用多智能体强化学习优化人力调度，兼顾短期派工与长期人才管理。
Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization
- 构建模块化模拟器，统一建模派工、人员管理与定位三者关系
- 支持可配置的动态场景，涵盖随机性与非平稳性变化
- 提供基准方法，便于算法对比与消融实验
人力资源优化在组织高效运作中至关重要，决策需跨越多个管理与时间尺度。例如，在即时服务请求调度的同时，还需规划具备不同技能的人才招聘，形成高度动态的优化问题。现有研究多聚焦于资源分配、设施选址等子问题，通常采用局部搜索等启发式方法，近年也引入深度强化学习。然而，这些方法难以准确反映真实场景中各子问题并非完全独立的情况。本文旨在填补这一空白，设计一个统一的人力资源优化模拟器。该模拟器支持多智能体强化学习方法的开发，重点关注人员派工、人力管理与人员定位三个相互关联的方面。通过可配置参数，模拟器能探索具有不同随机性与非平稳性的动态场景。为便于基准测试与消融分析，还集成了启发式与强化学习基线方法。
原文摘要 · Abstract (English)
Workforce optimization plays a crucial role in efficient organizational operations where decision-making may span several different administrative and time scales. For instance, dispatching personnel to immediate service requests while managing talent acquisition with various expertise sets up a highly dynamic optimization problem. Existing work focuses on specific sub-problems such as resource allocation and facility location, which are solved with heuristics like local-search and, more recently, deep reinforcement learning. However, these may not accurately represent real-world scenarios where such sub-problems are not fully independent. Our aim is to fill this gap by creating a simulator that models a unified workforce optimization problem. Specifically, we designed a modular simulator to support the development of reinforcement learning methods for integrated workforce optimization problems. We focus on three interdependent aspects: personnel dispatch, workforce management, and personnel positioning. The simulator provides configurable parameterizations to help explore dynamic scenarios with varying levels of stochasticity and non-stationarity. To facilitate benchmarking and ablation studies, we also include heuristic and RL baselines for the above mentioned aspects.
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