A Q-learning multi-objective grey wolf optimizer for the distributed hybrid flowshop scheduling problem DOI
Jianguo Zheng, Shuilin Chen

Engineering Optimization, Год журнала: 2024, Номер unknown, С. 1 - 20

Опубликована: Окт. 15, 2024

Most existing research focuses on a single objective for the distributed hybrid flowshop scheduling problem (DHFSP). This article multi-objective DHFSP with sequence-dependent set-up time (DHFSP-SDST). A Q-learning grey wolf optimizer (QMOGWO) is designed to optimize makespan, total energy consumption and tardiness. mathematical model DHFSP-SDST established. Several initialization strategies random method are introduced improve quality of initial population. The new individual developed by discrete solution updating mechanism QMOGWO. Based Q-learning, local search avoid optima. To verify performance proposed QMOGWO, different scales instances tested in various factories at stages, simulation results show that QMOGWO outperforms comparison methods.

Язык: Английский

Multi-Objective optimization of selective maintenance process considering profitability and personnel energy consumption DOI
Guangdong Tian, Miao Wang, Jianwei Yang

и другие.

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 110870 - 110870

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
A novel binomial strategy for simultaneous topology and size optimization of truss structures DOI
Ali Mortazavi

Engineering Optimization, Год журнала: 2024, Номер unknown, С. 1 - 35

Опубликована: Май 21, 2024

The current work introduces a new probability-based regulatory mechanism for the simultaneous size and topology optimization of truss structures. proposed mechanism, by leveraging Boolean nature topological variables, attempts to forecast behaviour search algorithm emphasizes either or actions reduce number ineffective iterations. importance this task becomes more evident in further iterations since optimal (or nearly optimal) structure is identified, improper elimination any member can result infeasible mechanisms lead waste several To assess effectiveness auxiliary module, it integrated with different algorithms solve distinct problems. results demonstrate that not only enhances accuracy but also significantly reduces required computational cost.

Язык: Английский

Процитировано

4
A Q-learning grey wolf optimizer for a distributed hybrid flowshop rescheduling problem with urgent job insertion DOI
Shuilin Chen, Jianguo Zheng

Journal of Applied Mathematics and Computing, Год журнала: 2025, Номер unknown

Опубликована: Янв. 17, 2025

Язык: Английский

Процитировано

0
Discrete Gray Wolf Optimizer for Solving Distributed Permutation Flowshop Scheduling Problem DOI
Shuilin Chen, Jianguo Zheng

Concurrency and Computation Practice and Experience, Год журнала: 2025, Номер 37(9-11)

Опубликована: Апрель 15, 2025

ABSTRACT Distributed manufacturing has become a mainstream production mode in economic globalization. A discrete gray wolf optimizer (DGWO) is proposed to solve the distributed permutation flowshop scheduling problem (DPFSP) minimize makespan. First, an extended Nawaz‐Enscore‐Ham2 (ENEH2) and randomly generated hybrid initialization method are used enhance diversity ergodicity of population. Second, population update mechanism for characteristics solved balance exploration exploitation. The variable neighborhood descent search strategy further improve quality solution. Finally, Wilcoxon signed rank Friedman test statistical comparison analysis. To verify performance DGWO, simulation experiments conducted on different scales instances compared with various methods demonstrate advantages DGWO solving DPFSP.

Язык: Английский

Процитировано

0
A Q-learning multi-objective grey wolf optimizer for the distributed hybrid flowshop scheduling problem DOI
Jianguo Zheng, Shuilin Chen

Engineering Optimization, Год журнала: 2024, Номер unknown, С. 1 - 20

Опубликована: Окт. 15, 2024

Most existing research focuses on a single objective for the distributed hybrid flowshop scheduling problem (DHFSP). This article multi-objective DHFSP with sequence-dependent set-up time (DHFSP-SDST). A Q-learning grey wolf optimizer (QMOGWO) is designed to optimize makespan, total energy consumption and tardiness. mathematical model DHFSP-SDST established. Several initialization strategies random method are introduced improve quality of initial population. The new individual developed by discrete solution updating mechanism QMOGWO. Based Q-learning, local search avoid optima. To verify performance proposed QMOGWO, different scales instances tested in various factories at stages, simulation results show that QMOGWO outperforms comparison methods.

Язык: Английский

Процитировано

0