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dc.contributor.advisorJoglekar, Nitin R.en_US
dc.contributor.authorWang, Leen_US
dc.date.accessioned2019-06-19T14:51:16Z
dc.date.available2019-06-19T14:51:16Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/2144/36014
dc.description.abstractMany firms are challenged to make inventory decisions with limited data, and high customer service level requirements. This thesis focuses on heuristic solutions for inventory management problems in data-scarce environments, employing rigorous mathematical frameworks and taking advantage of the information that is available in practice but often ignored in literature. We define a class of inventory models and solutions with demonstrable value in helping firms solve these challenges.en_US
dc.language.isoen_US
dc.subjectManagementen_US
dc.subjectData-driven solutionen_US
dc.subjectHeuristicen_US
dc.subjectInventory managementen_US
dc.subjectMarkov chain Monte Carlo methoden_US
dc.subjectSamplingen_US
dc.titlePractice-driven solutions for inventory management problems in data-scarce environmentsen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2019-06-03T22:08:35Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineManagementen_US
etd.degree.grantorBoston Universityen_US


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