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Collaborative Hyper-Heuristic Ant Colony Algorithm for Solving Multi-objective Fuzzy Low-Carbon Distributed Permutation Flow-Shop and Two-Echelon Vehicle Transportation Integrated Scheduling Problem

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Advanced Intelligent Computing Technology and Applications (ICIC 2024)

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Abstract

This paper proposed a collaborative hyper-heuristic ant colony algorithm (CHACA) for solving a kind of multi-objective fuzzy low-carbon distributed permutation flow-shop and two-echelon vehicle transportation integrated scheduling problem (MDF-TVISP), which adopt triangular fuzzy numbers (TFNs) to represent job processing times and vehicle travel times. The optimization objectives of MDF-TVISP are to minimize the total fuzzy production and transportation costs and fuzzy carbon emissions. CHACA integrated collaborative ant colony (CACA) and hyper-heuristic algorithm (HHA). We employ eight pre-designed heuristic operations in the low-level heuristics (LLHs) to enhance the algorithm’s local search capability, while utilizing a novel CACA to improve the performance of high-level strategy (HLS). The interaction between LLHs and HLS greatly improves the search performance of the CHACA. Finally, simulation experiments and algorithm comparisons validate the effectiveness of CHACA.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (62173169 and 61963022) and the Basic Research Key Project of Yunnan Province (202201AS070030).

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Correspondence to Rong Hu .

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Ma, Jh., Hu, R., Guo, N., Qian, B. (2024). Collaborative Hyper-Heuristic Ant Colony Algorithm for Solving Multi-objective Fuzzy Low-Carbon Distributed Permutation Flow-Shop and Two-Echelon Vehicle Transportation Integrated Scheduling Problem. In: Huang, DS., Zhang, X., Chen, W. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14862. Springer, Singapore. https://doi.org/10.1007/978-981-97-5578-3_26

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  • DOI: https://doi.org/10.1007/978-981-97-5578-3_26

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  • Print ISBN: 978-981-97-5577-6

  • Online ISBN: 978-981-97-5578-3

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