Computer Science ›› 2025, Vol. 52 ›› Issue (10): 33-49.doi: 10.11896/jsjkx.250500086

• Digital Intelligence Enabling FinTech Frontiers • Previous Articles     Next Articles

Integration of Machine Learning Prediction and Water Wave Optimization for Online Customer Service Representatives Scheduling in Bank Contact Centers

LU Xueqin1, XIE Xicheng2 , TANG Yan3, CHEN Shikun3, LIU Yangguang3   

  1. 1 College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
    2 School of Information Science and Technology,Hangzhou Normal University,Hangzhou 311121,China
    3 College of Finance & Information,Ningbo University of Finance & Economics,Ningbo,Zhejiang 315175,China
  • Received:2025-05-21 Revised:2025-07-20 Online:2025-10-15 Published:2025-10-14
  • About author:LU Xueqin,born in 1980,Ph.D candidate,associate professor,is a member of CCF(No.88689G).Her main research interest includes intelligent optimization algorithms and applications.
    LIU Yangguang,born in 1975,Ph.D,professor,master's supervisor,Zhejiang Province 151 Talent Program awardee,is a member of CCF(No.20163M).His main research interests include multi-scale data intelligence,AI and applied optimization.
  • Supported by:
    Zhejiang Provincial Basic Public Welfare Research Program(LGF22G020002),Zhejiang Provincial Department of Education College Visiting Scholars Professional Development Program(FX2023059) and Ningbo Natural Science Foundation(2023J060).

Abstract: Online customer service representative scheduling is a crucial component of operational management in a bank's contact center.Optimized staffing and shift scheduling of customer service representatives ensure prompt customer responses,which significantly improves service efficiency and customer satisfaction.However,factors such as the uncertainty of customer request arrivals and variations in customer service skill levels make the online service representatives scheduling problem complex.Considering the practical challenges such as customer service representative skill levels,diversity of customer types,and matching requirements,this study proposes a mixed-integer linear programming model with the optimization objectives of minimizing custo-mer waiting time and operational costs,and also presents a hybrid machine learning and water wave optimization(WWO) method to effectively solve the online customer service scheduling problem.In this method,a forecasting model based on long short-term memory neural networks is employed to predict customer arrival volumes,and this model can capture both time series dependencies and the influence of external factors.For the mixed-integer programming model of representative scheduling,WWO combining reinforcement learning Q-learning is used for efficient solution.This method leverages Q-learning to adaptively optimize neighborhood selection,enhancing the efficiency and quality of solutions.Based on real data from the contact center of Zhejiang Tailong Bank's Ningbo branch,the experimental results show that the proposed method significantly outperforms comparative methods in terms of operational cost control.Further,sensitivity analysis reveals that when forecast accuracy drops below 90%,customer arrival uncertainty markedly increases scheduling costs and customer waiting times.Conversely,when accuracy reaches or exceeds 90%,performance improvements stabilize.These findings highlight the critical role of high-precision forecasting in effective scheduling and provide theoretical insights and practical guidance for balancing forecasting model complexity with scheduling efficiency in real-world applications.

Key words: Online customer service representatives scheduling,LSTM neural network,Reinforcement learning,Water wave optimization

CLC Number: 

  • TP181
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