Abstract
During this era of digital transformation, applications in Logistics and Supply Chain based on Operations Research (OR) and Machine Learning (ML) techniques has catalyzed the development of innovative approaches that redefine industry standards. Particularly, transportation dispatching – a critical aspect of logistics – has seen significant advancements through the application of Reinforcement Learning (RL), achieving notable enhancements in operational efficiency. Despite these advancements, current research predominantly focuses on ride-sharing and on-demand delivery, with limited attention to fair dispatch practices by the workers point of view. This research addresses this gap by proposing a fair truck dispatch system designed to equitably distribute loads from a shipping company to carriers without compromise service levels. Utilizing real-world data characterized by uncertain demand and a dynamic fleet size our empirical results demonstrate the effectiveness of the proposed dispatch strategy, confirming its capability to ensure equitable load distribution across different operational scenarios reaching 86% on average allocation over carriers, compared to their available capacity.
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Acknowledgments
The authors wish to express their gratitude to Leandro Galinari, Marlon Lacerda, Talita Bezerra, and Victor Castro of ArcelorMittal Brasil for providing the dataset and their insights into the business processes.
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Tavares, M.A.A., Veloso, A.A. (2024). Using Deep Reinforcement Learning to Dispatch Loads to Carriers Under Uncertain Demand and Dynamic Fleet Size. In: Garrido, A., Paternina-Arboleda, C.D., Voß, S. (eds) Computational Logistics. ICCL 2024. Lecture Notes in Computer Science, vol 15168. Springer, Cham. https://doi.org/10.1007/978-3-031-71993-6_9
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