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

Computers & Operations Research, Год журнала: 2024, Номер unknown, С. 106919 - 106919

Опубликована: Ноя. 1, 2024

Язык: Английский

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A discrete artificial bee colony algorithm and its application in flexible flow shop scheduling with assembly and machine deterioration effect

Applied Soft Computing, Год журнала: 2024, Номер 159, С. 111593 - 111593

Опубликована: Апрель 16, 2024

Язык: Английский

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A multi-objective Immune Balancing Algorithm for Distributed Heterogeneous Batching-integrated Assembly Hybrid Flowshop Scheduling

Expert Systems with Applications, Год журнала: 2024, Номер 259, С. 125288 - 125288

Опубликована: Сен. 12, 2024

Язык: Английский

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Two-stage hybrid flow shop scheduling with sequence-dependent setup times in semiconductor manufacturing: A customized variable neighborhood search

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

Опубликована: Март 13, 2025

Язык: Английский

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Integrated distributed flexible job shop scheduling and vehicle routing problem via Q-learning-based evolutionary algorithms

Information Sciences, Год журнала: 2025, Номер unknown, С. 122169 - 122169

Опубликована: Апрель 1, 2025

Язык: Английский

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Scheduling with Sequence-Dependent Setup Times in Short-Term Production Planning: A Main Path Analysis-Based Review

Operations Research Perspectives, Год журнала: 2025, Номер unknown, С. 100340 - 100340

Опубликована: Апрель 1, 2025

Язык: Английский

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Ensemble evolutionary algorithms equipped with Q‐learning strategy for solving distributed heterogeneous permutation flowshop scheduling problems considering sequence‐dependent setup time

IET Collaborative Intelligent Manufacturing, Год журнала: 2024, Номер 6(1)

Опубликована: Март 1, 2024

Abstract A distributed heterogeneous permutation flowshop scheduling problem with sequence‐dependent setup times (DHPFSP‐SDST) is addressed, which well reflects real‐world scenarios in factories. The objective to minimise the maximum completion time (makespan) by assigning jobs factories, and sequencing them within each factory. First, a mathematical model describe DHPFSP‐SDST established. Second, four meta‐heuristics, including genetic algorithms, differential evolution, artificial bee colony, iterated greedy (IG) algorithms are improved optimally solve concerned compared other existing optimisers literature. Nawaz‐Enscore‐Ham (NEH) heuristic employed for generating an initial solution. Then, five local search operators designed based on characteristics enhance algorithms' performance. To choose appropriately during iterations, Q‐learning‐based strategy adopted. Finally, extensive numerical experiments conducted 72 instances using 5 optimisers. obtained optimisation results comparisons prove that IG algorithm along Q‐learning selection shows better performance respect its peers. proposed exhibits higher efficiency problems.

Язык: Английский

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A cooperative learning-aware dynamic hierarchical hyper-heuristic for distributed heterogeneous mixed no-wait flow-shop scheduling

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 90, С. 101668 - 101668

Опубликована: Июль 29, 2024

Язык: Английский

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A hybrid genetic programming algorithm for the distributed assembly scheduling problems with transportation and sequence-dependent setup times

Engineering Optimization, Год журнала: 2024, Номер unknown, С. 1 - 27

Опубликована: Апрель 23, 2024

This paper investigates a distributed assembly permutation flow-shop scheduling problem with transportation and sequence-dependent set-up times (DAPFSP-TSDST). A hybrid genetic programming (HGP) algorithm is proposed to optimize the makespan of stage, which inherits merits (GP) neighbourhood search operators. In HGP, problem-specific initialization heuristic developed make populations more diverse. Multiple operators are employed as leaf nodes, vital for success GP. product shift strategy strengthen its exploitability. addition, simulated annealing criterion adopted HGP explore thoroughly. Finally, statistical computational experiments carried out on benchmark instances. The results exhaustively identify notable competitiveness in coping DAPFSP-TSDST.

Язык: Английский

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A multiobjective optimizer with a K-means cluster algorithm for a distributed flexible flowshop rescheduling problem

Applied Soft Computing, Год журнала: 2024, Номер 166, С. 112124 - 112124

Опубликована: Авг. 14, 2024

Язык: Английский

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