Document Type
Thesis
Date of Degree Completion
Summer 2024
Degree Name
Master of Science (MS)
Department
Computational Science
Committee Chair
Dr. Donald Davendra
Second Committee Member
Dr. Szilard VAJDA
Third Committee Member
Dr. Razvan Andonie
Abstract
The development of electric vehicles is currently considered one of the most innovative areas in manufacturing. Largely driven by the desire to reduce greenhouse emissions, electric vehicles are seen as a viable alternative to internal combustion engine cars. Starting from consumer cars, a dedicated effort is being made to translate this into commercial vehicles for freight and delivery. This research introduces a novel adaptive Nawaz, Enscore, Ham (NEH) algorithm with constrained nearest neighbor subtour (NEH-NN). This algorithm is tested on the standard benchmark problems in literature and used as a seed solution for the Genetic Algorithm (GA). The performance and runtime of the algorithms are enhanced with the usage of CUDA to incorporate a High Performance Computing approach to computational optimization. From the experiments, the NEH seeded GA significantly improves on the GA, especially for the large-sized problem instances. CUDA further enhances the solution quality and execution time, making it a crucial component of this research.
Recommended Citation
Struthers, Andrew, "Adaptive NEH with Constrained Nearest Neighbor Subtours for the Electric Vehicle Routing Problem with Time Windows" (2024). All Master's Theses. 1967.
https://digitalcommons.cwu.edu/etd/1967
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