This course will provide students with the necessary analytical skills in classical and metaheuristic optimization methods to analyze the life cycle of a variety of Civil and Environmental Systems. Examples will be drawn from a variety of civil systems, with emphasis on water resources projects. Examples will be used to illustrate a wide variety of important principles including: network analysis, constrained optimization, risk analysis, multi-objective optimization, and stochastic optimization. In addition, the influence of natural and anthropogenic objectives and constraints on decision making will be investigated.

The first two sections of the course (see outline below) will place a strong emphasis on learning problem-formulation skills. The organization of data, the drivers forcing decisions, and the resources being allocated will be the focus of the problem formulation. Process simulation is a large part of this effort, whereby the performance function of civil systems are estimated.

The second two sections will emphasize how Operations Research (OR) methods can inform the decision making process for civil and environmental systems. Specifically, the effects of risk, uncertainty, and multiple stakeholders on the mathematical formulation of the problem will be investigated.

Emphasis will be placed on applied OR theory, therefore each section of the course will have a problem set along with a reading list. A term project will be assigned early in the semester in the area of the students' interest. The Professor will work closely with the individual student to develop a systems-based analysis of the project using appropriate tools learned in class. Ample time will be provided in the course for discussion and presentation of projects. This will provide valuable information for the entire class by illustrating the broad spectrum of project types, scales, and outcomes.

Part 1. Systems analysis in civil engineering (1 week).

1. Systems defined
2. Planning and design
3. Operation and life cycle

Part 2. Classical constrained optimization techniques (3 weeks)

1. Objective function definition
2. Equality and inequality constraints
3. Linear programming, simplex, dual problem
4. Non-linear programming including GRG and dynamic programming

Part 3. Metaheuristic optimization methods (4 weeks)

1. Global optimization
2. Genetic Algorithms
3. Other metaheuristics (tabu, simulated annealing, scatter search)

Part 4. Risk analysis and stochastic optimization (3 weeks)

1. Probabalistic methods
2. Monte Carlo methods
3. Linking Monte Carlo sampling with genetic algorithms

Part 5. Applications of Operations Research methods to civil systems (4 weeks)

Will provide students a sound fundamental basis for applying state of the art decision support methods to problems involving Civil and Environmental Systems. These fundamentals will then be used to study a variety of pragmatic problems that will be explored using a case study approach.

Provide the skills needed to support large-scale civil and environmental decision making through simulation, optimization and risk analysis.

Nothing beyond Engineering BS.

Revelle, et al., Civil and Environmental Systems Engineering, 2nd ed., Prentice Hall, 2003, ISBN-10: 0130478229.

Will use freeware from the US EPA and Army Corp of Engineers. May also use trial versions of freely available software.