FRE-Based Sparrow Search Algorithm for Green Flexible Job Shop Scheduling

Ziming Xue, Jun Zhou
Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, PMLR 278:573-586, 2025.

Abstract

The modern manufacturing is facing the challenge of energy saving and emission reduction. This study addresses the Multi-objective Green Flexible Job-shop Scheduling Problem (MGFJSP) with three objectives makespan, machine workload and carbon emissions, a Fuzzy Relative Entropy (FRE)-based improved Sparrow Search Algorithm (FISSA) is proposed. FISSA begins with special initialize methods to ensure a uniform distribution in solution space. Next, a logarithmic spiral is introduced in scroungers to enhance global search capability. Additionally, an insertion strategy is implemented to reduce machine idle time and carbon emissions. Finally, a FRE coefficient is introduced, where solutions are evaluated by comparing them with the ideal point, diversity is quantified, and selection is guided. Experimental results confirm that FISSA outperforms other multi-objective algorithms, significantly minimizing processing time and carbon emissions, demonstrate superior robustness and convergence.

Cite this Paper

BibTeX
@InProceedings{pmlr-v278-xue25a, title = {FRE-Based Sparrow Search Algorithm for Green Flexible Job Shop Scheduling}, author = {Xue, Ziming and Zhou, Jun}, booktitle = {Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing}, pages = {573--586}, year = {2025}, editor = {Zeng, Nianyin and Pachori, Ram Bilas and Wang, Dongshu}, volume = {278}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v278/main/assets/xue25a/xue25a.pdf}, url = {https://proceedings.mlr.press/v278/xue25a.html}, abstract = {The modern manufacturing is facing the challenge of energy saving and emission reduction. This study addresses the Multi-objective Green Flexible Job-shop Scheduling Problem (MGFJSP) with three objectives makespan, machine workload and carbon emissions, a Fuzzy Relative Entropy (FRE)-based improved Sparrow Search Algorithm (FISSA) is proposed. FISSA begins with special initialize methods to ensure a uniform distribution in solution space. Next, a logarithmic spiral is introduced in scroungers to enhance global search capability. Additionally, an insertion strategy is implemented to reduce machine idle time and carbon emissions. Finally, a FRE coefficient is introduced, where solutions are evaluated by comparing them with the ideal point, diversity is quantified, and selection is guided. Experimental results confirm that FISSA outperforms other multi-objective algorithms, significantly minimizing processing time and carbon emissions, demonstrate superior robustness and convergence.} }
Endnote
%0 Conference Paper %T FRE-Based Sparrow Search Algorithm for Green Flexible Job Shop Scheduling %A Ziming Xue %A Jun Zhou %B Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2025 %E Nianyin Zeng %E Ram Bilas Pachori %E Dongshu Wang %F pmlr-v278-xue25a %I PMLR %P 573--586 %U https://proceedings.mlr.press/v278/xue25a.html %V 278 %X The modern manufacturing is facing the challenge of energy saving and emission reduction. This study addresses the Multi-objective Green Flexible Job-shop Scheduling Problem (MGFJSP) with three objectives makespan, machine workload and carbon emissions, a Fuzzy Relative Entropy (FRE)-based improved Sparrow Search Algorithm (FISSA) is proposed. FISSA begins with special initialize methods to ensure a uniform distribution in solution space. Next, a logarithmic spiral is introduced in scroungers to enhance global search capability. Additionally, an insertion strategy is implemented to reduce machine idle time and carbon emissions. Finally, a FRE coefficient is introduced, where solutions are evaluated by comparing them with the ideal point, diversity is quantified, and selection is guided. Experimental results confirm that FISSA outperforms other multi-objective algorithms, significantly minimizing processing time and carbon emissions, demonstrate superior robustness and convergence.
APA
Xue, Z. & Zhou, J.. (2025). FRE-Based Sparrow Search Algorithm for Green Flexible Job Shop Scheduling. Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 278:573-586 Available from https://proceedings.mlr.press/v278/xue25a.html.

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