On Solving Selected Nonlinear Integer Programming Problems in Data Mining, Computational Biology, and Sustainability

This thesis consists of three essays concerning the use of optimization techniques to solve four problems in the fields of data mining, computational biology, and sustainable energy devices. To the best of our knowledge, the particular problems we discuss have not been previously addressed using optimization, which is a specific contribution of this dissertation. In particular, we analyze each of the problems to capture their underlying essence, subsequently demonstrating that each problem can be modeled as a nonlinear (mixed) integer program. We then discuss the design and implementation of solution techniques to locate optimal solutions to the aforementioned problems. Running throughout this dissertation is the theme of using mixed-integer programming techniques in conjunction with context-dependent algorithms to identify optimal and previously undiscovered underlying structure

Creating a Decision Support System for Service Classification and Assignment through Optimization

This paper explores the creation of a Decision Support System (DSS) for the classification and assignment of support tickets within an Information Technology Service (ITS) organization. With a reliance on lower-skilled, outsourced, or temporary workers, many organizations service desks experience issues with processing of incoming support tickets. Beyond this, these workers require extensive training and functional knowledge of the company’s structure, and service offerings in order to effectively process tickets. Training requirements, misassignment, unnecessary escalations, and low confidence all contribute to increased costs in the ITS organization. The DSS leverages optimization methods, machine learning, and historic data in order to match an incoming tickets to a service within a service catalog, and recommends an appropriate assignment to a team or individual within the ITS organization for fulfillment or resolution. With this system, a front-line worker gains confidence in the assignment and classification, with lower training barriers and sunk-costs

Large-scale migration into Britain during the Middle to Late Bronze Age

Present-day people from England and Wales harbour more ancestry derived from Early European Farmers (EEF) than people of the Early Bronze Age . To understand this, we generated genome-wide data from 793 individuals, increasing data from the Middle to Late Bronze and Iron Age in Britain by 12-fold, and Western and Central Europe by 3.5-fold. Between 1000 and 875 BC, EEF ancestry increased in southern Britain (England and Wales) but not northern Britain (Scotland) due to incorporation of migrants who arrived at this time and over previous centuries, and who were genetically most similar to ancient individuals from France. These migrants contributed about half the ancestry of Iron Age people of England and Wales, thereby creating a plausible vector for the spread of early Celtic languages into Britain. These patterns are part of a broader trend of EEF ancestry becoming more similar across central and western Europe in the Middle to Late Bronze Age, coincident with archaeological evidence of intensified cultural exchange . There was comparatively less gene flow from continental Europe during the Iron Age, and Britain's independent genetic trajectory is also reflected in the rise of the allele conferring lactase persistence to ~50% by this time compared to ~7% in central Europe where it rose rapidly in frequency only a millennium later. This suggests that dairy products were used in qualitatively different ways in Britain and in central Europe over this period. [Abstract copyright: © 2021. The Author(s), under exclusive licence to Springer Nature Limited.