Can RL optimize long-term service workforce?
Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization
March 4, 2025
https://arxiv.org/pdf/2503.01069This paper introduces a modular simulator for workforce optimization using multi-agent reinforcement learning (MARL). It addresses the problem of coordinating personnel, managing workforce size and expertise, and strategically positioning staff to maximize efficiency and minimize downtime in service-oriented operations. Relevant to LLM-based multi-agent systems, the research demonstrates:
- Integrated problem solving: MARL tackles interconnected aspects of workforce optimization jointly, rather than in isolation, leading to better overall performance. This parallels the potential of LLMs in multi-agent systems to handle complex, interdependent tasks.
- Long-term planning: The simulator considers long-term implications of decisions, aligning with the ability of LLMs to reason over extended periods.
- Dynamic and non-stationary environments: The simulator handles dynamic changes in demand and resource availability, reflecting the real-world scenarios where LLM-based agents would operate.
- Discrete Event Simulation (DES): The simulator's use of DES offers granular control and realistic modeling of complex workflows, relevant to simulating intricate multi-agent interactions mediated by LLMs.
- Heuristic baselines: The included heuristics provide a benchmark for evaluating LLM-based agent performance.
- Open-source simulation environment: This facilitates further research and development of LLM-based MARL solutions for similar problems.