interpretability of energy-efficient DRs.

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An Improved Genetic Algorithm Combining Tabu Search for Solving Flexible Job Shop Scheduling Problem with Transportation and Start-Stop Constraints

Gu,

Xu,

et al. 2024

Lecture Notes in Computer Science

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Dynamic distributed flexible job-shop scheduling problem considering operation inspection

Cited by 21 publications

References 47 publications

Dynamic Intelligent Scheduling in Low-Carbon Heterogeneous Distributed Flexible Job Shops with Job Insertions and Transfers

Dynamic Intelligent Scheduling in Low-Carbon Heterogeneous Distributed Flexible Job Shops with Job Insertions and Transfers

Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets

An Improved Genetic Algorithm Combining Tabu Search for Solving Flexible Job Shop Scheduling Problem with Transportation and Start-Stop Constraints

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