Dynamic robot routing optimization: State–space decomposition for operations research-informed reinforcement learning

Robotics and Computer-Integrated Manufacturing, Год журнала: 2024, Номер 90, С. 102812 - 102812

Опубликована: Июнь 25, 2024

There is a growing interest in implementing artificial intelligence for operations research the industrial environment. While numerous classic solvers ensure optimal solutions, they often struggle with real-time dynamic objectives and environments, such as routing problems, which require periodic algorithmic recalibration. To deal deep reinforcement learning has shown great potential its capability self-learning optimizing mechanism. However, real-world applications of are relatively limited due to lengthy training time inefficiency high-dimensional state spaces. In this study, we introduce two methods enhance optimization. The first method involves transferring knowledge from during training, accelerates exploration reduces time. second uses state–space decomposer transform space into low-dimensional latent space, allows agent learn efficiently space. Lastly, demonstrate applicability our approach an application automated welding process, where identifies shortest pathway robotic arm weld set dynamically changing target nodes, poses sizes. suggested cuts computation by 25% 50% compared algorithms.

Язык: Английский

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Smart adaptable assembly line rebalancing and maintenance

The International Journal of Advanced Manufacturing Technology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 8, 2025

Язык: Английский

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A Comprehensive Survey on Deep Learning-based Predictive Maintenance

ACM Transactions on Embedded Computing Systems, Год журнала: 2025, Номер unknown

Опубликована: Апрель 24, 2025

With the advent of Industrial 4.0 and push towards Industry 5.0, data generated by industries have become surprisingly large. This abundance significantly boosts machine deep learning models for Predictive Maintenance (PdM). The PdM plays a vital role in extending lifespan industrial equipment machines while also helping to reduce risk unscheduled downtime. Given its multidisciplinary nature, field has been approached from many different angles: this comprehensive survey aims provide an up-to-date overview focused on all learning-based strategies, discussing weaknesses strengths. is based Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) methodological flow, allowing systematic complete review literature. In particular, firstly, we explore main used PdM, mainly Convolutional Neural Networks (ConvNets), Autoencoders (AEs), Generative Adversarial (GANs), Transformers, giving newest such as diffusion foundation models. Then, discuss paradigms applied i.e. , supervised, unsupervised, ensemble, transfer, federated, reinforcement learning. Furthermore, work discusses pipeline data-driven benefits, practical applications, datasets, benchmarks. addition, evaluation metrics each stage state-of-the-art hardware devices are discussed. Finally, challenges future presented.

Язык: Английский

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Joint maintenance and spare part ordering from multiple suppliers for multicomponent systems using a deep reinforcement learning algorithm

Reliability Engineering & System Safety, Год журнала: 2023, Номер 241, С. 109628 - 109628

Опубликована: Сен. 4, 2023

Язык: Английский

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A deep reinforcement learning approach for repair-based maintenance of multi-unit systems using proportional hazards model

Reliability Engineering & System Safety, Год журнала: 2023, Номер 234, С. 109179 - 109179

Опубликована: Фев. 20, 2023

Язык: Английский

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A two-stage RNN-based deep reinforcement learning approach for solving the parallel machine scheduling problem with due dates and family setups

Journal of Intelligent Manufacturing, Год журнала: 2023, Номер 35(3), С. 1107 - 1140

Опубликована: Март 9, 2023

Abstract As an essential scheduling problem with several practical applications, the parallel machine (PMSP) family setups constraints is difficult to solve and proven be NP-hard. To this end, we present a deep reinforcement learning (DRL) approach PMSP considering setups, aiming at minimizing total tardiness. The first modeled as Markov decision process, where design novel variable-length representation of states actions, so that DRL agent can calculate comprehensive priority for each job time point then select next directly according these priorities. Meanwhile, state matrix action vector enable trained instances any scales. handle sequence simultaneously ensure calculated global among all jobs, employ recurrent neural network, particular gated unit, approximate policy agent. based on Proximal Policy Optimization algorithm. Moreover, develop two-stage training strategy enhance efficiency. In numerical experiments, train given instance it much larger experimental results demonstrate strong generalization capability comparison three dispatching rules two metaheuristics further validates superiority

Язык: Английский

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Deep reinforcement learning for maintenance optimization of a scrap-based steel production line

Reliability Engineering & System Safety, Год журнала: 2024, Номер 249, С. 110199 - 110199

Опубликована: Май 18, 2024

This paper presents a Deep Reinforcement Learning (DRL)-based optimization approach for determining the optimal inspection and maintenance planning of scrap-based steel production line. The DRL-based recommends adequate time inspections activities based on monitoring conditions line, such as machine productivity, buffer level, demand. Some practical aspects system, uncertainty duration variable rate machines, were considered. A line was modeled multi-component system considering components dependencies. simulation model developed to simulate dynamics assist with development DRL approach. proposed is compared traditional policies, reactive maintenance, time-based condition-based maintenance. In addition, different algorithms PPO (Proximal Policy Optimization), TRPO (Trust Region DQN (Deep Q-Network) are investigated in case-based scenario. findings indicated potential significant financial savings. Therefore, demonstrates adaptability has be powerful tool industrial competitiveness.

Язык: Английский

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Counterfactual-attention multi-agent reinforcement learning for joint condition-based maintenance and production scheduling

Journal of Manufacturing Systems, Год журнала: 2023, Номер 71, С. 70 - 81

Опубликована: Сен. 13, 2023

Язык: Английский

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A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies

Reliability Engineering & System Safety, Год журнала: 2023, Номер 241, С. 109668 - 109668

Опубликована: Сен. 20, 2023

Язык: Английский

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Hierarchical framework for interpretable and specialized deep reinforcement learning-based predictive maintenance

Data & Knowledge Engineering, Год журнала: 2023, Номер 149, С. 102240 - 102240

Опубликована: Ноя. 1, 2023

Язык: Английский

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