Publication Date

Spring 2019

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Sami Khuri

Second Advisor

Natalia Khuri

Third Advisor

Philip Heller


Capacitated Vehicle Routing Problem, Benchmarking, OR-Tools


The Vehicle Routing Problem (VRP) originated in the 1950s when algorithms and mathematical approaches were applied to find solutions for routing vehicles. Since then, there has been extensive research in the field of VRPs to solve real-life problems. The process of generating an optimal routing schedule for a VRP is complex due to two reasons. First, VRP is considered to be an NP-Hard problem. Second, there are several constraints involved, such as the number of available vehicles, the vehicle capacities, time-windows for pickup or delivery etc.

The main goal for this project was to compare different optimization algorithms for solving Capacitated Vehicle Routing Problems (CVRP). The three specific aims for this project were to (1) survey existing research and identify suitable optimization algorithms for CVRP and (2) implement a work-flow in the Python programming lan- guage, to evaluate their performance, (3) perform different computational experiments on existing CVRP benchmarks.

Experiments were conducted by leveraging Google’s OR-Tools library on the well-known benchmarks. Different strategies were evaluated to see if there exists a solution or a better solution than the best-known solutions for these benchmarks. The results show that almost 60% of the problems in the benchmarks have a better solution than the current best-known solution. The second finding of this project is that there is not one strategy which can provide the best solution for all types of CVRPs.