用强化学习优化客服中心派单，PPO表现最佳
Optimising Call Centre Operations using Reinforcement Learning: Value Iteration versus Proximal Policy Optimisation
- 对比基于模型的值迭代与无模型的PPO算法
- PPO在1000轮测试中实现最低客户等待和空闲时间
- 适合关注智能客服调度与强化学习应用的研究者
本文研究强化学习在客服中心呼叫路由优化中的应用，目标是降低客户等待时间和员工空闲时间。比较了两种方法：基于模型的值迭代（VI），需已知系统动态；以及无模型的近端策略优化（PPO），通过经验学习。模型基于技能匹配路由（SBR）框架，假设客户到达为泊松过程，服务与放弃时间呈指数分布。采用结合离散事件模拟（DES）与OpenAI Gym的仿真环境进行评估。对随机、VI和PPO策略进行测试，经1,000轮仿真后，PPO始终获得最高奖励，同时实现最低客户等待时间和员工空闲时间，尽管训练时间更长。
原文摘要 · Abstract (English)
This paper investigates the application of Reinforcement Learning (RL) to optimise call routing in call centres to minimise client waiting time and staff idle time. Two methods are compared: a model-based approach using Value Iteration (VI) under known system dynamics, and a model-free approach using Proximal Policy Optimisation (PPO) that learns from experience. For the model-based approach, a theoretical model is used, while a simulation model combining Discrete Event Simulation (DES) with the OpenAI Gym environment is developed for model-free learning. Both models frame the problem as a Markov Decision Process (MDP) within a Skills-Based Routing (SBR) framework, with Poisson client arrivals and exponentially distributed service and abandonment times. For policy evaluation, random, VI, and PPO policies are evaluated using the simulation model. After 1,000 test episodes, PPO consistently achives the highest rewards, along with the lowest client waiting time and staff idle time, despite requiring longer training time.
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