标题： 考虑情绪压力下绩效演化的呼叫中心员工排班 显示英文标题

标题： Call-Center Staff Scheduling Considering Performance Evolution under Emotional Stress

摘要： 情绪压力通常对员工的工作表现有显著影响，但这种影响在现有的员工排班方法中常常被忽视。 我们研究了一个电话中心员工排班问题，该问题考虑了在情绪压力下员工工作表现的变化。 首先，我们提出一个由情绪压力驱动的模型，该模型不仅基于技能水平，还基于情绪状态来估计电话中心员工的工作表现。 在该模型的基础上，我们制定了一个结合短期和长期的电话中心员工排班问题，旨在最大化客户服务级别，该级别取决于员工的工作表现。 然后，我们提出一种结合全局变异和由深度强化学习辅助的邻域搜索的遗传优化算法，以高效解决这个问题。 在银行电话中心员工排班的真实问题实例上的实验结果表明，所提出的方法在性能上优于选定的流行员工排班方法。 通过明确建模和引入情绪压力，我们的方法反映了在员工排班中对人类行为更现实的理解和利用。 显示英文摘要

摘要： Emotional stress often has a significant effect on the working performance of staff, but this effect is commonly neglected in existing staff scheduling methods. We study a call-center staff scheduling problem, which considers the evolution of work performance of staff under emotional stress. First, we present an emotional stress driven model that estimates the working performance of call-center employees based on not only skill levels but also emotional states. On the basis of the model, we formulate a combined short-term and long-term call-center staff scheduling problem aiming at maximizing the customer service level, which depends on the working performance of employees. We then propose a memetic optimization algorithm combining global mutation and neighborhood search assisted by deep reinforcement learning to efficiently solve this problem. Experimental results on real-world problem instances of bank call-center staff scheduling demonstrate the performance advantages of the proposed method over selected popular staff scheduling methods. By explicitly modeling and incorporating emotional stress, our method reflects a more realistic understanding and utilization of human behavior in staff scheduling.