This thesis evaluates the ability of the Xpress-MP software package to solve complex, iterative mathematicalprogramming problems. The impetus is the need to improve solution times for the VEGA software package, which identifies vulnerabilities to terrorist attacks in electric power grids. VEGA employs an iterative, optimizing heuristic, which may need to solve hundreds of related linear programs. This heuristic has been implemented in GAMS (General Algebraic Modeling System), whose inefficiencies in data handling and model generation mean that a modest, 50-iteration solution of a real-world problem can require over five hours to run. This slowness defeats VEGA's ultimate purpose, evaluating vulnerability-reducing structural improvements to a power grid. We demonstrate that Xpress-MP can reduce run times by 60%-85% because of its more efficient data handling, faster model generation, and the ability, lacking entirely in GAMS, to solve related models without regenerating each from scratch. Xpress-MP's modeling language, Mosel, encompasses a full-featured procedural language, also lacking in GAMS. This language enables a simpler, more modular and more maintainable implementation. We also demonstrate the value of VEGA's optimizing heuristic by comparing it to rule-based heuristics rules adapted from the literature. The optimizing heuristic is much more powerful.
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