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    Allocating resources to people with preferences

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    LI-DISSERTATION-2017.pdf (14.14Mb)
    Date
    2017-07-14
    Author
    Li, Zihao
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    Abstract
    Allocating Resources to People with Preferences Zihao Li 157 Pages Directed by Dr. Julie Swann Resource allocation can be viewed as the assignment of available resources to different populations, projects, or tasks. Usually, resources can be allocated by markets (where people exchange goods or services based on price and personal preferences) or by central planning (a central planner or agency makes the decision). In many industrial applications such as assembling automobiles from parts or assigning time-constrained tasks to processors, central planning can often yield the optimal result with respect to the entire system. However, in other settings such as allocating healthcare services to patients, distributing vaccines to local providers, or assigning jobs to workers, preferences of the individuals need to be considered in addition to system objectives. Three practical and theoretical problems are presented that involve allocating resources to populations with different preferences or demands: 1) measuring the spatial access of patients across a large network where patients have different preferences over the healthcare providers, 2) quantifying the benefit of allocating vaccines to local areas based on inventory information when vaccine uptake rates vary geographically, and 3) assigning workers to jobs in a multi-period setting with workers and jobs listing preferences over each other. We use mathematical modeling approaches tailored for each of the problems that allow us to capture the preferences of populations in each system. The problems presented in the thesis demonstrate how mathematical modeling can be used to incorporate different types of preferences in a network and to inform efficient decision makings for complex systems.
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    http://hdl.handle.net/1853/58704
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    • Georgia Tech Theses and Dissertations [23877]
    • School of Industrial and Systems Engineering Theses and Dissertations [1457]

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