Multi-objective energy-efficient hybrid flow shop scheduling using Q-learning and GVNS driven NSGA-II

An Adaptive Two-Class Teaching-Learning-Based Optimization for Energy-Efficient Hybrid Flow Shop Scheduling Problems with Additional Resources

“…The redundancy of the machines used may enable dealing with jobs flexibly and avoid bottlenecks [2]. In recent decades, HFSPs have been extensively investigated and a number of results have been obtained [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17].…”

“…Wang et al [12] considered a fuzzy EHFSP with variable machine speed and proposed use of the NSGA-II algorithm to minimize the fuzzy makespan and total fuzzy energy consumption. Li et al [13] proposed use of the NSGA-II algorithm combined with Q-learning and general variable neighborhood search. Wang et al [14] presented an improved multi-objective firefly algorithm.…”

Collaborative optimization model of blast furnace raw materials and operating parameters based on intelligent calculation

“…Yang et al established a multi-objective establish a matching relationship between raw materials and process parameters. In other fields, scholars usually use machine learning methods to find the fitting relationship between parameters and optimize the calculation by optimization algorithms 17),18), 19) . In a similar application in the steel sector, the, Li Zhuangnian et al 20) used six machine learning algorithms, namely, support vector machine, random forest, gradient boosting tree, XGBoost, LightGBM, and artificial neural network, to predict the coke ratio and permeability of the blast furnace, and on the basis of which they used the NSGA-II genetic algorithm to carry out a multi-objective optimization analysis of the blast furnace parameters, and the Pareto optimal solution was obtained; the blast furnace operator can obtain the optimal solution according to this Based on the multi-objective 5…”