Knowledge-enhanced multi-objective memetic algorithm for energy-efficient flexible job shop scheduling with limited multi-load automated guided vehicles

In alignment with the national call for energy conservation and emission reduction, energy-efficient scheduling in manufacturing, especially intelligent workshops, has become a key research area. Automated guided vehicles (AGVs), as the core component of intelligent logistics systems, especially in...

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Veröffentlicht in:	Engineering applications of artificial intelligence Jg. 159; S. 111771
Hauptverfasser:	Fan, Lianghua, Lei, Qi, Song, Yuchuan, Liu, Yang, Yang, Yunfan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 08.11.2025
Schlagworte:	Energy-efficient flexible job shop scheduling Memetic algorithm Multi-load automated guided vehicles Multi-objective optimization Total energy consumption Multi-load automated guided vehicles Memetic algorithm Multi-objective optimization Total energy consumption Energy-efficient flexible job shop scheduling
ISSN:	0952-1976
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Zusammenfassung:	In alignment with the national call for energy conservation and emission reduction, energy-efficient scheduling in manufacturing, especially intelligent workshops, has become a key research area. Automated guided vehicles (AGVs), as the core component of intelligent logistics systems, especially in applying multi-load AGVs, play a vital role in improving green manufacturing and optimizing logistics efficiency. While AGV transportation is considered in traditional energy-saving scheduling, most studies assume unlimited AGVs, and each can only load one job. This paper is the first to study the energy-efficient flexible job shop scheduling with limited multi-load AGVs (EFJSP-LMA), which integrates the sequencing of pickup and delivery tasks, and the allocation strategy of machines and AGVs. To address this problem effectively, the multi-objective mixed-integer programming (MMIP) model is developed to optimize the makespan and total energy consumption. To solve the MMIP model, a knowledge-enhanced multi-objective memetic algorithm (KMMA) is proposed. In the proposed KMMA, problem-specific heuristics are designed to generate a high-quality initial population with strong convergence and diversity. Subsequently, five knowledge-enhanced variable neighborhood structures are designed to enhance the quality and diversity of solutions. Additionally, an energy-saving strategy is incorporated to further optimize energy consumption. The effect of AGV quantity and load modes on the performance of the production system is studied and analyzed. Furthermore, experiment results of 60 test instances indicate that KMMA outperforms comparison algorithms, demonstrating its effectiveness in addressing the EFJSP-LMA. Finally, Real-world case studies further support our research, offering valuable insights for managing manufacturing environments.
ISSN:	0952-1976
DOI:	10.1016/j.engappai.2025.111771