Abstract
With the deepening of economic globalization, the integrated production and transportation mode has become an inevitable tendency of modern supply chain. At the same time, facing increasingly serious ecological and environmental problems, green production and green transportation are important ways to reduce carbon emissions. In this study, a green hybrid flow-shop scheduling and transportation integrated optimization problem (GHFSSTIOP) with the objective of minimizing total cost is investigated. To cope with this problem, we present a hyper-heuristic estimation of distribution algorithm (HHEDA). According to the features of GHFSSTIOP, firstly, a novel loading strategy is proposed. Secondly, estimation of distribution algorithm (EDA) is used for high-level strategy, to learn and accumulate the sequence information of high-quality solutions and their location information in the high-level population, and then generate new high-level solutions by sampling probabilistic model in EDA to enhance the global search ability of HHEDA; subsequently, four effective low-level heuristic operations are designed in the low-level of HHEDA to enhance the local search capability of HHEDA, and a new update strategy is designed to ensure variegation of high-level population. Finally, effectiveness of HHEDA is evidenced by numerical simulation and algorithm comparison.
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Acknowledgements
This research was supported by the National Natural Science Foundation of China (62173169 and 61963022), the Basic Research Key Project of Yunnan Province (202201AS070030) and Yunnan Fundamental Research Projects (grant NO. 202301AT070458).
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Bai, L., Qian, B., Hu, R., Li, Z., Jin, HP. (2023). Hyper-heuristic Estimation of Distribution Algorithm for Green Hybrid Flow-Shop Scheduling and Transportation Integrated Optimization Problem. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14086. Springer, Singapore. https://doi.org/10.1007/978-981-99-4755-3_18
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