Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adja...Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.展开更多
Due to the fourth revolution experiencing,referred to as Industry 4.0,many production firms are devoted to integrating new technological tools to their manufacturing process.One of them,is rescheduling the tasks on th...Due to the fourth revolution experiencing,referred to as Industry 4.0,many production firms are devoted to integrating new technological tools to their manufacturing process.One of them,is rescheduling the tasks on the machines responding to disruptions.While,for static scheduling,the efficiency criteria measure the performance of scheduling systems,in dynamic environments,the stability criteria are also used to assess the impact of jobs deviation.In this paper,a new performance measure is investigated for a flowshop rescheduling problem.This one considers simultaneously the total weighted waiting time as the efficiency criterion,and the total weighted completion time deviation as the stability criterion.This fusion could be a very helpful and significant measure for real life industrial systems.Two disruption types are considered:jobs arrival and jobs cancellation.Thus,a Mixed Integer Linear Programming(MILP)model is developed,as well as an iterative predictive-reactive strategy for dealing with the online part.At last,two heuristic methods are proposed and discussed,in terms of solution quality and computing time.展开更多
基金supported by National Natural Science Foundation of China(52222215, 52272420, 52072051)。
文摘Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.
文摘Due to the fourth revolution experiencing,referred to as Industry 4.0,many production firms are devoted to integrating new technological tools to their manufacturing process.One of them,is rescheduling the tasks on the machines responding to disruptions.While,for static scheduling,the efficiency criteria measure the performance of scheduling systems,in dynamic environments,the stability criteria are also used to assess the impact of jobs deviation.In this paper,a new performance measure is investigated for a flowshop rescheduling problem.This one considers simultaneously the total weighted waiting time as the efficiency criterion,and the total weighted completion time deviation as the stability criterion.This fusion could be a very helpful and significant measure for real life industrial systems.Two disruption types are considered:jobs arrival and jobs cancellation.Thus,a Mixed Integer Linear Programming(MILP)model is developed,as well as an iterative predictive-reactive strategy for dealing with the online part.At last,two heuristic methods are proposed and discussed,in terms of solution quality and computing time.
