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Workforce scheduling is widely used within most industries. A well-outlined and efficient schedule gives cost savings, such as reduced number of overtime hours, increases overall utilization, and facilitates meeting demands. A large and complex schedule, for example, scheduling of a health care workforce, needs to consider many parameters when constructed; it is essential to account for all critical constraints regarding who can dispense a particular medicine, laws restricting the health care system, etcetera. This thesis evaluates two different methods for implementing a workforce scheduling system for one of Linköping’s most well-known restaurants and bars for students, using mixed integer programming and heuristics. Flamman Pub & Disco recruits new employees prior to every semester. Usually, the workforce consists of around 100 employees, and the vast majority of them work either in the bar or in the kitchen. Historically, the scheduling process has been handled manually using Excel. This does, however, take up much time for the operations manager, something considered frowned upon. Therefore, this thesis suggests an automated scheme for future scheduling processes. Because Flamman is a student organization, they do not hold the capital to invest in expensive licensed optimization software. However, literature studies have shown that heuristics such as large neighborhood search can generate sufficient performance, and therefore the investigation of free-of-charge software using a heuristic approach is conducted. The constructed framework uses a mixed integer programming model, which also lays the cornerstone for the two heuristics: a reverse constructive heuristic and a large neighborhood search. The results retrieved from the analysis prove that a heuristic can be a helpful tool for upcoming recruitment periods. There are, however, recommended areas for improvement regarding the current state of the heuristic.

By issuing interest rate derivative contracts, market makers such as large banks are exposed to undesired risk. There are several methods for banks to hedge themselves against this type of risk; one such method is the stochastic programming model developed by Blomvall and Hagenbjörk (2022). The effectiveness of their model relies on accurate pricing of interest rate derivatives and risk factor analysis, both of which are derived from a term structure. Blomvall and Ndengo (2013) present a discretized multiple yield curve framework for term structure measurement that allows for price deviations. The model uses regularization to deal with noise inherent in market price observations, where the regularization counteracts oscillations in the term structure and retains the smoothness of the curve by penalizing the first and second-order derivatives. Consequently, the resulting model creates a trade-off between a smooth curve and market price deviations. Changes in policy rates adjusted by a country’s central bank significantly impact the financial market and its actors. In this thesis, the model developed by Blomvall and Ndengo (2013) was further extended to include these steps in conjunction with monetary policy meetings. Two models were developed to realize the steps in the risk-free curve. The first model introduced an additional deviation term to allow for a shift in the curve. In the second model, the weights in the regularization were adjusted to allow for rapid changes on days surrounding the closest monetary policy meeting. A statistical test was conducted to determine the performance of the two models. The test showed that the model with adjusted regularization outperformed the model with an additional deviation term as well as a benchmark model without steps. However, both step models managed to reduce in-sample pricing errors, while the model with an additional deviation term performed worse than the benchmark model for out-of-sample data, given the current para