Machine Learning Applied to Logistics Decision Making: Improvements to the Soybean Seed Classification Process

Abstract

Soybean seed classification is a relevant and time-consuming process for Brazilian agribusiness cooperatives. This activity can generate queues and waiting times that directly affect logistics costs. This is the reason why it is so important to properly allocate resources, considering the most relevant factors that can influence their performance. This paper aims to present an approach to predicting the average lead time and waiting queue time for the soybean seed classification process, which supports the decision regarding the number of workers and machines to be deployed in the process. The originality of the paper relies on the applied approach, which combines discrete event simulation with machine learning algorithms in a real-world applied case. The approach comprises three steps: data collection to structure the simulation scenarios; simulation runs to generate artificial historical data; and machine learning applications to predict lead and queuing times. As a result, various scenarios using the data generated by machine learning were simulated, making it possible to choose the one that generated the best trade-off between performance, investments, and operational costs. The approach can be adapted to support the solution of different logistic-related decision-making problems that combine human and equipment resources.