Multi-objective optimization of material delivery for mixed model assembly lines with energy consideration

• Published in: Journal of cleaner production Vol. 192; pp. 293 - 305
• Main Authors: Zhou, Bing-Hai, Shen, Cheng-Yi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 10-08-2018
• Subjects: Energy efficiency; Mixed-model assembly line; Multi-objective optimization; Part feeding; Mixed-model assembly line; Energy efficiency; Multi-objective optimization; Part feeding
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2018.04.251

Since sustainable scheduling is arousing increasing attention from many manufacturing enterprises and energy consumption is a core problem regarding sustainability, the purpose of this paper is to develop an energy-efficient scheduling method to fulfill material delivery tasks in mixed-model assembly lines. In this research, the objective of minimizing the energy consumption is jointly integrated with the operational criterions when executing material delivery tasks. Owing to the NP-hard nature of the considered problem, a Taboo enhanced Particle Swarm Optimization (TEPSO) algorithm is developed to solve the multi-objective problem. Several improving strategies are applied to enhance the performance of the proposed TEPSO in order to obtain a stronger local search capability and faster search speed. The performance of the proposed TEPSO algorithm is evaluated by comparing with two other high-performing multi-objective optimization methods. Computational experiments are conducted in order to test and verify the effectiveness and efficiency of the proposed TEPSO algorithm. The achievements reported in this paper might be inspiring for further studies on energy-efficient production scheduling.
•This paper studies a new Capacitated Pickup and Delivery Problem (CPDP) in a mixed-model assembly line.•The researched problem is content with the trend that carbon footprints of the manufacturing process should be minimized.•This paper provides referable scheduling trade-offs for real-situation production managements.