The aim of this project is to develop a computer-based algorithm that will integrate a planning model with a scheduling model to improve operations management for analytical service facilities. An iterative decomposition algorithm that can provide optimal production scheduling sequences (in acceptable computational times) based on changes in the strategic planning decisions will be provided and tested on an actual industrial-scale facility. Integration of planning and scheduling studies for large-scale plant sizes like that considered in this study have not been reported in the literature.
Pricing risks is of pivotal importance for the insurer’s well-being. Indeed, inappropriately determined prices, whether too high or too low, may result in insolvency of insurance policies, failure of business lines, and even bankruptcy of entire insurance enterprises. This project will help Wawanesa Insurance to develop sophisticated pricing techniques that will take into account (a) exogenous pricing factors, and (b) interdependencies among risks. Wawanesa Insurance will therefore benefit from the resulting competitive advantage.
This project uses a large amount of geographic information and advanced computing technology to re-evaluate the influence of climate on the productivity of Saskatchewan’s agricultural land, which represents about 40% of all the cropland Canada. The intern will combine digital maps of soil, land use, crop yield, elevation, and historical weather observations.
Insurance companies heavily fund marketing campaigns such as, for instance, customer retention or cross-sell initiatives. Uplift modeling aims at predicting the causal effect of an action such as medical treatment or a marketing campaign on a particular individual by taking into consideration the response to an action. Typically, the result of an uplift model is used to call customers for marketing some products based on important attributes of a customer.
The procurement process of an organization is key to understand company costs. Organizations gather large amounts of data coming from different sources (e.g. income statement, balance sheet, general ledger lines). This information is heterogeneous in nature as it is a mix of unstructured and structured data. Moreover, it needs to be cleaned and consolidated in a taxonomy to enable category management. The objective is to group like-to-like items and/or services into categories from Supply Market Analysis point of view and consider category management for the holistic spend.
To estimate the market value of a real estate, a computer software program can produce the result by analyzing the location, market conditions, and other characteristics relevant to it. This is known as the 'automated valuation model (AVM)'. The previous Mitacs Accelerate project with 'Data Nerds' has conducted a series of experiments with the attributes of real estates and geographic dependencies. This project is to further develop the AVM by incorporating temporal factors. The deep learning based approaches will be employed in this project.
Given a set of financial instruments with inherent characteristics at different time intervals, we are interested in finding an optimal trading rule in a high-frequency trading context. A trading rule is defined as a combination of indicators as well as an entry threshold (and potentially other trading parameters). The objective function we are trying to maximize is the profits of the strategy based on the trading rule. One impact of the non-linearity of such problems is that the gradient of the objective function is hard to estimate using a black-box approach.
PRO Financial Solutions (PRO) is looking to investigate the benefits of actively including Value at Risk (VaR) in the capital management process of credit unions. Whereas Earnings at Risk (EaR) is a well-established risk measure, VaR still lacks understanding among credit unions. In this research project, the intern will illustrate the relationship between EaR and Duration of Equity, a VaR proxy, and therefor explain VaR in terms of EaR ? a number credit unions are more familiar with.
Whether due to urbanization or climate changes, flood events have an impact on property value. Newly available geography data about flood risk zone have yet not been utilized to their full potential in Canada. This project aims to study the impact of the flood risk zone on the value of homes for Quebec City, and to identify other regions in Canada where similar conditions could affect housing value. The development of a strategy to expand the aim of research at the country-wide level will else be part of the project.
This proposal is part of a larger research program that aims to study the relationships between IT usage for communication, diversity in social and technical knowledge, and innovation in groups. We have developed a research framework based on social network theories to suggest two different mechanisms for relating diversity and IT usage to novel idea generation and implementation. Our findings will help the partner organization to improve its capability to innovate by better forming and managing diversity within and across group in the context of innovations labs.