Xiaosong Ding
ABSTRACT
This paper proposes a deep reinforcement learning (DRL)-based framework for a novel variant of technician routing and scheduling problem. First, we model the problem as a Markov decision process (MDP). Then, we build the policy network with attention-based Graph Neural Network (GNN), autoregressive decoding, and sampling graph search technique. Finally, reinforcement learning (RL) is adopted in policy learning to overcome the difficulty of creating labelled datasets. Extensive computational results validate the efficacy of the framework and managerial insights are revealed for decision makers in real world practice.
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Index Terms
- Deep Reinforcement Learning for Solving Multi-period Routing Problem with Binary Driver-customer Familiarity
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