Multi-objective Optimization of Maintenance, Repair and Rehabilitation Schedule considering Greenhouse Gas Emissions
Open Access
- Author:
- Chowdhury, Lamiya Farah
- Graduate Program:
- Civil Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- June 28, 2023
- Committee Members:
- S. Ilgin Guler, Thesis Advisor/Co-Advisor
Shelley Marie Stoffels, Thesis Advisor/Co-Advisor
Patrick Fox, Program Head/Chair
Xianbiao (XB) Hu, Committee Member - Keywords:
- Maintenance
Repair
and Rehabilitation (MRR)
Greenhouse Gas Emission
work zone delay
Bi-level optimization
Multi-objective optimization
Population based incremental learning
Link transmission model - Abstract:
- Pavement maintenance, repair, and rehabilitation (MRR) planning is a complex decision-making process, which traditionally focuses on maximizing pavement condition or minimizing agency costs. However, objectives such as minimizing road user costs and greenhouse gas emissions are often overlooked. Additionally, lane closures in construction work zones can significantly impact traffic flow and emissions throughout the network. To address these challenges, this thesis aimed to optimize the MRR schedule of a network by minimizing agency costs, user costs, and emission costs while considering the impact of work zones on travel delays and detours in a dynamic traffic environment. The study employed a bi-level optimization framework, using the population-based incremental learning (PBIL) algorithm to generate MRR schedules while considering constraints on the number of activities. The Link Transmission Model (LTM) was utilized to simulate the effects of MRR actions on specific links, accounting for traffic flow dynamics. Different scenarios were simulated on an urban network. The results demonstrated that optimizing schedules with emissions led to the lowest overall costs, despite slightly higher user costs. The approach resulted in significant savings in agency expenses and emissions while simultaneously maintaining pavement conditions. The optimized scenarios outperformed a prescribed scenario where MRR actions were implemented based only on pavement conditions. Furthermore, a Pareto front revealed the trade-off between emissions and user-agency costs. More expense on the agency and user end results in lower emissions that eventually lead to diminishing returns of emission reduction after a certain point. The proposed methodology can be applied to the MRR planning of larger networks, providing valuable insights for agencies in improving overall network performance and sustainability.