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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.01069 |
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| _version_ | 1866916639356747776 |
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| author | Eissa, Kareem Prasad, Rayal Mohan, Sarith Kapoor, Ankur Comaniciu, Dorin Singh, Vivek |
| author_facet | Eissa, Kareem Prasad, Rayal Mohan, Sarith Kapoor, Ankur Comaniciu, Dorin Singh, Vivek |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01069 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization Eissa, Kareem Prasad, Rayal Mohan, Sarith Kapoor, Ankur Comaniciu, Dorin Singh, Vivek Artificial Intelligence Multiagent Systems 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. |
| title | Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2503.01069 |