Surprisingly Popular-Based Adaptive Memetic Algorithm for Energy-Efficient Distributed Flexible Job Shop Scheduling

Li, Rui; Gong, Wenyin; Lu, Chao; Zhuang, Xinying

doi:10.1109/tcyb.2023.3280175

Cited by 35 publications

(8 citation statements)

References 51 publications

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“…Metaheuristic algorithm [9] Flexible job shop ✗ ✗ ✓ ✗ S Monte Carlo tree search [26] Flexible job shop ✗ ✗ ✓ ✗ S Improved Q-learning [27] Flexible job shop ✗ ✗ ✓ ✗ S Double DQN [34] Flexible job shop ✗ ✗ ✓ ✗ S Double DQN [28] Distributed flexible permutation flow shop…”

Section: Discussionmentioning

confidence: 99%

“…This is far from covering the complex factors that exist in actual production. [11] Flexible job shop ✗ 1 ✗ ✓ ✗ S 2 DQN [32] Flexible job shop ✗ ✗ ✓ ✗ M Improved PPO [33] Flexible job shop ✗ ✗ ✓ ✗ M Hierarchical DQN [10] Distributed assembly No-idle flow-shop ✗ ✗ ✗ ✗ S Q-learning and metaheuristic algorithm [8] Distributed flexible job shop ✓ ✗ ✗ ✗ M Metaheuristic algorithm [15] Distributed flexible job shop ✗ ✗ ✗ ✗ S Metaheuristic algorithm [13] Distributed flexible job shop ✓ ✓ ✗ ✓ M Metaheuristic algorithm [9] Flexible job shop ✗ ✗ ✓ ✗ S Monte Carlo tree search [26] Flexible job shop ✗ ✗ ✓ ✗ S Improved Q-learning [27] Flexible job shop ✗ ✗ ✓ ✗ S Double DQN [34] Flexible job shop ✗ ✗ ✓ ✗ S Double DQN [28] Distributed flexible permutation flow shop…”

Section: Discussionmentioning

confidence: 99%

“…By effectively coordinating multiple workshops and machines, distributed flexible job shop scheduling (DFJS) enables efficient utilization of resources, idle time minimization, production delay reduction, and the overall productivity enhancement. Therefore, distributed manufacturing is gradually emerging as the main production method [9]. De Giovanni and Pezzella [7] first defined the DFJS problem and proposed an improved genetic algorithm to address the problem for small and medium-sized distributed manufacturing units in a single factory.…”

Section: Distributed Flexible Job Shop Schedulingmentioning

confidence: 99%

“…To solve the distributed flexible job shop scheduling problem, the majority of existing works restrict that all the operations of a job must be processed in the same job shop [7][8][9][10]. Few works [11][12][13] allow for workpieces to be transferred and processed in different job shops, assuming homogenous job shops with equal transportation times between job shops and machines to simplify the problem.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Distributed Flexible Job Shop Schedulingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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show abstract

“…However, operator selection is critical to MA performance. Existing MA frameworks always cycle through global search followed by local search, and random selection [33] , polling selection [35] , and confidence-based selection [36,37] are used for operator selection. All of these studies inherently rely on confidence levels.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Chen,

Luo,

Gong

et al. 2024

Complex Syst. Model. Simul.

Self Cite

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This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time (EADHFSP-ST) that simultaneously optimizes the makespan and the energy consumption. We develop a mixed integer linear programming model to describe this problem and present a two-stage adaptive memetic algorithm (TAMA) with a surprisingly popular mechanism. First, a hybrid initialization strategy is designed based on the two optimization objectives to ensure the convergence and diversity of solutions. Second, multiple population coevolutionary approaches are proposed for global search to escape from traditional cross-randomization and to balance exploration and exploitation. Third, considering that the memetic algorithm (MA) framework is less efficient due to the randomness in the selection of local search operators, TAMA is proposed to balance the local and global searches. The first stage accumulates more experience for updating the surprisingly popular algorithm (SPA) model to guide the second stage operator selection and ensures population convergence. The second stage gets rid of local optimization and designs an elite archive to ensure population diversity. Fourth, five problem-specific operators are designed, and non-critical path deceleration and right-shift strategies are designed for energy efficiency. Finally, to evaluate the performance of the proposed algorithm, multiple experiments are performed on a benchmark with 45 instances. The experimental results show that the proposed TAMA can solve the problem effectively.

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Reinforcement Learning-Based Estimation of Distribution Algorithm for Energy-Efficient Distributed Heterogeneous Flexible Job Shop Scheduling Problem

Zhao,

2024

Lecture Notes in Computer Science

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Surprisingly Popular-Based Adaptive Memetic Algorithm for Energy-Efficient Distributed Flexible Job Shop Scheduling

Cited by 35 publications

References 51 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

Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Reinforcement Learning-Based Estimation of Distribution Algorithm for Energy-Efficient Distributed Heterogeneous Flexible Job Shop Scheduling Problem

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