Abstract
The Capacitated Vehicle Routing Problem (CVRP) is an important combinatorial optimization problem in which a fleet of vehicles must be routed to a set of customers. Many exact and heuristic methods have been proposed to solve this problem. Recently, efficient machine learning algorithms have been developed for solving the VRP. A major component of these algorithms is the Attention Mechanism which enables the vehicle to make more informed decisions on which customer to serve next. This paper compares various topologies for implementing such attention mechanisms when solving VRPs. Using simulations, it is found that Single-Head Attention works better with larger node instances, whereas Multi-Head Attention is superior for smaller node instances.
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Krishna Munjuluri Vamsi, V.S., Telukuntla, Y.R., Kumar, P.S., Gutjahr, G. (2023). Comparison of Attention Mechanisms in Machine Learning Models for Vehicle Routing Problems. In: Singh, P., Singh, D., Tiwari, V., Misra, S. (eds) Machine Learning and Computational Intelligence Techniques for Data Engineering. MISP 2022. Lecture Notes in Electrical Engineering, vol 998. Springer, Singapore. https://doi.org/10.1007/978-981-99-0047-3_53
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DOI: https://doi.org/10.1007/978-981-99-0047-3_53
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