Sharif University of Technology Scientia Iranica 1026-3098 32 12 2025 05 01 Multi-objective low-carbon hybrid flow-shop scheduling via an improved teaching-learning-based optimization algorithm 24165 10.24200/sci.2022.58317.5665 EN Wenjie Wang Key Laboratory of High Efficiency and Clean Mechanical Manufacture (Ministry of Education), National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China. Xuesheng Zhou College of Engineering, Nanjing Agricultural University, Nanjing, China. Guangdong Tian Key Laboratory of High Efficiency and Clean Mechanical Manufacture (Ministry of Education), National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China. Amir M. Fathollahi-Fard Department of Electrical Engineering, École de Technologie Supérieure, University of Québec, Montréal, Canada. Peng Wu School of Econ & Management, Fuzhou University, Fuzhou, China. Chaoyong Zhang State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China. Chengwen Liu Department of Shop Management in Ruixing Group Co., Ltd., Taian, China. Zhiwu Li Institute of Systems Engineering, Macau University of Science and Technology, Macau, China. School of Electro-Mechanical Engineering, Xidian University, Xi’an, China. Journal Article 2021 05 12 In this article, for achieving effective and environmentally friendly production scheduling, we investigate a Multi-objective Low-carbon Hybrid Flow-shop Scheduling Problem (MLHFSP) with the consideration of machines with varied energy usage ratios. The problem is formulated by a multi-objective mathematical model with two optimization objectives, i.e., minimizing Total Carbon Emission (TCE) and makespan (Cmax). We primarily analyse the formation of TCE and derive its mathematical expression. MLHFSP is Non-deterministic Polynomial-hard (NP-hard), therefore, to tackle the model, an Improved multi-objective Teaching-Learning-Based Optimization (ITLBO) algorithm is proposed. The ITLBO algorithm mainly contains global search-based teaching phase and local search-based learning phase. In ITLBO, a solution is represented by two vectors, i.e., job sequence vector and machine assignment vector. Sigma method is utilized to quantify each individual, and to avoid local optimum, Sequential Neighbourhood Search (SNS) method is also adopted. Experimental results validate the feasibility and effectiveness of the proposed ITLBO in addressing MLHFSP. The research findings help manufacturing engineers to seek a sophisticated balance between carbon emission reduction and makespan reduction. https://scientiairanica.sharif.edu/article_24165_8f25327bf67db45be41e7c8b4769d0ee.pdf