Currently,energy conservation draws wide attention in industrial manufacturing systems.In recent years,many studies have aimed at saving energy consumption in the process of manufacturing and scheduling is regarded as...Currently,energy conservation draws wide attention in industrial manufacturing systems.In recent years,many studies have aimed at saving energy consumption in the process of manufacturing and scheduling is regarded as an effective approach.This paper puts forwards a multi-objective stochastic parallel machine scheduling problem with the consideration of deteriorating and learning effects.In it,the real processing time of jobs is calculated by using their processing speed and normal processing time.To describe this problem in a mathematical way,amultiobjective stochastic programming model aiming at realizing makespan and energy consumption minimization is formulated.Furthermore,we develop a multi-objective multi-verse optimization combined with a stochastic simulation method to deal with it.In this approach,the multi-verse optimization is adopted to find favorable solutions from the huge solution domain,while the stochastic simulation method is employed to assess them.By conducting comparison experiments on test problems,it can be verified that the developed approach has better performance in coping with the considered problem,compared to two classic multi-objective evolutionary algorithms.展开更多
In the era of modern high performance computing, GPUs have been considered an excellent accelerator for general purpose data-intensive parallel applications. To achieve application speedup from GPUs, many of performan...In the era of modern high performance computing, GPUs have been considered an excellent accelerator for general purpose data-intensive parallel applications. To achieve application speedup from GPUs, many of performance-oriented optimization techniques have been proposed. However, in order to satisfy the recent trend of power and energy consumptions, power/energy-aware optimization of GPUs needs to be investigated with detailed analysis in addition to the performance-oriented optimization. In this work, in order to explore the impact of various optimization strategies on GPU performance, power and energy consumptions, we evaluate performance and power/energy consumption of a well-known application running on different commercial GPU devices with the different optimization strategies. In particular, in order to see the more generalized performance and power consumption patterns of GPU based accelerations, our evaluations are performed with three different Nvdia GPU generations(Fermi, Kepler and Maxwell architectures), various core clock frequencies and memory clock frequencies. We analyze how a GPU kernel execution is affected by optimization and what GPU architectural factors have much impact on its performance and power/energy consumption. This paper also categorizes which optimization technique primarily improves which metric(i.e., performance, power or energy efficiency). Furthermore, voltage frequency scaling(VFS) is also applied to examine the effect of changing a clock frequency on these metrics. In general, our work shows that effective GPU optimization strategies can improve the application performance significantly without increasing power and energy consumption.展开更多
A novel parallel hybrid electrical urban bus （PHEUB） configuration consisting of an extra one-way clutch and an automatic mechanical transmission （AMT） is taken as the study subject. An energy management strategy ...A novel parallel hybrid electrical urban bus （PHEUB） configuration consisting of an extra one-way clutch and an automatic mechanical transmission （AMT） is taken as the study subject. An energy management strategy combining a logic threshold approach and an instantaneous optimization algorithm is proposed for the investigated PHEUB. The objective of the energy management strategy is to achieve acceptable vehicle performance and drivability requirements while simultaneously maximizing the engine fuel consumption and maintaining the battery state of charge in its operation range at all times. Under the environment of Matlab/Simulink, a computer simulation model for the PHEUB is constructed by using the model building method combining theoretical analysis and bench test data. Simulation and experiment results for China Typical Bus Driving Schedule at Urban District （CTBDS_UD） are obtained, and the results indicate that the proposed control strategy not only controls the hybrid system efficiently but also improves the fuel economy significantly.展开更多
Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybri...Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybrid electric vehicle em-展开更多
文摘Currently,energy conservation draws wide attention in industrial manufacturing systems.In recent years,many studies have aimed at saving energy consumption in the process of manufacturing and scheduling is regarded as an effective approach.This paper puts forwards a multi-objective stochastic parallel machine scheduling problem with the consideration of deteriorating and learning effects.In it,the real processing time of jobs is calculated by using their processing speed and normal processing time.To describe this problem in a mathematical way,amultiobjective stochastic programming model aiming at realizing makespan and energy consumption minimization is formulated.Furthermore,we develop a multi-objective multi-verse optimization combined with a stochastic simulation method to deal with it.In this approach,the multi-verse optimization is adopted to find favorable solutions from the huge solution domain,while the stochastic simulation method is employed to assess them.By conducting comparison experiments on test problems,it can be verified that the developed approach has better performance in coping with the considered problem,compared to two classic multi-objective evolutionary algorithms.
