Shared manufacturing-based distributed flexible job shop scheduling with supply-demand matching

Computers & Industrial Engineering, Год журнала: 2024, Номер 189, С. 109950 - 109950

Опубликована: Фев. 5, 2024

Язык: Английский

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Multimanned disassembly line balancing optimization considering walking workers and task evaluation indicators

Journal of Manufacturing Systems, Год журнала: 2023, Номер 72, С. 263 - 286

Опубликована: Дек. 12, 2023

Язык: Английский

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An enhanced memetic algorithm with hierarchical heuristic neighborhood search for type-2 green fuzzy flexible job shop scheduling

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 130, С. 107762 - 107762

Опубликована: Дек. 26, 2023

Язык: Английский

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Integrated remanufacturing scheduling of disassembly, reprocessing and reassembly considering energy efficiency and stochasticity through group teaching optimization and simulation approaches

Engineering Optimization, Год журнала: 2024, Номер 56(12), С. 2018 - 2039

Опубликована: Янв. 4, 2024

The energy crisis and environmental pollution are receiving increasing attention from governments communities. This study researches energy-aware remanufacturing systems. Remanufacturing aims to reuse valuable resources end-of-life products produce as-new products. Since systems involve a series of disassembly, processing assembly operations, schedule integrates shops. A multi-objective scheduling is proposed, considering workstation use, consumption customer satisfaction simultaneously. chance-constrained programming model established minimize makespan while satisfying total tardiness requirements. hybrid method developed, using group teaching optimization discrete event simulation system, which can seek evaluate potentially favourable solutions. approach validated on test instances well-known methods. results reveal that this find non-dominated solutions with well-converged well-diversified performance, verifying its advantages in providing informed decisions for managers engineers.

Язык: Английский

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Dynamic scheduling of hybrid flow shop problem with uncertain process time and flexible maintenance using NeuroEvolution of Augmenting Topologies

IET Collaborative Intelligent Manufacturing, Год журнала: 2024, Номер 6(3)

Опубликована: Сен. 1, 2024

Abstract A hybrid flow shop is pivotal in modern manufacturing systems, where various emergencies and disturbances occur within the smart context. Efficiently solving dynamic scheduling problem (HFSP), characterised by release times, uncertain job processing flexible machine maintenance has become a significant research focus. NeuroEvolution of Augmenting Topologies (NEAT) algorithm proposed to minimise maximum completion time. To improve NEAT algorithm's efficiency effectiveness, several features were integrated: multi‐agent system with autonomous interaction centralised training develop parallel policy, maintenance‐related action for optimal decision learning, proactive avoid waiting jobs at moments, thereby exploring broader solution space. The performance trained model was experimentally compared Deep Q‐Network (DQN) five classical priority dispatching rules (PDRs) across scales. results show that achieves better solutions responds more quickly changes than DQN PDRs. Furthermore, generalisation test demonstrate NEAT's rapid problem‐solving ability on instances different from set.

Язык: Английский

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A Deep Reinforcement Learning Method Based on a Transformer Model for the Flexible Job Shop Scheduling Problem

Electronics, Год журнала: 2024, Номер 13(18), С. 3696 - 3696

Опубликована: Сен. 18, 2024

The flexible job shop scheduling problem (FJSSP), which can significantly enhance production efficiency, is a mathematical optimization widely applied in modern manufacturing industries. However, due to its NP-hard nature, finding an optimal solution for all scenarios within reasonable time frame faces serious challenges. This paper proposes that transforms the FJSSP into Markov Decision Process (MDP) and employs deep reinforcement learning (DRL) techniques resolution. First, we represent state features of environment using seven feature vectors utilize transformer encoder as extraction module effectively capture relationships between representation capability. Second, based on jobs machines, design 16 composite dispatching rules from multiple dimensions, including completion rate, processing time, waiting resource utilization, achieve efficient decisions. Furthermore, project intuitive dense reward function with objective minimizing total idle machines. Finally, verify performance feasibility algorithm, evaluate proposed policy model Brandimarte, Hurink, Dauzere datasets. Our experimental results demonstrate framework consistently outperforms traditional rules, surpasses metaheuristic methods larger-scale instances, exceeds existing DRL-based across most

Язык: Английский

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A novel neighborhood structure for flexible job shop scheduling problem considering Quality-Efficiency coupling effect

Computers & Industrial Engineering, Год журнала: 2024, Номер unknown, С. 110735 - 110735

Опубликована: Ноя. 1, 2024

Язык: Английский

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A knowledge-driven memetic algorithm for distributed green flexible job shop scheduling considering the endurance of machines

Applied Soft Computing, Год журнала: 2025, Номер 170, С. 112697 - 112697

Опубликована: Янв. 6, 2025

Язык: Английский

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Structural entropy-based scheduler for job planning problems using multi-agent reinforcement learning

International Journal of Machine Learning and Cybernetics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 12, 2025

Язык: Английский

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Deep reinforcement learning-based memetic algorithm for solving dynamic distributed green flexible job shop scheduling problem with finite transportation resources

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 94, С. 101885 - 101885

Опубликована: Фев. 21, 2025

Язык: Английский

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