Abstract
Smart manufacturing is an effective way to improve the efficiency of resource utilization and reduce the response time of making joint decisions for the enterprises. Though, with the globalization of manufacturing enterprises, manufacturing optimization problems often occur in complex manufacturing systems under the deteriorating and fuzzy environment, which brings many challenges to smart manufacturing, such as the lack of coordinating scheduling strategies to guarantee the low latency requirement. This paper investigates a robust parallel-batching scheduling problem with fuzzy processing time and past-sequence-dependent delivery time. Some structural properties are first identified, and an optimal algorithm is further developed for the single-machine scheduling problem. Then, the problem is proved to be NP-hard. We thus design a hybrid Multi-Verse Optimizer-Variable Neighborhood Search algorithm to solve the investigated problem in a reasonable time. Abundant experiments of different scales are conducted to verify the performance of the proposed hybrid method with a comparison of the state-of-the-art methods. The proposed hybrid meta-heuristic shows excellent results, robustness, and computational time performance under various experiments.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos. 71871080, 71922009, 71601065, 71690235, 71601060, 71531008), and Innovative Research Groups of the National Natural Science Foundation of China (71521001), the Humanities and Social Sciences Foundation of the Chinese Ministry of Education (No. 15YJC630097), and Base of Introducing Talents of Discipline to Universities for Optimization and Decision-making in the Manufacturing Process of Complex Product (111 project: B17014). Prof. Panos M. Pardalos was supported by a Humboldt Research Award (Germany).
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Lu, S., Pei, J., Liu, X. et al. Robust parallel-batching scheduling with fuzzy deteriorating processing time and variable delivery time in smart manufacturing. Fuzzy Optim Decis Making 19, 333–357 (2020). https://doi.org/10.1007/s10700-020-09324-x
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DOI: https://doi.org/10.1007/s10700-020-09324-x