A single-individual based variable neighborhood search algorithm for the blocking hybrid flow shop group scheduling problem DOI Creative Commons

Zhongyuan Peng,

Haoxiang Qin

Egyptian Informatics Journal, Journal Year: 2024, Volume and Issue: 27, P. 100509 - 100509

Published: July 30, 2024

Language: Английский

Enhancing Quality-Diversity algorithm by reinforcement learning for Flexible Job Shop Scheduling with transportation constraints DOI
Haoxiang Qin, Yi Xiang,

Fangqing Liu

et al.

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 93, P. 101849 - 101849

Published: Jan. 23, 2025

Language: Английский

Citations

0
Constructive-destructive neighbor search drives artificial bee colony algorithm for variable speed green hybrid flowshop scheduling problem DOI Creative Commons
Danying Hu, Yali Wu, Lei Qiu

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 20, 2025

The hybrid flowshop scheduling problem (HFSP), a typical NP-hard problem, has gained significant interest from researchers focusing on the development of solution methods. We focus variable speed problem. assume that machines operate at when processing workpieces, making more reflective real-world scenarios. Aiming this optimization strategy for encoding and decoding is proposed. Meanwhile, we design constructive-destructive search driven artificial bee colony algorithm to solve variable-speed green flow shop minimize makespan total energy consumption. A neighbor method designed update population in employed phase. process redesigned with three operators named technique order preferences similarity ideal solutions, binary tournament selection, global strategies onlooker In scout phase, individual evaluation replacement are designed. Extensive experimental evaluations testify CDSABC outperforms other algorithms regarding best, worst, average, standard deviation IGD index 80% test cases.

Language: Английский

Citations

0
Q-Learning-Driven Accelerated Iterated Greedy Algorithm for Multi-Scenario Group Scheduling in Distributed Blocking Flowshops DOI
Zhen Li,

Yuting Wang,

Yuyan Han

et al.

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113424 - 113424

Published: March 1, 2025

Language: Английский

Citations

0
Optimization of machine configuration and scheduling in the hybrid flow shop using a linear programming-driven evolutionary approach DOI
Mengya Zhang, Cuiyu Wang, Xinyu Li

et al.

Robotics and Computer-Integrated Manufacturing, Journal Year: 2025, Volume and Issue: 95, P. 103029 - 103029

Published: April 21, 2025

Language: Английский

Citations

0
Distributionally robust scheduling for the two-stage hybrid flowshop with uncertain processing time DOI
Zhi Pei,

Rong Dou,

Jiayan Huang

et al.

European Journal of Operational Research, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Language: Английский

Citations

0
Ensemble evolutionary algorithms equipped with Q‐learning strategy for solving distributed heterogeneous permutation flowshop scheduling problems considering sequence‐dependent setup time DOI Creative Commons
Fubin Liu, Kaizhou Gao, Dachao Li

et al.

IET Collaborative Intelligent Manufacturing, Journal Year: 2024, Volume and Issue: 6(1)

Published: March 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.

Language: Английский

Citations

3
A parallel deep adaptive large neighbourhood search algorithm for distributed heterogeneous hybrid flow shops with mixed-model assembly scheduling DOI
Weishi Shao, Zhongshi Shao, Dechang Pi

et al.

Engineering Optimization, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 28

Published: April 26, 2024

Nowadays, manufacturing enterprises must have fast response and flexible production capabilities to meet personalized diversified market demands. Mixed-model distributed become the preferred methods for enterprises. This article studies a heterogeneous hybrid flow shop scheduling problem with mixed-model assembly line (DHHFSP-MMAL), which consists of stages. The DHHFSP-MMAL is modelled by mixed integer linear programming (MILP) model. Three constructive heuristics parallel deep adaptive large neighbourhood search (PDALNS) are presented. A heuristic group strategy employed obtain an initial solution. Several destroy-and-repair operators proposed where problem-specific greedy local applied. PDALNS assigns weights guide selection operators. computing technique introduced increase efficiency training. experiments demonstrate that algorithm efficient effective solving problem.

Language: Английский

Citations

3
A knowledge-driven many-objective algorithm for energy-efficient distributed heterogeneous hybrid flowshop scheduling with lot-streaming DOI
Sanyan Chen, Xuewu Wang, Ye Wang

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 91, P. 101771 - 101771

Published: Nov. 14, 2024

Language: Английский

Citations

3
A Self-Adaptive Collaborative Differential Evolution Algorithm for Solving Energy Resource Management Problems in Smart Grids DOI
Haoxiang Qin, Wenlei Bai, Yi Xiang

et al.

IEEE Transactions on Evolutionary Computation, Journal Year: 2023, Volume and Issue: 28(5), P. 1427 - 1441

Published: Sept. 7, 2023

Handling energy resource management (ERM) in today's systems is complex and challenging due to uncertainties arising from the high penetration of distributed resources. Such introduces various uncertain factors, such as renewable energy, storage, electric vehicles, making it difficult for traditional mathematical methods find effective solutions. However, Evolutionary Algorithms (EAs) have shown good performance solving this problem. Therefore, paper, a self-adaptive collaborative differential evolution algorithm (SADEA) proposed solve ERM problem under uncertainty. In SADEA, three-stage adaptive collaboration strategy, includes boundary randomization stage, knowledge-assisted range restructuration used generate The solutions generated above stages will jointly participate perturbation DE strategies explore promising addition, different are selected according count values random factors. At end algorithm, control, elite selection retention ensure legitimacy robustness SADEA compared several state-of-the-art algorithms on real-world distribution network located Salamanca, Spain. results show that superior its competitors terms objective function, ranking index, convergence. summary, handle

Language: Английский

Citations

8
Mathematical model and adaptive multi-objective evolutionary algorithm for cellular manufacturing with mixed production mode DOI
Lixin Cheng, Qiuhua Tang, Liping Zhang

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 86, P. 101545 - 101545

Published: March 22, 2024

Language: Английский

Citations

2