Rainwater harvesting is the process of collecting, storing, and distributing rainwater for reuse, rather than allowing it to run off. Greywater recycling consists of purifying processes that remove contaminants present in wastewater coming from faucets, showers, baths, clothes washing, and dishwashing, so that it can be reused, instead of becoming inlet wastewater for municipal wastewater treatment plants. In urban areas, rainwater harvesting and greywater recycling systems provide additional water supplies for houses, buildings, and industries, and also help to mitigate flooding events and pollution. Construction companies are looking for eco-friendly systems that benefit the environment while being profitable. This paper proposes a decision support system (DSS) for the design of rainwater harvesting and greywater recycling system (RHGRS) in residential construction projects. The DSS uses optimization to support construction companies in the design of a RHGRS that minimizes construction and operational costs. With the underlying optimization routine, the DSS addresses both the challenges of deciding whether to construct or not a RHGRS for a residential unit and its minimum-cost design. It is expected that the DSS will help construction companies to make decisions toward sustainable and eco-friendly cities.

Contact: Andres Medaglia

Increasing urbanization is an undeniable feature of this century. Addressing urban growth under severely constrained resources is a task that demands comprehensive efforts in planning towards efficient development: optimizing the treatment of resources (natural, economic) and residues (emissions, waste, idle time), for which an accurate understanding of urban processes is necessary. While network analysis and systems dynamics provide tools to describe and understand relationships within and between stakeholders and the physical world, statistical models and simulation allow to estimate and predict behavior patterns that enable better decision making. Qualitative and quantitative descriptions of urban problems are translated to optimization models that provide support in the design of public policy and strategic planning in problems such as: land-use regulation; incentives and planning for renewable energy adoption; configuration and planning for transportation systems; resource allocation for disaster preparedness.

Contact: Camilo Gomez

In the context of infrastructure resilience, key decisions occur in the aftermath of adverse events, but most importantly, preemptive decisions must be made without knowledge about the characteristics of a potential future event. An optimization framework is proposed to address problems in which preemptive decisions are coupled with those eventually required to respond to an uncertain adverse event. Strategic decisions are pursued regarding whether to proactively retrofit or reactively repair bridges in a transportation network under seismic hazards, aiming to minimize the cost of maintaining travel times under a set of adverse scenarios. A two-stage stochastic programming approach is proposed, which relates pre- and post-event decisions, using a decomposition of the optimization problem which is advantageous when dealing with complex networks. We perform an analysis of the San Francisco Bay Area transportation network, as an instance of a realistic, complex infrastructure network. Results evidence the potential of the approach to provide risk-informed decision support, while being able to deal with large sets of components and scenarios under an exact optimization approach, and solving problems with millions of variables and constraints.

Contact: Camilo Gomez

Resilience of infrastructure networks is key for urban development, mostly when understood as the way in which physical systems help societies prosper. In this sense, infrastructure engineering must incorporate the societal aspects related to infrastructure operation, namely: the organizations involved in infrastructure operation (government, agencies, contractors, regulators), and the general public who uses and benefits from these systems. The explicit consideration of these parties impacts the modeling and analysis of infrastructure systems with regards to how decisions are made (depending on the type of stakeholder, and its organizational constraints) and how the impact is measured (depending who the constituents are). For example, in the context of risk assessment and management, organizational constraints may imply stakeholders with divergent interests, or centralized vs. decentralized resource availability, decision processes, and operations, etc.; regarding user-related aspects, socio-economic vulnerability may have an impact on how different segments of the population experience the (lack of) service/benefits of an infrastructure system. Incorporating these aspects may affect the definition of performance metrics of interest and, consequently, influence risk management policies. In this project, we propose recovery strategies for interdependent networks of power, gas, and water in Shelby County, TN (USA), considering operators’ as well as societal objectives within an optimization framework.

Contact: Camilo Gomez

Bus rapid transit (BRT) systems such as TransMilenio (in Bogota) have become a real alternative to more expensive rail-based public transportation systems. However, once the BRT system infrastructure is operational, its success often depends on the routes offered to the population. One of the challenges that planners face is the estimation of the origin-destination (OD) matrix that is the key driver to the set of routes and frequencies that need to be offered to the population. This matrix captures how people move in the city from each origin to each destination throughout the day. The estimation of the OD matrix is challenged by the lack of complete information. In many BRT systems it is known where passengers board buses, but not where they leave the system. Based on a vast amount of (partial) data and the use of modern analytics techniques this research line is focused on estimating the OD matrix of public transportation systems. Once the OD matrix is estimated, the problem of finding a set of routes (services) and frequencies that minimizes the operational and passenger costs (travel time) while simultaneously satisfying the system’s technical constraints (e.g., demands for trips, bus frequencies, and lane capacities). Our approach to the problem is based on a mathematical formulation that exploits the underlying network structure. However, because of the vast number of routes, solving the problem directly is out of reach for most practical instances. Thus, our approach is based on decomposition strategies (e.g., simultaneous column-and-row generation and matheuristics) that break the problem into manageable parts.

Photo by mariordo59/ CC BY-SA 2.0

Contact: Andrés L. Medaglia

Related work::

• Walteros, J. L., Medaglia, A. L., and Riaño, G. (2015). Hybrid algorithm for route design on bus rapid transit systems. Transportation Science. 49(1):66-84.
• Feillet, D., Gendreau, M., Medaglia, A. L., Walteros, J. L. (2010). A note on branch-and-cut-and-price. Operations Research Letters. 38(5):346-353.

A key aspect related to network design, is estimating how a given system is going to perform under stress or an undesired event. We have worked on models that are able to evaluate the evacuation dynamics in a network of interconnected facilities (e.g., buildings). By using a macroscopic approach, a network optimization model determines the optimal evacuation paths under a rational behavior of the evacuees and without taking into account network congestion. Because this is overly optimistic, an (stochastic) microscopic approach incorporates congestion and the erratic behavior of the evacuees. By feeding back both models, a mesoscopic model is able to estimate evaluation times and identify bottlenecks that cause critical congestion during an evacuation. With this information, it is now possible to identify investment plans that make the network more robust and efficient in terms of disaster or shock.

Contact: Andrés L. Medaglia and Jorge A. Huertas

At a strategic level, optimizing the resilience of any given system requires optimizing decisions for diminishing vulnerability and increasing efficient response. We have worked on devising a comprehensive optimization framework that incorporates resilience and uncertainty of interconnected networks. The methodologies devised in this line of research help decision makers to plan where to invest in the networks (e.g., road network, supply chain, gas network, power network), so that after an uncertain and shocking (e.g., disaster or low-demand) event, the system is able to recover and perform well as early as possible.

Contact: Andrés L. Medaglia
Related work:
González, A., Dueñas-Osorio, L., Sánchez-Silva, M., and Medaglia, A. L. (2016). The interdependent network design problem for optimal infrastructure system restoration. Computer-Aided Civil and Infrastructure Engineering. 31(5):334–350.