The paper aims to schedule check-in staff with hierarchical skills as well as day and night shifts in weekly rotation.That shift ensures staff work at day in a week and at night for the next week.The existing approach...The paper aims to schedule check-in staff with hierarchical skills as well as day and night shifts in weekly rotation.That shift ensures staff work at day in a week and at night for the next week.The existing approaches do not deal with the shift constraint.To address this,the proposed algorithm firstly guarantees the day and night shifts by designing a data copy tactic,and then introduces two algorithms to generate staff assignment in a polynomial time.The first algorithm is to yield an initial solution efficiently,whereas the second incrementally updates that solution to cut off working hours.The key idea of the two algorithms is to utilize a block Gibbs sampling with replacement to simultaneously exchange multiple staff assignment.Experimental results indicate that the proposed algorithm reduces at least 15.6 total working hours than the baselines.展开更多
Given a large number of applications and complex processing procedures,how to efficiently shift and schedule tax officers to provide good services to taxpayers is now receiving more attention from tax authorities.The ...Given a large number of applications and complex processing procedures,how to efficiently shift and schedule tax officers to provide good services to taxpayers is now receiving more attention from tax authorities.The availability of historical application data makes it possible for tax managers to shift and schedule staff with data support,but it is unclear how to properly leverage the historical data.To investigate the problem,this study adopts a user-centered design approach.We first collect user requirements by conducting interviews with tax managers and characterize their requirements of shifting and scheduling into time series prediction and resource scheduling problems.Then,we propose Tax-Scheduler,an interactive visualization system with a time-series prediction algorithm and genetic algorithm to support staff shifting and scheduling in the tax scenarios.To evaluate the effectiveness of the system and understand how non-technical tax managers react to the system with advanced algorithms and visualizations,we conduct user interviews with tax managers and distill several implications for future system design.展开更多
基金the Natural Science Foundation of Tianjin(No.18JCYBJC85100)The Civil Aviation Key Technologies R&D Program of China(No.MHRD20140105)+1 种基金the Ministry of Education in China(MOE)Project of Humanities and Social Sciences(No.19YJA630046)the Open Project from Key Laboratory of Artificial Intelligence for Airlines,CAAC.
文摘The paper aims to schedule check-in staff with hierarchical skills as well as day and night shifts in weekly rotation.That shift ensures staff work at day in a week and at night for the next week.The existing approaches do not deal with the shift constraint.To address this,the proposed algorithm firstly guarantees the day and night shifts by designing a data copy tactic,and then introduces two algorithms to generate staff assignment in a polynomial time.The first algorithm is to yield an initial solution efficiently,whereas the second incrementally updates that solution to cut off working hours.The key idea of the two algorithms is to utilize a block Gibbs sampling with replacement to simultaneously exchange multiple staff assignment.Experimental results indicate that the proposed algorithm reduces at least 15.6 total working hours than the baselines.
基金This research is supported by the grant FSUST19-CWB09 under the Foshan-HKUST Projects.
文摘Given a large number of applications and complex processing procedures,how to efficiently shift and schedule tax officers to provide good services to taxpayers is now receiving more attention from tax authorities.The availability of historical application data makes it possible for tax managers to shift and schedule staff with data support,but it is unclear how to properly leverage the historical data.To investigate the problem,this study adopts a user-centered design approach.We first collect user requirements by conducting interviews with tax managers and characterize their requirements of shifting and scheduling into time series prediction and resource scheduling problems.Then,we propose Tax-Scheduler,an interactive visualization system with a time-series prediction algorithm and genetic algorithm to support staff shifting and scheduling in the tax scenarios.To evaluate the effectiveness of the system and understand how non-technical tax managers react to the system with advanced algorithms and visualizations,we conduct user interviews with tax managers and distill several implications for future system design.
