The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worke...The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.展开更多
To overcome the problems of natural decreases in power quality,and to eliminate wind speed fluctuation due to wind shear and tower shadow effect arising from wind turbine structural parameters,an improved prediction m...To overcome the problems of natural decreases in power quality,and to eliminate wind speed fluctuation due to wind shear and tower shadow effect arising from wind turbine structural parameters,an improved prediction model accounting for the dual effect of wind shear and tower shadow is,in this paper,built.Compared to the conventional prediction model,the proposed model contains a new constraint condition,which makes the disturbance term caused by the tower shadow effect always negative so that the prediction result is closer to the actual situation.Furthermore,wind turbine structural parameters such as hub height,rotor diameter,the diameter of the tower top,and rotor overhang on wind shear and tower shadow effect are also explored in detail.The results show that the wind shear effect became weaker with the increase in hub height.The hub height is independent of the tower shadow effect.The rotor diameter is positively correlated with the wind shear and tower shadow effect.The tower shadow effect is positively correlated with the diameter of the tower top and negatively correlated with the rotor overhang.展开更多
采用压缩和激励网络(squeeze and excitation network,SENET)构建双塔推荐模型,针对卷烟消费推荐问题进行了研究.基于长期采集的包含用户、商品和历史交互信息的数据集,使用SENET双塔推荐模型对该数据集进行建模,以预测用户的卷烟消费行...采用压缩和激励网络(squeeze and excitation network,SENET)构建双塔推荐模型,针对卷烟消费推荐问题进行了研究.基于长期采集的包含用户、商品和历史交互信息的数据集,使用SENET双塔推荐模型对该数据集进行建模,以预测用户的卷烟消费行为.实验结果表明,基于SENET双塔构架的卷烟消费推荐模型在消费者与产品间的交互信息获取上具有优势;与传统的推荐算法相比,SENET双塔推荐模型具有更好的推荐效果.展开更多
基金supported by the Natural Science Foundation of Anhui Province(Grant Number 2208085MG181)the Science Research Project of Higher Education Institutions in Anhui Province,Philosophy and Social Sciences(Grant Number 2023AH051063)the Open Fund of Key Laboratory of Anhui Higher Education Institutes(Grant Number CS2021-ZD01).
文摘The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.
基金funded by the National Natural Science Foundation of China(51866012).
文摘To overcome the problems of natural decreases in power quality,and to eliminate wind speed fluctuation due to wind shear and tower shadow effect arising from wind turbine structural parameters,an improved prediction model accounting for the dual effect of wind shear and tower shadow is,in this paper,built.Compared to the conventional prediction model,the proposed model contains a new constraint condition,which makes the disturbance term caused by the tower shadow effect always negative so that the prediction result is closer to the actual situation.Furthermore,wind turbine structural parameters such as hub height,rotor diameter,the diameter of the tower top,and rotor overhang on wind shear and tower shadow effect are also explored in detail.The results show that the wind shear effect became weaker with the increase in hub height.The hub height is independent of the tower shadow effect.The rotor diameter is positively correlated with the wind shear and tower shadow effect.The tower shadow effect is positively correlated with the diameter of the tower top and negatively correlated with the rotor overhang.
文摘采用压缩和激励网络(squeeze and excitation network,SENET)构建双塔推荐模型,针对卷烟消费推荐问题进行了研究.基于长期采集的包含用户、商品和历史交互信息的数据集,使用SENET双塔推荐模型对该数据集进行建模,以预测用户的卷烟消费行为.实验结果表明,基于SENET双塔构架的卷烟消费推荐模型在消费者与产品间的交互信息获取上具有优势;与传统的推荐算法相比,SENET双塔推荐模型具有更好的推荐效果.
