Optimizing Pollution Routing Problem

<p>Pollution is a major environmental issue around the world. Despite the growing use and impact of commercial vehicles, recent research has been conducted with minimizing pollution as the primary objective to be reduced. The objective of this project is to implement different optimization algorithms to solve this problem. A basic model is created using the Vehicle Routing Problem (VRP) which is further extended to the Pollution Routing Problem (PRP). The basic model is updated using a Monte Carlo Algorithm (MCA). The data set contains 180 data files with a combination of 10, 15, 20, 25, 50, 75, 100, 150, and 200 groups of cities. The optimizing techniques applied are the Discrete Differential Evolution (DDE) and, Discrete Particle Swarm Optimization (DPSO) with a Python Tkinter frontend. The objectives to be optimized are the fuel consumption rate and distance traveled and a statistical comparison is done between the different algorithm to compare effectiveness.</p>

Name thesis:degree_name

Master of Science (MS)

Discipline thesis:degree_discipline

Computational Science

Year dc:date.available

2020

Author dc:creator

• Dewan, Shivika

Contributors dc:contributor

• Donald Davendra
• Razvan Andonie
• Szilárd Vajda

• Optimization
• Pollution Routing Problem
• MCA
• DDE
• DPSO
• Tkinter Python
• Environmental Health and Protection
• Oil, Gas, and Energy
• Other Computer Sciences

Repository record dc:identifier

https://digitalcommons.cwu.edu/etd/1353

OAI identifier oai:identifier

oai:digitalcommons.cwu.edu:etd-2377