Optimizing Agricultural Order Fulfillment Systems: A Hybrid Tree Search Approach

TL;DR

This paper presents a hybrid tree search algorithm combining Monte Carlo methods and domain knowledge to optimize seed order fulfillment in agriculture, improving efficiency in complex, dynamic environments.

Contribution

It introduces an adaptive hybrid tree search method that incorporates domain-specific information to effectively solve the complex seed fulfillment scheduling problem.

Findings

Significantly outperforms existing industry methods in simulations.

Effectively handles large state and action spaces.

Improves scheduling efficiency in seasonal seed supply chains.

Abstract

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.