Multi-objective low-carbon hybrid flow-shop scheduling via an improved teaching-learning-based optimization algorithm

Document Type : Research 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, China.

2 College of Engineering, Nanjing Agricultural University, Nanjing, China.

3 Department of Electrical Engineering, École de Technologie Supérieure, University of Québec, Montréal, Canada.

4 School of Econ & Management, Fuzhou University, Fuzhou, China.

5 State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China.

6 Department of Shop Management in Ruixing Group Co., Ltd., Taian, China.

7 Institute of Systems Engineering, Macau University of Science and Technology, Macau, China.

8 School of Electro-Mechanical Engineering, Xidian University, Xi’an, China.

10.24200/sci.2022.58317.5665

Abstract

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.

Keywords

Main Subjects

References

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Scientia Iranica
Volume 32, Issue 12
Transactions on Industrial Engineering
May and June 2025 Article ID:5665

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  • Receive Date: 12 May 2021
  • Revise Date: 11 October 2021
  • Accept Date: 03 January 2022

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