Document Type : Article
Authors
^{1}
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 250061, China
^{2}
College of Engineering, Nanjing Agricultural University, Nanjing 210095, China
^{3}
Department of Electrical Engineering, University of Quebec, Montreal QC 001, Canada
^{4}
Fuzhou University, School of Econ and Management, Fuzhou 350108, China
^{5}
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
^{6}
Ruixing Group Co., Ltd, Taian 271509, China
^{7}
- Institute of Systems Engineering, Macau University of Science and Technology, Taipa 999078, Macau, China - School of Electro-Mechanical Engineering, Xidian University, Xi’an 710071, China
Abstract
In this article, for achieving an effective and environmental-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 on the formation of TCE and derive its mathematical expression. MLHFSP is 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 proposed ITLBO in addressing MLHFSP. The research findings help manufacturing engineers to seek a sophisticated balance between carbon emission reduction and makespan reduction.
Keywords