Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization

• URL: http://arxiv.org/abs/2503.01069v1
• Date: Mon, 03 Mar 2025 00:16:47 GMT
• Title: Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization
• Authors: Kareem Eissa, Rayal Prasad, Sarith Mohan, Ankur Kapoor, Dorin Comaniciu, Vivek Singh,
• Abstract summary: Our aim is to create a simulator that models a unified workforce optimization problem.<n> Specifically, we designed a modular simulator to support the development of reinforcement learning methods.<n>The simulator provides parameterizations to help explore dynamic scenarios with varying levels of ablationity and non-stationarity.
• Score: 2.865067924658368
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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