Theses and Dissertations from UMD

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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Author: search.filters.author.Wang, XiaSubject: search.filters.subject.Linear Programming

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    Applications of Genetic Algorithms, Dynamic Programming, and Linear Programming to Combinatorial Optimization Problems
    (2008-10-16) Wang, Xia; Golden, Bruce L.; Mathematics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Combinatorial optimization problems are important in operations research and computer science. They include specific, well-known problems such as the bin packing problem, sequencing and scheduling problems, and location and network design problems. Each of these problems has a wide variety of real-world applications. In addition, most of these problems are inherently difficult to solve, as they are NP-hard. No polynomial-time algorithm currently exists for solving them to optimality. Therefore, we are interested in developing high-quality heuristics that find near-optimal solutions in a reasonable amount of computing time. In this dissertation, we focus on applications of genetic algorithms, dynamic programming, and linear programming to combinatorial optimization problems. We apply a genetic algorithm to solve the generalized orienteering problem. We use a combination of genetic algorithms and linear program to solve the concave cost supply scheduling problem, the controlled tabular adjustment problem, and the two-stage transportation problem. Our heuristics are simple in structure and produce high-quality solutions in a reasonable amount of computing time. Finally, we apply a dynamic programming-based heuristic to solve the shortest pickup planning tour problem with time windows.