AI/ML BASED DECISION SUPPORT SYSTEM FOR OPTIMIZING RAKE FORMATION STRATEGIES FOR SAIL
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Abstract
Efficient logistics planning is essential in large-scale industrial supply chains such as steel manufacturing. Rake formation, which involves assembling railway wagons for transporting bulk materials, is a critical activity that directly impacts cost and delivery performance. Traditional approaches rely on manual coordination across multiple operational parameters, leading to inefficiencies such as delays, underutilization of wagons, and increased logistics costs.This paper presents an Artificial Intelligence and Machine Learning (AI/ML)-based Decision Support System (DSS) to optimize rake formation strategies. The proposed system integrates demand prediction with optimization techniques to generate cost-efficient and resource-optimized rake plans. A Mixed Integer Programming (MIP) model is used to minimize total logistics cost under operational constraints. The system is validated using simulated datasets, demonstrating improved rake utilization and reduced delays. The approach provides a scalable solution for modern logistics optimization
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