During the past decade, many sophisticated mathematical and computational techniques have been developed for analyzing finance data. Financial engineering is a multidisciplinary field that uses tools and concepts of statistics, economics, computational methods and applied mathematics, to solve financial problems and to support the decision-making process of investment, loans, and risk management.
COPA conducts research on a wide range of energy issues. One of the topics is the oil economy, commodity price review and volatility estimation, energy business strategies, risk management, hedging strategy, and regulation of energy industries. Another new area of research focuses on the economics of renewable energy, especially wind, solar and water.
| In an era where people health has been unjustly impacted by their income, age, gender, education, ethnicity, and socioeconomic status, programs like the Recreovía and the Ciclovía Recreativa serve the entire community regardless of any of these factors with a health-focused approach. Accordingly, the necessity to bring healthy living into people’s daily lives has been and will continue to be a significant challenge for public health practitioners and city officials. This challenge inevitably involves assigning resources innovatively and efficiently using analytics-based techniques towards the goal of having healthier and sustainable cities. See the video of the RecreoBOG project |
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Contact: Andrés L. Medaglia
Related work and links:
| Oil palm agricultural systems involve large extensions of land that demand careful planning of the harvesting operations. The plantation manager is in charge of synchronizing resources (i.e., crews and a complex cableway network) to harvest at the right time to maximize palm oil yield in latter stages of the value chain. In particular, in this type of crop it is ideal to visit each palm every eight to ten days to avoid loose fruit picking, over-ripeness, or rotten fruit harvesting. To optimize harvest operations, we propose an end-to-end analytics solution involving data treatment, predictive, and prescriptive models (optimization and simulation) in this agricultural system. At the core of our approach lies a set of interconnected models that use optimization, heuristic techniques, and simulation. These models cover strategic (harvest cycle), tactical (resource allocation), and operational (transport allocation) decisions. The results show a strong potential for improving yield by adjusting the harvest cycle length from 19.6 to 8.3 days in a 2000-hectare plantation located in the Colombian Orinoquia. This is a joint research collaboration with the Centro de Estudios de la Orinoqia (CEO). | |
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Contact: Andrés L. Medaglia
| The microstructure of pure Copper processed by machining manufacturing process such as turning and milling is explored with the aim to create highly refined grain structures to achieve the highest strength while postponing the potential possibilities for future recrystallization which inherently lead to non-thermally-stable materials. These two targeted properties are often conflicting requirements: an improvement in one (strength) generally leads to a deterioration in the other (thermal stability). Hence, it is not straight forward to follow a procedure that would set both the properties at their individual best: all that can be achieved is a tradeoff, a compromise between the two objectives. Here we attempt to obtain a set of optimum solutions using multi-objective optimization algorithms as well as the Kuhn-Tucker optimality conditions. Additionally, we verify the solution empirically by creating the sample condition. The resulting microstructure is characterized via electron microscopy confirming the theoretical result. The thermal stability of the optimal solution is verified as well. Finally, we studied the kinetics of crystallization on the optimal solution using the Johson–Mehl–Avrami–Kolmogorov (JMAK) theory. | |
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Contact: Sepideh Abolghasem
| Capturing microstructure evolution in machining experiments such as turning and milling can help in controlling and predicting materials responses as well as the final properties of the engineering metallic components. We examine different metallic materials through machining. The cutting conditions are characterized and the microstructure characteristics are quantified utilizing orientation imaging microscopy (OIM). Finite element models are performed to obtain the central machining conditions for various ranges of paramters. We develop models to study microstructure transformations via two approaches. The first one develops statistical mappings between the machining parameters and the microstructural characteristics. We perform a particular designed experiments method seeking a linear mixed-effects model for the two principal machining parameters, cutting angle and the velocity. The statistical model is applied to identify the factors that most significantly contribute to variability in the mean grain size and orientation angle among the gains. The second approach incorporates the effect of cutting tool edge radius using functional regression method. Building on this, the model estimates the final grain size given the distribution of the tool edge radius. . The premise of the ongoing research is that machining under realistic conditions is characterized by a stochastic system wherein the temporal evolution of the tool-tip geometry and the distributions of the bulk microstructure would interact in complex ways, while leading to the evolution of final microstructures. Identifying this evolution would likely involve a Bayesian framework to link the effect of tool-wear to the modification of machining conditions. | |
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Contact: Sepideh Abolghasem
| 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. |
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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
| Effective resource allocation and quantification of its benefits are essential in practical engineering problems. However, determining the allocation and location of resources is computationally challenging when dealing with large, interconnected systems, as is the case of infrastructure networks. We address the allocation and location of different types of resources to a set of potential sites in an infrastructure system, aiming to satisfy service demands at a minimum cost (e.g., in the context of disaster preparedness). Exact optimization methods become impractical for realistic network sizes, whereas fully heuristic approaches lack optimality guarantees. We use exact optimization to solve a heuristically pre-processed version of the resource location/allocation problem, which incorporates constraints that capture topological network properties based on community detection algorithms. Adding topological constraints leads to significant reductions in computation time without considerable deviation from optimal solutions, as community detection mostly forbids solutions that are unattractive in terms of network topology. The proposed approach to resource allocation incorporates a novel way to account for infrastructure network configurations within linear programming, leading to computational efficiency while producing solutions that capture the inherent topological properties of the network. |  |
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
COPA supports the decision making process at organizations via the analysis, design and application of operations research (OR) and statistical computer-based techniques. Our purpose is to contribute to the scientific and technological development of Colombia, becoming a leading group in R&D.