Browsing by Author "Greenwood, Allen G."

Now showing 1 - 2 of 2
  • Thumbnail Image
    A decision support system for tuition and fee policy analysis
    Greenwood, Allen G. (Virginia Polytechnic Institute and State University, 1984)
    Tuition and fees are a major source of income for colleges and universities and a major portion of the cost of a student's education. The university administration's task of making sound and effective tuition and fee policy decisions is becoming both more critical and more complex. This is a result of the increased reliance on student-generated tuition-and-fee income, the declining college-age student population, reductions in state and Federal funds, and escalating costs of operation. The comprehensive computerized decision support system (DSS) developed in this research enhances the administration's planning, decision-making, and policy-setting processes. It integrates data and reports with modeling and analysis in order to provide a systematic means for analyzing tuition and fee problems, at a detailed and sophisticated level, without the user having to be an expert in management science techniques or computers. The DSS with its imbedded multi-year goal programming (GP) model allocates the university's revenue requirements to charges for individual student categories based on a set of user-defined objectives, constraints, and priorities. The system translates the mathematical programming model into a valuable decision-making aid by making it directly and readily accessible to the administration. The arduous tasks of model formulation and solution, the calculation of the model's parameter values, and the generation of a series of reports to document the results are performed by the system; whereas, the user is responsible for defining the problem framework, selecting the goals, setting the targets, establishing the priority structure, and assessing the solution. The DSS architecture is defined in terms of three highly integrated subsystems - dialog, data, and models - that provide the following functions: user/system interface, program integration, process control, data storage and handling, mathematical, statistical, and financial computations, as well as display, memory aid, and report generation. The software was developed using four programming languages/systems: EXEC 2, FORTRAN, IFPS, and LINDO. While the system was developed, tested, and implemented at Virginia Polytechnic Institute and State University, the concepts developed in this research are general enough to be applied to any public institution of higher education.
  • Thumbnail Image
    A knowledge-based simulation optimization system with machine learning
    Crouch, Ingrid W. M. (Virginia Tech, 1992-05-06)
    A knowledge-based system is formulated to guide the search strategy selection process in simulation optimization. This system includes a framework for machine learning which enhances the knowledge base and thereby improves the ability of the system to guide optimizations. Response surfaces (i.e., the response of a simulation model to all possible input combinations) are first classified based on estimates of various surface characteristics. Then heuristics are applied to choose the most appropriate search strategy. As the search is carried out and more information about the surface becomes available, the knowledge-based system reclassifies the response surface and, if appropriate, selects a different search strategy. Periodically the system’s Learner is invoked to upgrade the knowledge base. Specifically, judgments are made to improve the heuristic knowledge (rules) in the knowledge base (i.e., rules are added, modified, or combined). The Learner makes these judgments using information from two sources. The first source is past experience -- all the information generated during previous simulation optimizations. The second source is results of experiments that the Learner performs to test hypotheses regarding rules in the knowledge base. The great benefits of simulation optimization (coupled with the high cost) have highlighted the need for efficient algorithms to guide the selection of search strategies. Earlier work in simulation optimization has led to the development of different search strategies for finding optimal-response-producing input levels. These strategies include response surface methodology, simulated annealing, random search, genetic algorithms, and single-factor search. Depending on the characteristics of the response surface (e.g., presence or absence of local optima, number of inputs, variance), some strategies can be more efficient and effective than others at finding an optimal solution. If the response surface were perfectly characterized, the most appropriate search strategy could, ideally, be immediately selected. However, characterization of the surface itself requires simulation runs. The knowledge-based system formulated here provides an effective approach to guiding search strategy selection in simulation optimization.