{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T10:12:43Z","timestamp":1768903963551,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Basic Scientific Research Project of Wenzhou City","award":["G2023036"],"award-info":[{"award-number":["G2023036"]}]},{"name":"Basic Scientific Research Project of Wenzhou City","award":["G20240020"],"award-info":[{"award-number":["G20240020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In real-life mixed-model assembly lines, multiple problems collectively affect the final production\u2019s performance. In this study, mixed-model assembly lines integrated with balancing and sequencing problems are considered simultaneously solved. A comprehensive mathematical model is formulated to evaluate the current multi-objective problem. An intelligent hybrid genetic algorithm (IHGA) is proposed to solve the integrated mixed-model assembly line balancing and sequencing problem. The performance of the proposed algorithm is triggered by integrating heuristic rules through a generation gap mechanism which helps in reducing search space without succumbing to local optima. Additionally, parametric tuning of the algorithm is performed using Q-learning, enabling adaptive optimization through reinforcement learning. This helps to enhance computational efficiency and achieve robust performance of the proposed algorithm. The performance of the IHGA algorithm is rigorously compared with existing approaches, including a non-dominated sorting genetic algorithm, multi-objective artificial bee colony, multi-objective particle swarm optimization, multi-objective evolutionary algorithm based on Decomposition, and multi-objective grey wolf optimizer. Results demonstrate the superior performance of the proposed algorithm across various metrics, showcasing its efficacy in optimizing mixed-model assembly lines, where symmetry in task allocation and sequencing can significantly enhance operational efficiency in contemporary industrial settings. Additionally, a real-life case study is solved to validate the empirical applicability of the proposed IHGA. The extensive experimental analysis notably shows that the proposed IHGA outperforms the existing methods.<\/jats:p>","DOI":"10.3390\/sym17060811","type":"journal-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T06:35:09Z","timestamp":1747982109000},"page":"811","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Efficient Q-Learning-Based Multi-Objective Intelligent Hybrid Genetic Algorithm for Mixed-Model Assembly Line Efficiency"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2325-9512","authenticated-orcid":false,"given":"Mudassar","family":"Rauf","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6386-272X","authenticated-orcid":false,"given":"Jabir","family":"Mumtaz","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China"}]},{"given":"Rabia","family":"Adeel","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Modern Languages, Lahore 54000, Pakistan"}]},{"given":"Kaynat Afzal","family":"Minhas","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Engineering and Technology, Taxila 47050, Pakistan"}]},{"given":"Muhammad","family":"Usman","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, City University of Hong Kong, Hong Kong"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.ejor.2007.09.013","article-title":"Sequencing mixed-model assembly lines: Survey, classification and model critique","volume":"192","author":"Boysen","year":"2009","journal-title":"Eur. 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