Seru scheduling problem with lot streaming and worker transfers: A multi-objective approach

Computers & Operations Research, Год журнала: 2025, Номер unknown, С. 106967 - 106967

Опубликована: Янв. 1, 2025

Язык: Английский

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A reinforcement learning-driven adaptive decomposition algorithm for multi-objective hybrid seru system scheduling considering worker transfer

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 88, С. 101602 - 101602

Опубликована: Май 18, 2024

Язык: Английский

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A learning-based dual-population optimization algorithm for hybrid seru system scheduling with assembly

Swarm and Evolutionary Computation, Год журнала: 2025, Номер unknown, С. 101901 - 101901

Опубликована: Фев. 1, 2025

Язык: Английский

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Reinforcement Learning Driven Cross‐Trained Worker Assignment Approach Based on Big Models: A Study for A Hybrid Seru Production System Considering Learning Effect

Computational Intelligence, Год журнала: 2025, Номер 41(2)

Опубликована: Март 24, 2025

ABSTRACT As manufacturing faces evolving customer demands, the integration of Industrial Internet Things (IIoT) networks is crucial for enhancing production flexibility. In this context, Seru Production System (SPS) has emerged as a highly adaptable mode and emphasizes strategic assignment cross‐trained workers, particularly in hybrid configurations combining divisional rotating serus. This paper proposes novel bi‐objective mathematical model incorporating learning effects to minimize makespan balance workloads among workers. With development Artificial Intelligence Generated Content (AIGC) empowered big models, new breakthroughs have industrial decision‐making. These models utilize deep foundational content processing leverage reinforcement optimize strategies. process provides robust support achieving efficient decision optimization. Building on concepts AIGC training, study employs refine results multi‐objective genetic algorithms, thereby improving solution capability model. Experimental demonstrate that proposed algorithm effectively optimal strategies tuning crossover mutation operations. Additionally, numerical experiments offer insights into formation SPS configurations.

Язык: Английский

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Modeling workers rotation in divisional seru production systems

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 111141 - 111141

Опубликована: Апрель 1, 2025

Язык: Английский

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Multi-population cooperative multi-objective evolutionary algorithm for sequence-dependent group flow shop with consistent sublots

Expert Systems with Applications, Год журнала: 2023, Номер 237, С. 121594 - 121594

Опубликована: Сен. 16, 2023

Язык: Английский

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A knowledge-driven many-objective algorithm for energy-efficient distributed heterogeneous hybrid flowshop scheduling with lot-streaming

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 91, С. 101771 - 101771

Опубликована: Ноя. 14, 2024

Язык: Английский

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Multi-population coevolutionary algorithm for a green multi-objective flexible job shop scheduling problem with automated guided vehicles and variable processing speed constraints

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 91, С. 101774 - 101774

Опубликована: Ноя. 15, 2024

Язык: Английский

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Joint decision-making for divisional seru scheduling and worker assignment considering process sequence constraints

Annals of Operations Research, Год журнала: 2024, Номер 338(2-3), С. 1157 - 1185

Опубликована: Май 22, 2024

Язык: Английский

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Bottleneck alleviation and scheduling optimization of flexible manufacturing system based on information-energy flow model

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 89, С. 101600 - 101600

Опубликована: Июнь 5, 2024

Язык: Английский

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