A Generalizable MARL-LP Approach for Scheduling in Logistics | Towards Data Science

Towards Data Science

by Alexander Levin

February 26, 2026

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The article introduces a novel approach to logistics scheduling using Multi-Agent Reinforcement Learning with Linear Programming (MARL-LP), aiming to address inefficiencies in the logistics industry. Traditional methods often rely on static schedules and manual processes, leading to suboptimal vehicle utilization, which can result in millions of dollars lost annually for large companies. The proposed solution focuses on creating a generalizable model that can adapt to unseen scenarios without retraining, leveraging reinforcement learning's ability to generalize decision-making. The project was initiated by the author in a fast-growing logistics company facing scalability challenges with over 100 line-haul terminals. Historical shipment data revealed significant inefficiencies, particularly in vehicle utilization, which directly impacts operational costs. The goal was to develop a scheduling system that dynamically optimizes routes and loads, maximizing efficiency while minimizing delays and costs. The MARL-LP approach combines reinforcement learning with linear programming to make real-time decisions. By training an agent to handle various scenarios, the model can generalize solutions across different conditions, ensuring optimal performance even in dynamic environments. This method prioritizes practical outcomes over perfection, focusing on meeting service level agreements (SLAs) while improving efficiency. The significance of this approach lies in its potential to revolutionize logistics operations by reducing reliance on rigid schedules and manual interventions. For AI enthusiasts, the integration of reinforcement learning demonstrates a shift towards more adaptable and scalable solutions. The model's ability to generalize across diverse

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Originally published on Towards Data Science on 2/26/2026