Kodirbek Makharov

Learning Equitable and Efficient Shift Rosters with Multi-Objective Deep Reinforcement Learning and Counterfactual Fairness Constraints in Operations

Shift rostering systems often optimize cost and coverage while treating fairness as a secondary objective. This study develops a multi-objective deep reinforcement learning framework for workforce scheduling that integrates Proximal Policy Optimization with a graph neural network encoder, a Conditional Value-at-Risk te...