Abstract
In the era of escalating customer demands for tailored products, advancements in intelligent manufacturing technologies and the proliferation of diverse production and operational models, production-logistics systems face heightened internal and external disruptions. This work contributes a novel dynamic multi-objective opti-state decision-making framework and method to address the complex decision-making challenges that arise from such disruptions. It delves into the re-decision-making requirements for maintaining optimal state performance in production-logistics systems amidst disturbances, focusing on operational objectives. The proposed method employs intelligent algorithms for local sub-models, utilizing the gray target theory to determine the best dynamic multi-objective optimization strategy. To demonstrate its practicality, the method is instantiated as an intermittent synchronized production operation system, with a case study in the context of enterprises with intermittent production, validating the effectiveness of the proposed approach.
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Acknowledgment
This paper is financially supported by National Natural Science Foundation of China (52305538, 52375498), National Key Research and Development Program of China (2021YFB3301701), 2019 Guangdong Special Support Talent Program – Innovation and Entrepreneurship Leading Team (China) (2019BT02S593), 2018 Guangzhou Leading Innovation Team Program (China) (201909010006). We also appreciate Carpoly Chemical Group Co., Ltd. for providing the application scenario.
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Zhang, K. et al. (2024). Dynamic Multi-objective Opti-State Decision-Making Method for Intermittent Synchronized Production Operation System. In: Thürer, M., Riedel, R., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments. APMS 2024. IFIP Advances in Information and Communication Technology, vol 732. Springer, Cham. https://doi.org/10.1007/978-3-031-71637-9_31
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DOI: https://doi.org/10.1007/978-3-031-71637-9_31
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