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Energy-efficient buffer and service rate allocation in manufacturing systems using hybrid machine learning and evolutionary algorithms

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摘要 Currently,simultaneous buffer and service rate allocation is a topic of interest in the optimization of manufacturing systems.Simultaneous allocation problems have been solved previously to satisfy economic requirements;however,owing to the progress of green manufacturing,energy conservation and environmental protection have become increasingly crucial.Therefore,an energy-efficient approach is developed to maximize the throughput and minimize the energy consumption of manufacturing systems,subject to the total buffer capacity,total service rate,and predefined energy efficiency.The energy-efficient approach integrates the simulated annealing-non-dominated sorting genetic algorithm-II with the honey badger algorithm-histogram-based gradient boosting regression tree.The former algorithm searches for Pareto-optimal solutions of sufficient quality.The latter algorithm builds prediction models to rapidly calculate the throughput,energy consumption,and energy efficiency.Numerical examples show that the proposed hybrid approach can achieve a better solution quality compared with previously reported approaches.Furthermore,the prediction models can rapidly evaluate manufacturing systems with sufficient accuracy.This study benefits the multi-objective optimization of green manufacturing systems.
出处 《Advances in Manufacturing》 SCIE EI CAS CSCD 2024年第2期227-251,共25页 先进制造进展(英文版)
基金 supported by the National Natural Science Foundation of China(Grant Nos.61873283 and 52072412) the International Postdoctoral Exchange Fellowship Program(Grant No.YJ20210201).
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  • 1宋东平,The 13th IFAC World Congress,Vol.B,1996年,73页

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