Transportation infrastructure assets deteriorate over time due to natural hazards, heavy traffic, and aging, increasing their risk of failure. National transportation agencies must strategically invest in maintenance to avoid significant social and economic impacts. We address the infrastructure maintenance planning problem, in which a maintenance plan must be designed for each asset within a budget limit to maximize the weighted average asset condition over a planning horizon. We derive a knapsack-type mathematical formulation and propose an exact column enumeration algorithm to solve it. First, a column-and-cut generation algorithm computes a (dual) upper bound on the optimal value. The master problem selects a maintenance plan for each asset and is strengthened with extended q-cover inequalities. By representing maintenance plans as paths over a directed acyclic multigraph that captures asset deterioration and maintenance decisions, the pricing problems unveil feasible plans through a specialized labeling algorithm. Second, a relaxation-enforced neighborhood search finds a (primal) lower bound. Finally, using these bounds, we enumerate sufficient columns to find an optimal solution via a commercial MILP solver. Computational results on generated instances spanning a 10-year planning horizon demonstrate that our algorithm delivers optimal solutions for instances with up to 50 assets and near-optimal solutions (gap < 0.18%) for instances with up to 100 assets within five hours.

This study demonstrates that advanced optimization modeling and algorithm design can directly improve how society invests in and preserves critical infrastructure, ensuring safer and more reliable systems for the future. You can read the full article at the following link: https://www.sciencedirect.com/science/article/abs/pii/S0305054825002758?via%3Dihub

Quantum circuit compilation plays a fundamental role in optimizing quantum algorithms for execution on near-term quantum hardware. Traditional compilation approaches rely on heuristic or rule-based methods, which may not fully exploit the potential of advanced machine learning techniques. This study explores the synergy between reinforcement learning (RL) and supervised learning (SL) to enhance quantum circuit compilation efficiency. The proposed hybrid approach leverages RL for decision-making in circuit transformations while using SL to refine and accelerate learning through historical data. Experimental results demonstrate that this methodology achieves superior gate count reduction and depth optimization compared to conventional techniques, paving the way for more efficient quantum computing applications.

Keywords: Quantum circuit compilation, reinforcement learning, supervised learning, hybrid machine learning, quantum computing optimization.

Would you like to learn more about how reinforcement learning and supervised learning can improve quantum circuit compilation? This study was published on arXiv, an open-access repository for cutting-edge research in physics, mathematics, and computer science. You can read the full article at the following link: https://arxiv.org/pdf/2503.15394.

Ensuring the reliability of auxiliary services in electrical substations during power outages is critical for the stability and safety of power systems. Traditional backup solutions, such as diesel generators, often involve high operational costs and environmental concerns. This study presents a stochastic programming approach to optimally size hybrid backup systems that integrate renewable energy sources and battery storage to support substation auxiliary services during outages. The proposed model accounts for the uncertainties associated with renewable energy generation and load demand, aiming to minimize the total cost while ensuring reliability. A case study demonstrates the effectiveness of the approach, showing significant improvements in cost efficiency and sustainability compared to conventional backup systems.

Keywords: Substation auxiliary services, hybrid backup systems, stochastic programming, renewable energy integration, battery storage, reliability, cost optimization.

Would you like to learn more about optimizing hybrid backup systems for substation auxiliary services and how stochastic programming enhances their reliability and cost-effectiveness? This study was published in Electric Power Systems Research, a leading journal in power system engineering and optimization. You can read the full article at the following link: https://www.sciencedirect.com/science/article/abs/pii/S0378779624010770

This study analyzes the economic impact of floods in the Andean region of Colombia and their relationship with climate change by examining meteorological variables such as precipitation, air temperature, and relative humidity. Using an Autoregressive Distributed Lag-Error Correction Model (ARDL-ECM), the research quantifies both the short-term and long-term effects of these variables on flood damage costs, employing monthly data from October 2006 to December 2023.

The results show a statistically significant relationship between meteorological conditions and flood damage costs. Specifically, precipitation has a positive and significant impact on flood costs in both the short and long run, whereas air temperature shows a negative impact. To enhance the analysis, climate change projections were incorporated using Global Climate Models (GCMs) and statistical downscaling techniques (SD). These projections indicate an increase in flood frequency and severity in the coming decades, reinforcing the urgency of climate adaptation and mitigation measures.

The study underscores the necessity of strengthening flood prevention policies, including investment in resilient infrastructure, improved early warning systems, and sustainable land-use planning. Additionally, the findings support broader climate policies aimed at reducing greenhouse gas emissions and deforestation, particularly in vulnerable areas like the Amazon.

Would you like to learn more about the impact of climate change on flood damage costs and how these effects can be projected into the future? This study was published in Sustainable Cities and Society, a high-impact journal in urban sustainability and resilience. You can read the full article at the following link: https://www.sciencedirect.com/science/article/abs/pii/S2210670725001866#d1e3829

As part of the event, distinctions were awarded to the most outstanding works presented. Esteban Leiva, a Master’s student in Industrial Engineering and a graduate of the Mathematics undergraduate program, received an honorable mention for the quality and rigor of his work. His contribution was evaluated alongside submissions from Ph.D. students from various international institutions.

This recognition highlights not only the student’s academic excellence but also the level of training and research fostered in postgraduate programs in the region.

For more information, visit: https://optimization-workshop.github.io/