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.
View on arXiv@article{eissa2025_2503.01069,
title={ Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization },
author={ Kareem Eissa and Rayal Prasad and Sarith Mohan and Ankur Kapoor and Dorin Comaniciu and Vivek Singh },
journal={arXiv preprint arXiv:2503.01069},
year={ 2025 }
}