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Students will form semester teams comprising of 1-2 students. Each student is going to model an interesting real-life/practical problem or problems of their choice using stochastic programming. Students are encouraged to work on applications from their thesis or dissertation. Students will then implement extensions of the algorithms learning in class and implemented via mini-projects, conduct computational experiments to solve large-scale instances from the application area using the implemented code (instances may be from the literature, or randomly generated), and perform a solution analysis. Finally, each team will write a project report with a minimum of ten references and will do a class presentation during the last week of the course. A one-page project proposal due by TBA should include a project title, problem description, why the problem is interesting/important to society today, and some preliminary references.
Using CPLEX with Microsoft Visual C++ 6.0 Using CPLEX with Microsoft Visual Studio .NET 2003
Potential Project Areas Health care, environmental planning, homeland security, telecommunication, manufacturing, transportation, electricity power generation, finance, etc. Potential Project Topics 1. Airline Fuel Management. Recent high fuel prices and the impact they have had on the airline industry call for airline fuel planning models that take into account future uncertainty. In this case one can envision developing a stochastic programming model to determine the optimal strategy for fueling aircraft to support both short and long-term planning. Such a model should specify best fueling stations and vendor for each flight based on uncertain fuel prices, availability, fuel burn, flight data, and cost of tankerage. Read this article from the February issue of ORMS Today: Are Airlines Doing Their Homework? By Dr. Joseph C. Hartman. 2. Energy Systems Planning. Recently there has been interest in planning for future energy sources (nuclear, coal power plants, wind, solar, etc) under deregulation of the electrical power industry. The goal is develop models to assist in planning future locations of power plants in the face on uncertain load demands and system adequacy. Stochastic programming provides one way to develop models for this class of problems. 3. Emergency Planning. This is a broad topic and deals with resource location and allocation in the face of future emergency situations such as forest fires, hurricanes, and other disasters. The goal here is to develop strategic and tactical decision-making models that consider various uncertain possible future outcomes (scenarios) in placing necessary resources at strategic locations before and during an emergency situation. 4. Health Care Planning. This is a broad area with potential applications for stochastic programming. For example Health care costs are escalating so rapidly and new treatments and expensive technologies (CATSCANS, MRI, etc) are frequently introduced to provide better health care. However, clinics and hospitals are still faced with the problem of optimizing their patient throughput through the system from the time the patient enters the doctors office, to the time the patient receives treatment. There is uncertainties in patient scheduling for treatment (surgery, radiology, etc) dictated by the availability of the staff to administer the treatment as well as they equipment. Also, these health centers are faced with the need to efficiently maximize the utilization of costly medical equipment in order to efficiently perform their cost management. 5. Other. Other potential research areas include predictive modeling for manufacturing and process optimization, planning and control of manufacturing and distribution operations in highly distributed global environments, personnel planning, optimization of designs for manufacturing systems and facilities, especially in the presence of uncertainty and risk about the operating environment. |
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