Tax-Scheduler: An interactive visualization system for staff shifting and scheduling at tax authorities

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...

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Bibliographic Details
Published in:Visual informatics (Online) Vol. 7; no. 2; pp. 30 - 40
Main Authors: Yuan, Linping, Li, Boyu, Li, Siqi, Wong, Kam Kwai, Zhang, Rong, Qu, Huamin
Format: Journal Article
Language:English
Published: Elsevier B.V 01.06.2023
Elsevier
Subjects:
Genetic algorithm
Staff scheduling
Tax service
Time series prediction
Time series prediction
Staff scheduling
Tax service
Genetic algorithm
ISSN:2468-502X, 2468-502X
Online Access:Get full text
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Summary: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.
ISSN:2468-502X
2468-502X
DOI:10.1016/j.visinf.2023.02.001