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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Strategic Plan For Industrial And Management Systems Engineering, Paul Savory
Strategic Plan For Industrial And Management Systems Engineering, Paul Savory
Industrial and Management Systems Engineering: Reports
The role of this strategic plan is to map out a range of department goals, offer objectives for achieving each goal, and list potential strategies for meeting an objective. It also lists performance metrics for measuring progress for each goal. Each year, a subset of goals and objectives will be identified by the IMSE department as a priority for the coming year. Detailed metrics for measuring improvement for the department’s priorities areas will then be defined. The department committee to develop the plan consisted of Dr. Paul Savory (committee chair), Dr. Susan Hallbeck, and Dr. Erick Jones.
Randomly Generating Manufacturing Flow Line Models Using Mathematica, Paul Savory
Randomly Generating Manufacturing Flow Line Models Using Mathematica, Paul Savory
Department of Industrial and Management Systems Engineering: Faculty Publications
To test heuristic algorithms and techniques, researchers need numerous datasets so as to measure effectiveness and improve approaches. This paper discusses using Mathematica, a mathematical programming language, for randomly generating the specifications for manufacturing flow line models. Important issues include determining an arrival rate to a flow line, the number of flow line stations, the number of parallel servers for each production station, and specifying the service time distributions and their associated parameters. The paper concludes with a discussion on generating more general types of simulation models.
How Do You Interpret A Confidence Interval?, Paul Savory
How Do You Interpret A Confidence Interval?, Paul Savory
Industrial and Management Systems Engineering: Instructional Materials
A confidence interval (CI) is an interval estimate of a population parameter. Instead of estimating the parameter by a single value, a point estimate, an interval likely to cover the parameter is developed. Many student incorrectly interpret the meaning of a confidence interval. This paper offers a quick overview of how to correctly interpret a confidence interval.
Why Divide By (N-1) For Sample Standard Deviation?, Paul Savory
Why Divide By (N-1) For Sample Standard Deviation?, Paul Savory
Industrial and Management Systems Engineering: Instructional Materials
In statistics, the sample standard deviation is a widely used measure of the variability or dispersion of a data set. The standard deviation of a data set is the square root of its variance. In calculating the sample standard deviation, the divisor is the number of samples in the data set minus one (n-1) rather than n. This often confuses students. This paper offers a quick overview of why the divisor is (n-1) for calculating the sample standard deviation.