An Efficient Q-Learning-Based Multi-Objective Intelligent Hybrid Genetic Algorithm for Mixed-Model Assembly Line Efficiency DOI Open Access
Mudassar Rauf, Jabir Mumtaz,

Rabia Adeel

и другие.

Symmetry, Год журнала: 2025, Номер 17(6), С. 811 - 811

Опубликована: Май 22, 2025

In real-life mixed-model assembly lines, multiple problems collectively affect the final production’s performance. this study, lines integrated with balancing and sequencing are considered simultaneously solved. A comprehensive mathematical model is formulated to evaluate current multi-objective problem. An intelligent hybrid genetic algorithm (IHGA) proposed solve line The performance of triggered by integrating heuristic rules through a generation gap mechanism which helps in reducing search space without succumbing local optima. Additionally, parametric tuning performed using Q-learning, enabling adaptive optimization reinforcement learning. This enhance computational efficiency achieve robust algorithm. IHGA rigorously compared existing approaches, including non-dominated sorting algorithm, artificial bee colony, particle swarm optimization, evolutionary based on Decomposition, grey wolf optimizer. Results demonstrate superior across various metrics, showcasing its efficacy optimizing where symmetry task allocation can significantly operational contemporary industrial settings. case study solved validate empirical applicability IHGA. extensive experimental analysis notably shows that outperforms methods.

Язык: Английский

An Efficient Q-Learning-Based Multi-Objective Intelligent Hybrid Genetic Algorithm for Mixed-Model Assembly Line Efficiency DOI Open Access
Mudassar Rauf, Jabir Mumtaz,

Rabia Adeel

и другие.

Symmetry, Год журнала: 2025, Номер 17(6), С. 811 - 811

Опубликована: Май 22, 2025

In real-life mixed-model assembly lines, multiple problems collectively affect the final production’s performance. this study, lines integrated with balancing and sequencing are considered simultaneously solved. A comprehensive mathematical model is formulated to evaluate current multi-objective problem. An intelligent hybrid genetic algorithm (IHGA) proposed solve line The performance of triggered by integrating heuristic rules through a generation gap mechanism which helps in reducing search space without succumbing local optima. Additionally, parametric tuning performed using Q-learning, enabling adaptive optimization reinforcement learning. This enhance computational efficiency achieve robust algorithm. IHGA rigorously compared existing approaches, including non-dominated sorting algorithm, artificial bee colony, particle swarm optimization, evolutionary based on Decomposition, grey wolf optimizer. Results demonstrate superior across various metrics, showcasing its efficacy optimizing where symmetry task allocation can significantly operational contemporary industrial settings. case study solved validate empirical applicability IHGA. extensive experimental analysis notably shows that outperforms methods.

Язык: Английский

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