Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems

Computers & Electrical Engineering, Год журнала: 2024, Номер 120, С. 109780 - 109780

Опубликована: Окт. 18, 2024

Язык: Английский

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An Inverse Reinforcement Learning Algorithm with Population Evolution Mechanism for The Multi-objective Flexible Job-shop Scheduling Problem under Time-of-use Electricity Tariffs

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112764 - 112764

Опубликована: Янв. 1, 2025

Язык: Английский

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A modified multi-agent proximal policy optimization algorithm for multi-objective dynamic partial-re-entrant hybrid flow shop scheduling problem

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 140, С. 109688 - 109688

Опубликована: Ноя. 27, 2024

Язык: Английский

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A modularity-based improved small-world genetic algorithm for large-scale intercell scheduling with flexible routes

Computers & Operations Research, Год журнала: 2025, Номер unknown, С. 106979 - 106979

Опубликована: Янв. 1, 2025

Язык: Английский

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A three-stage adaptive memetic algorithm for multi-objective optimization of flexible assembly job-shop scheduling problem

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 144, С. 110098 - 110098

Опубликована: Янв. 24, 2025

Язык: Английский

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A fully parallel multi-objective genetic algorithm for optimization of flexible shop floor production performance and schedule stability under dynamic environments

Annals of Operations Research, Год журнала: 2025, Номер unknown

Опубликована: Фев. 26, 2025

Язык: Английский

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A matheuristic-based self-learning evolutionary algorithm for lot streaming hybrid flow shop group scheduling with limited auxiliary modules

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 96, С. 101965 - 101965

Опубликована: Май 8, 2025

Язык: Английский

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Bio-inspired model for oil production prediction: combining gene regulation-based optimization and radial basis function network

Memetic Computing, Год журнала: 2025, Номер 17(2)

Опубликована: Май 20, 2025

Язык: Английский

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A matheuristic-based self-learning approach for distributed heterogeneous assembly flowshop scheduling with multiple assembly factories and make-to-order delivery

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 97, С. 101996 - 101996

Опубликована: Июнь 5, 2025

Язык: Английский

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Augmented ɛ‐constraint‐based matheuristic methodology for Bi‐objective production scheduling problems

IET Collaborative Intelligent Manufacturing, Год журнала: 2024, Номер 6(4)

Опубликована: Окт. 9, 2024

Abstract Matheuristic is an optimisation methodology that integrates mathematical approaches and heuristics to address intractable combinatorial problems, where a common framework insert mixed integer linear programming (MILP) models as local search functions for evolutionary algorithms. However, since formulation only tries find the solution with best objective value, matheuristics are rarely adopted multi‐objective scenarios asking set of Pareto optimal solutions, example, vehicle routing problems production scheduling problems. In this situation, ɛ ‐constraint, which transforms into single‐objective formulations by considering selected objectives constraints, seems be promising approach. First, augmented ‐constraint‐based matheuristic ( ‐MH) proposed apply idea ‐constraint embedded MILP models, so fronts obtained meta‐heuristics can further improved solving models. Afterwards, four speed‐up strategies developed alleviate computational burden resulting from repeatedly formulations, also imply preferable taking advantages ‐MH. Finally, several real‐world bi‐objective discussed present potential applications methodology.

Язык: Английский

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