基金supported by Basic Science Research Program through the National Research Foundation(2015R1D1A3A01019869),Korea
文摘In the era of modern high performance computing, GPUs have been considered an excellent accelerator for general purpose data-intensive parallel applications. To achieve application speedup from GPUs, many of performance-oriented optimization techniques have been proposed. However, in order to satisfy the recent trend of power and energy consumptions, power/energy-aware optimization of GPUs needs to be investigated with detailed analysis in addition to the performance-oriented optimization. In this work, in order to explore the impact of various optimization strategies on GPU performance, power and energy consumptions, we evaluate performance and power/energy consumption of a well-known application running on different commercial GPU devices with the different optimization strategies. In particular, in order to see the more generalized performance and power consumption patterns of GPU based accelerations, our evaluations are performed with three different Nvdia GPU generations(Fermi, Kepler and Maxwell architectures), various core clock frequencies and memory clock frequencies. We analyze how a GPU kernel execution is affected by optimization and what GPU architectural factors have much impact on its performance and power/energy consumption. This paper also categorizes which optimization technique primarily improves which metric(i.e., performance, power or energy efficiency). Furthermore, voltage frequency scaling(VFS) is also applied to examine the effect of changing a clock frequency on these metrics. In general, our work shows that effective GPU optimization strategies can improve the application performance significantly without increasing power and energy consumption.
基金Shanghai Municipal Science and Technology Commission, China (No. 033012017).
文摘A novel parallel hybrid electrical urban bus （PHEUB） configuration consisting of an extra one-way clutch and an automatic mechanical transmission （AMT） is taken as the study subject. An energy management strategy combining a logic threshold approach and an instantaneous optimization algorithm is proposed for the investigated PHEUB. The objective of the energy management strategy is to achieve acceptable vehicle performance and drivability requirements while simultaneously maximizing the engine fuel consumption and maintaining the battery state of charge in its operation range at all times. Under the environment of Matlab/Simulink, a computer simulation model for the PHEUB is constructed by using the model building method combining theoretical analysis and bench test data. Simulation and experiment results for China Typical Bus Driving Schedule at Urban District （CTBDS_UD） are obtained, and the results indicate that the proposed control strategy not only controls the hybrid system efficiently but also improves the fuel economy significantly.
文摘为提升并联式混合动力汽车(parallel hybrid electric vehicle,PHEV)的燃油经济性,针对等效燃油消耗最小控制策略(equivalent fuel consumption minimum strategy,ECMS)在不同工况下适应性差的问题,以优化整车等效燃油消耗量为目标,设计基于工况识别算法的变等效因子ECMS能量管理策略。选取3类典型工况建立支持向量机分类模型,通过递归特征消除法对样本特征进行选择,采用鲸鱼算法对支持向量机进行参数优化,使用模拟退火算法分别对3类工况的ECMS等效因子进行离线全局最优求解,并分别存储于等效因子库中,通过训练好的支持向量机分类器对目标优化工况进行工况识别,不同类型的工况片段采用不同的等效因子进行转矩分配。仿真结果显示:相比于逻辑门限能量管理策略,基于工况识别算法的变等效因子ECMS能量管理策略的电池荷电状态(state of charge,SOC)变化量减少8.67%,节油率为13.11%;相比于优化前的ECMS策略电池SOC变化量减少3.47%,节油率约为6.63%。本文提出的基于工况识别算法的变等效因子ECMS能量管理策略可以有效地减少燃油消耗量,提升PHEV的整车经济性。
基金supported by the Natural Science Foundation of Hubei Province(Grant No.2015CFB586)
文摘Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybrid electric vehicle em-
