Optimizing Pollution Routing Problem
Document Type
Oral Presentation
Campus where you would like to present
Ellensburg
Event Website
https://digitalcommons.cwu.edu/source
Start Date
18-5-2020
Abstract
Pollution has been a big problem all over 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 Random Sequence Generator (RSG). 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, Particle Swarm Optimization (PSO) 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 two.
Recommended Citation
Dewan, Shivika, "Optimizing Pollution Routing Problem" (2020). Symposium Of University Research and Creative Expression (SOURCE). 45.
https://digitalcommons.cwu.edu/source/2020/COTS/45
Department/Program
Computer Sciences
Additional Mentoring Department
https://cwu.studentopportunitycenter.com/2020/04/optimizing-pollution-routing-problem/
Optimizing Pollution Routing Problem
Ellensburg
Pollution has been a big problem all over 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 Random Sequence Generator (RSG). 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, Particle Swarm Optimization (PSO) 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 two.
https://digitalcommons.cwu.edu/source/2020/COTS/45
Faculty Mentor(s)
Donald Davendra