  Efficient order fulfillment is vital in the agricultural industry,
particularly due to the seasonal nature of seed supply chains. This paper
addresses the challenge of optimizing seed orders fulfillment in a centralized
warehouse where orders are processed in waves, taking into account the
unpredictable arrival of seed stocks and strict order deadlines. We model the
wave scheduling problem as a Markov decision process and propose an adaptive
hybrid tree search algorithm that combines Monte Carlo tree search with
domain-specific knowledge to efficiently navigate the complex, dynamic
environment of seed distribution. By leveraging historical data and stochastic
modeling, our method enables forecast-informed scheduling decisions that
balance immediate requirements with long-term operational efficiency. The key
idea is that we can augment Monte Carlo tree search algorithm with
problem-specific side information that dynamically reduces the number of
candidate actions at each decision step to handle the large state and action
spaces that render traditional solution methods computationally intractable.
Extensive simulations with realistic parameters-including a diverse range of
products, a high volume of orders, and authentic seasonal durations-demonstrate
that the proposed approach significantly outperforms existing industry standard
methods.
