An Iterative Greedy Algorithm With Q-Learning Mechanism for the Multiobjective Distributed No-Idle Permutation Flowshop Scheduling

IEEE Transactions on Systems Man and Cybernetics Systems, Journal Year: 2024, Volume and Issue: 54(5), P. 3207 - 3219

Published: Feb. 13, 2024

The distributed no-idle permutation flowshop scheduling problem (DNIPFSP) has widely existed in various manufacturing systems. makespan and total tardiness are optimized simultaneously considering the variety of scales problems with introducing an improved iterative greedy (IIG) algorithm. variable neighborhood descent (VND) algorithm is applied to local search method Two perturbation operators based on critical factory proposed as structure VND. In destruction phase, scale varies size problem. An insertion operator-based strategy sorts undeleted jobs after phase. $Q$ -learning mechanism for selecting weighting coefficients introduced obtain a relatively small objective value. Finally, tested benchmark suite compared other existing algorithms. experiments show that IIG obtained more satisfactory results.

Language: Английский

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Intelligent optimization under the makespan constraint: Rapid evaluation mechanisms based on the critical machine for the distributed flowshop group scheduling problem

European Journal of Operational Research, Journal Year: 2023, Volume and Issue: 311(3), P. 816 - 832

Published: May 31, 2023

Language: Английский

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Historical information based iterated greedy algorithm for distributed flowshop group scheduling problem with sequence-dependent setup times

Omega, Journal Year: 2023, Volume and Issue: 123, P. 102997 - 102997

Published: Nov. 4, 2023

Language: Английский

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An iterated greedy algorithm with acceleration of job allocation probability for distributed heterogeneous permutation flowshop scheduling problem

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 88, P. 101580 - 101580

Published: May 3, 2024

Language: Английский

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Evolutionary computation and reinforcement learning integrated algorithm for distributed heterogeneous flowshop scheduling

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 135, P. 108775 - 108775

Published: June 12, 2024

Language: Английский

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An overview of industrial engineering and operations management over the first fifty years of Engineering Optimization

Engineering Optimization, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 12

Published: Jan. 3, 2025

Industrial engineering and operations management have been two of the most studied fields by operational research practitioners in recent decades owing to their theoretical practical significance. Although several surveys addressed various problems this field, best author's knowledge, no survey has dedicated analysing evolution optimization studies over past decades. On 50th anniversary launch journal Engineering Optimization (GENO), all contributions published domain are evaluated. All documents GENO since its inception 1974 analysed, 402 articles selected. The prolific areas field production scheduling, planning control, transportation logistics, routing, cutting packing problems. main emerging trends include integrated distribution problems, application constraint programming methods, incorporation sustainable objective functions, use machine learning combined with metaheuristics.

Language: Английский

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An end-to-end decentralised scheduling framework based on deep reinforcement learning for dynamic distributed heterogeneous flowshop scheduling

International Journal of Production Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 21

Published: Jan. 23, 2025

Language: Английский

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Improved Jaya algorithm for energy-efficient distributed heterogeneous permutation flow shop scheduling

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(2)

Published: Jan. 24, 2025

Language: Английский

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Scheduling distributed heterogeneous non-permutation flowshop to minimize the total weighted tardiness

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126713 - 126713

Published: Feb. 1, 2025

Language: Английский

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Artificial Intelligence to Solve Production Scheduling Problems in Real Industrial Settings: Systematic Literature Review

Electronics, Journal Year: 2023, Volume and Issue: 12(23), P. 4732 - 4732

Published: Nov. 22, 2023

This literature review examines the increasing use of artificial intelligence (AI) in manufacturing systems, line with principles Industry 4.0 and growth smart factories. AI is essential for managing complexities modern manufacturing, including machine failures, variable orders, unpredictable work arrivals. study, conducted using Scopus Web Science databases bibliometric tools, has two main objectives. First, it identifies trends AI-based scheduling solutions most common techniques. Second, assesses real impact on production industrial settings. study shows that particle swarm optimization, neural networks, reinforcement learning are widely used techniques to solve problems. have reduced costs, increased energy efficiency, improved practical applications. increasingly critical addressing evolving challenges contemporary environments.

Language: Английский

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