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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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作者 si-xiao gao Hui Liu Jun Ota 《Advances in Manufacturing》 SCIE EI CAS CSCD 2024年第2期227-251,共25页
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 requiremen... 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. 展开更多
关键词 Energy-efficient allocation Multi-objective optimization Energy efficiency Energy consumption Machine learning
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