A Reinforcement Learning Algorithm for Dynamic Job Shop Scheduling

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 350 - 366

Опубликована: Янв. 1, 2025

Язык: Английский

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Artificial Intelligence Application in Production Scheduling Problem Systematic Literature Review: Bibliometric Analysis, Research Trend, and Knowledge Taxonomy

Operations Research Forum, Год журнала: 2024, Номер 5(2)

Опубликована: Апрель 1, 2024

Язык: Английский

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Large scale foundation models for intelligent manufacturing applications: a survey

Journal of Intelligent Manufacturing, Год журнала: 2025, Номер unknown

Опубликована: Янв. 4, 2025

Язык: Английский

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A review of AI-powered production scheduling in smart factories: Impacts on lead times and resource utilization

AIP conference proceedings, Год журнала: 2025, Номер 3262, С. 050022 - 050022

Опубликована: Янв. 1, 2025

Язык: Английский

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Artificial Intelligence (AI) Applications in Job Sequencing and Scheduling

SpringerBriefs in applied sciences and technology, Год журнала: 2025, Номер unknown, С. 21 - 35

Опубликована: Янв. 1, 2025

Язык: Английский

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Explaining Other Bio-inspired Algorithm Applications in Job Sequencing and Scheduling

SpringerBriefs in applied sciences and technology, Год журнала: 2025, Номер unknown, С. 103 - 126

Опубликована: Янв. 1, 2025

Язык: Английский

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Resource allocation strategies and task scheduling algorithms for cloud computing: A systematic literature review

Journal of Intelligent Systems, Год журнала: 2025, Номер 34(1)

Опубликована: Янв. 1, 2025

Abstract The concept of cloud computing has completely changed how computational resources are delivered and used. By enabling on-demand access to collective through the internet. While this technological shift offers unparalleled flexibility, it also brings considerable challenges, especially in scheduling resource allocation, particularly when optimizing multiple objectives a dynamic environment. Efficient allocation critical computing, as they directly impact system performance, utilization, cost efficiency heterogeneous conditions. Existing approaches often face difficulties balancing conflicting objectives, such reducing task completion time while staying within budget constraints or minimizing energy consumption maximizing utilization. As result, many solutions fall short optimal leading increased costs degraded performance. This systematic literature review (SLR) focuses on research conducted between 2019 2023 Following preferred reporting items for reviews meta-analyses guidelines, ensures transparent replicable process by employing inclusion criteria bias. explores key concepts management classifies existing strategies into mathematical, heuristic, hyper-heuristic approaches. It evaluates popular algorithms designed optimize metrics consumption, reduction, makespan minimization, performance satisfaction. Through comparative analysis, SLR discusses strengths limitations various schemes identifies emerging trends. underscores steady growth field, emphasizing importance developing efficient address complexities modern systems. findings provide comprehensive overview current methodologies pave way future aimed at tackling unresolved challenges management. work serves valuable practitioners academics seeking environments, contributing advancements computing.

Язык: Английский

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Examining Different Artificial Intelligence Techniques Used in a Mixed Model Stochastic System to Increase Production Efficiency

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 481 - 493

Опубликована: Янв. 1, 2025

Язык: Английский

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Adversarial attacks on artificial Internet of Things-based operational technologies in theme parks

Internet of Things, Год журнала: 2025, Номер unknown, С. 101654 - 101654

Опубликована: Май 1, 2025

Язык: Английский

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Deep reinforcement learning for solving car resequencing with selectivity banks in automotive assembly shops

International Journal of Production Research, Год журнала: 2024, Номер unknown, С. 1 - 22

Опубликована: Сен. 24, 2024

Язык: Английский

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