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

Abstract

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.