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.

翻译：情绪压力常对员工工作绩效产生显著影响，但现有人员调度方法普遍忽视这一因素。本文研究一种考虑情绪压力下员工工作绩效演化的呼叫中心人员调度问题。首先，我们提出一种情绪压力驱动模型，该模型不仅依据技能水平，还结合情绪状态来评估呼叫中心员工的工作绩效。基于该模型，我们构建了一个兼顾短期与长期的呼叫中心人员调度组合优化问题，其目标在于最大化取决于员工工作绩效的客户服务水平。随后，我们提出一种结合全局变异与邻域搜索的模因优化算法，并辅以深度强化学习技术，以高效求解该问题。在银行呼叫中心人员调度的实际案例上的实验结果表明，相较于所选的主流人员调度方法，本文所提方法展现出显著的性能优势。通过显式建模并融入情绪压力因素，我们的方法在人员调度中体现了对人类行为更符合实际的理解与利用。