Generating employee rosters on a 24 hour, 7 day per week basis taking into account fluctuating demand for employees, employee skills, working conditions, training and employee preferences, while ensuring efficiency and equity between the employees is a very difficult task due to the very large number of possible rostering combinations available. The research done in this thesis sets to solve this exact problem for CS Energy's Swanbank Power Station located in Queensland, Australia. -- abstract. / Thesis (PhDMathematics)--University of South Australia, 2002.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, March 2009. / Thesis Advisor(s): Carlyle, W. Matthew. "March 2009." Description based on title screen as viewed on April 24, 2009. Author(s) subject terms: Integer Programming, Operational Planning, Navy Mission Planner, Navy Asset-Mission Pairing, Maritime Headquarters, Maritime Operations Center, Constrained Enumeration, Stack-based Enumeration, Mathematical Programming, Optimization, Decision Aid, Planning Tool, Ship Employment Schedule. Includes bibliographical references (p. 61-62). Also available in print.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, March 2005. / Thesis Advisor(s): W. Matthew Carlyle. Includes bibliographical references (p. 47-48). Also available online.

Thesis (Ph. D. in Operations Research)--Naval Postgraduate School, Sept. 2004. / Thesis Advisor(s): R. Kevin Wood. Includes bibliographical references (p. 71-80). Also available online.

Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2006u. / Nemhauser, George L., Committee Chair ; Ahmed, Shabbir, Committee Member ; Bartholdi, John J., Committee Member ; Takriti, Samer, Committee Member ; Gu, Zonghao, Committee Member.

Thesis (Ph. D.)--University of Delaware, 2005. / Principal faculty advisor: David B. Saunders, Dept. of Computer & Information Sciences. Includes bibliographical references.

SNCF is a large-sized railway transportation company that needs to be operated 365 days a year and 24 hours a day. In order to schedule a certain category of workers in train stations and selling points, rosters are designed to cover a cyclical demand. However, the highly combinatorial nature of the rostering problem makes it very difficult to solve manually, and experts spend a huge amount of time to derive implementable solutions that improve a number of preference criteria. This thesis presents two formulations based on mixed-integer programming to adress the cyclical rostering problem. The first one uses variables to express the nature of each day of the roster, whereas the second one uses patterns corresponding to feasible blocks of seven days and assigns them to each week of the roster. Different strategies relative to the management of some preference criteria are compared, some of them leading to significant reductions in computational times. Cuts are finally introduced to improve the bounds obtained by the linear relaxation of the mixed-integer programs. The impact of these cuts on computational times depends much on the problem. / Master of Science

Improving the explainability of results from machine learning methods has become an important research goal. In this thesis, we have studied the problem of making clusters more interpretable using a recent approach by Davidson et al., and Sambaturu et al., based on succinct representations of clusters. Given a set of objects S, a partition of S (into clusters), and a universe T of descriptors such that each element in S is associated with a subset of descriptors, the goal is to find a representative set of descriptors for each cluster such that those sets are pairwise-disjoint and the total size of all the representatives is at most a given budget. Since this problem is NP-hard in general, Sambaturu et al. have developed a suite of approximation algorithms for the problem. We also show applications to explain clusters of genomic sequences that represent different threat levels / Master of Science / Improving the explainability of results from machine learning methods has become an important research goal. Clustering is a commonly used Machine Learning technique which is performed on a variety of datasets. In this thesis, we have studied the problem of making clusters more interpretable; and have tried to answer whether it is possible to explain clusters using a set of attributes which were not used while generating these clusters.

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