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.
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).