Optimizing Rural Liquid Collection Routes: A Constraint-Aware Hybrid Framework Using OR-Tools

Abstract

This paper presents a hybrid optimization framework for the collection of rural liquid products that combines constraint programming with operational routing heuristics. Addressing real-world challenges including vehicle accessibility, electrical compatibility, product segregation, and variable collection frequencies, our system dynamically allocates a heterogeneous fleet to maximize operational efficiency. The proposed solution leverages Google OR-Tools with customized constraint modeling to achieve 93.6% average fill rates while ensuring 100% feasibility across all operational constraints. Field testing demonstrated complete coverage of the point of interest, elimination of manual route revisions, and significant improvements in planning efficiency. The framework includes practical deployment features such as automated reporting and interactive visualization, offering a robust solution for resource-sensitive supply chains with complex operational rules.