Smart selective navigator (SSN): enhancing urban winter road maintenance through optimized arc routing with hard turn restrictions

Abstract This paper introduces a novel heuristic method, the smart selective navigator (SSN), for addressing arc routing problems (ARPs) with a focus on integrating hard turn restrictions in urban winter operations. Addressing a significant gap in existing ARP methodologies, SSN seamlessly incorporates common side constraints, such as vehicle characteristics and road priorities, while strictly adhering to turn restrictions. Mathematically, the approach involves representing urban road networks as directed multigraphs. SSN's effectiveness was demonstrated through a case study on winter road maintenance in the City of Oshawa, which showed improved operation times. This study not only fills a crucial research gap in ARP but also offers a versatile solution applicable to various urban routing challenges, with potential applications extending beyond winter operations. Future research directions include exploring dynamic weighting models further and replacing classical optimization methods with machine learning for real‐time route generation.