A multi-dimensional co-evolutionary algorithm for multi-objective resource-constrained flexible flowshop with robotic transportation

Applied Soft Computing, Journal Year: 2025, Volume and Issue: 170, P. 112689 - 112689

Published: Jan. 2, 2025

Language: Английский

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

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 120, P. 109780 - 109780

Published: Oct. 18, 2024

Language: Английский

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Comparison of lot streaming division methodologies for multi-objective hybrid flowshop scheduling problem by considering limited waiting time

Journal of Industrial and Management Optimization, Journal Year: 2024, Volume and Issue: 20(11), P. 3373 - 3414

Published: Jan. 1, 2024

In this paper, a multi-objective hybrid flowshop scheduling problem (HFSP) with limited waiting time and machine capability constraints is addressed. Given its importance, the implementation of lot streaming division methodologies investigated through design experiment (DoE) setting based on real data extracted from leading tire manufacturer in Gebze, Turkey. By doing so, specific characteristics addressed HFSP can be further explored to provide insights into complexity suggest recommendations for improving operational efficiency such systems resembling it. Based specifications constraints, novel generic optimization model objectives including makespan, average flow time, total workload imbalance formulated. Since studied NP-hard strong sense, several algorithms non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ) are proposed according methodologies, i.e., consistent sublots equal sublots. main aim analyze problem, developed compared each other gain remarkable problem. Four different comparison metrics employed assess solution quality terms intensification diversification aspects. Computational results demonstrate that employing sublot methodology leads significant improvements all methodology.

Language: Английский

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A feedback learning-based selection hyper-heuristic for distributed heterogeneous hybrid blocking flow-shop scheduling problem with flexible assembly and setup time

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 131, P. 107818 - 107818

Published: Jan. 9, 2024

Language: Английский

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A reinforcement learning enhanced memetic algorithm for multi-objective flexible job shop scheduling toward Industry 5.0

International Journal of Production Research, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 29

Published: May 30, 2024

Flexible job shop scheduling problem (FJSP) with worker flexibility has gained significant attention in the upcoming Industry 5.0 era because of its computational complexity and importance production processes. It is normally assumed that each machine typically operated by one at any time; therefore, shop-floor managers need to decide on most efficient assignments for machines workers. However, processing time variable uncertain due fluctuating environment caused unsteady operating conditions learning effect Meanwhile, they also balance workload while meeting efficiency. Thus a dual resource-constrained FJSP worker's fuzzy (F-DRCFJSP-WL) investigated simultaneously minimise makespan, total workloads maximum workload. Subsequently, reinforcement enhanced multi-objective memetic algorithm based decomposition (RL-MOMA/D) proposed solving F-DRCFJSP-WL. For RL-MOMA/D, Q-learning incorporated into perform neighbourhood search further strengthen exploitation capability algorithm. Finally, comprehensive experiments extensive test instances case study aircraft overhaul are conducted demonstrate effectiveness superiority method.

Language: Английский

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A Q-learning driven multi-objective evolutionary algorithm for worker fatigue dual-resource-constrained distributed hybrid flow shop

Computers & Operations Research, Journal Year: 2024, Volume and Issue: unknown, P. 106919 - 106919

Published: Nov. 1, 2024

Language: Английский

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A knowledge-driven scatter search algorithm for the distributed hybrid flow shop scheduling problem

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 142, P. 109915 - 109915

Published: Jan. 2, 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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A variable-representation discrete artificial bee colony algorithm for a constrained hybrid flow shop

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 254, P. 124349 - 124349

Published: May 28, 2024

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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