Ph.D. in Industrial and Management Systems Engineering

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Admission Requirements:

Grade Point
Admission with full standing requires a grade point average (GPA) for all previous graduate work of at least a 3.50 point (on a 4.00 scale). Applicants with a GPA below a 3.50 or with deficiencies may be granted admission on a provisional basis until requirements established by the Department Graduate Committee are fulfilled.

Graduate Record Examination (GRE)
The General Graduate Record Examination is required for all applicants.

Previous Degrees
A student must possess a Masters degree with at least one degree in Industrial Engineering, Manufacturing Systems Engineering, or a related field. At the discretion of the graduate committee, any applicant without a degree in engineering may be required to complete a Master of Science in Industrial and Management Systems Engineering or Manufacturing Systems Engineering before he/she is admitted to the doctoral program.

Courses
The following subjects in the areas of mathematics and statistics, and engineering science will be required of all applicants:

Mathematics, Statistics, and Computer Science:

• Analytical Geometry and Calculus III
  
• Differential Equations
  
• Probability and Statistics (Calculus-based)
  
• At least one Programming Language (FORTRAN, BASIC, C, or Pascal)

Engineering Science (minimum of 3 subjects):

• Statics
  
• Dynamics
  
• Thermodynamics
  
• Fluid Mechanics
  
• Measurements
  
• Strength of Materials
  
• Electrical Engineering

No Pass/Fail coursework may be used in the PhD program or to satisfy prerequisite requirements.

Doctoral Program Requirements:

Residency
For a student who transfers to the University of Nebraska-Lincoln with a masters degree from another institution, or who takes a break in his/her graduate work at UNL between the time the masters degree is awarded and the time he/she starts to work on a doctoral program, the residency requirement is 27 hours of graduate work in a consecutive 18-month period or less. (Consult the latest edition of the Graduate Studies Bulletin for more information.)

Program of Study
The minimum amount of graduate credit is 90 semester hours, including a dissertation. Not fewer than 45 semester hours must be completed at the University of Nebraska-Lincoln. The student must complete at least one-half of the graduate work, including the dissertation, in the Department of Industrial and Management Systems Engineering. The language and research tool requirement of the UNL Graduate Studies must be met. The program of study must be approved by the supervisory committee.

Supervisory Committee
The student shall select a major after passing the qualifying examination and before completing 45 hours toward his or her doctoral degree. The student, in consultation with his or her major professor, will form a supervisory committee consisting of at least four graduate faculty fellows. At least two members of the supervisory committee must be from the Industrial and Management Systems Engineering Department. The committee membership list will be submitted to the Chair of the College Ph.D. committee to be forwarded to the Graduate Dean for official appointment.

Mathematics and Statistics Requirement for Ph.D. Students:

A Ph.D. student in Industrial and Management System Engineering must demonstrate competency in three courses focused on mathematics and statistics. Students are encouraged to complete the three courses as soon as possible and are required to follow all Requirements for the Degree of Doctor of Philosophy as outlined in the Graduate School Bulletin.

Students are required to take one course from each of the following three areas (courses listed in two areas can only be used to satisfy the requirement of a single area):

Area 1:

• STAT 482/882: Mathematical Statistics I-Distribution Theory

Area 2:

• BIOM 810: Survey of Multivariate Techniques in Biometry
  
• STAT 485/885: Applied Statistics I
  
• STAT 486/886: Applied Statistics II
  
• STAT 487/887: Applied Statistics III

Area 3:

• EDPS 969: Nonparametric Statistical Methods
  
• MATH 314/814: Applied Linear Algebra (Matrix Theory)
  
• STAT 483/883: Mathematical Statistics II-Statistical Inference
  
• STAT 486/886: Applied Statistics II
  
• STAT 487/887: Applied Statistics III

Note: with the pending merger of the Biometry (BIOM) and Statistics (STAT) departments, some of the above courses will potentially change.

The minimum acceptable grade earned in each of the three courses is a B. In addition, a final combined grade point average (GPA) of 3.25 must be obtained for the three courses. If a student takes a course multiple times, the last course grade will be used in the GPA calculation.

No credit or substitutions will be made for courses taken at another university. If a student has already completed a course in an area during previous studies at the University of Nebraska-Lincoln, the student is exempt from taking a course from that area, but is still subject to the grade and GPA requirement.

A student will not be allowed to form a supervisory committee until all three courses have been completed and the course grade and combined GPA requirements are satisfied. It is up to the student's supervisory committee as to whether any of the courses taken to meet this mathematics and statistics requirement will be included on the submitted program of study.

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Area 1 Courses:

STAT 882: Mathematical Statistics I-Distribution Theory (3 credits) Sample space, random variable, expectation, conditional probability and independence, moment generating function, special distributions, sampling distributions, order statistics, limiting distributions and central limit theory.

Area 2 Courses:

BIOM 810: Survey of Multivariate Techniques in Biometry (3 credits) Introduction to multivariate techniques commonly used in agriculture research with emphasis on general application, relevance, and interpretation. Course divided into three modules. Module 1: reduction of dimensionality and multivariate dependencies including principle components, factor analysis and canonical correlation. Module II: classification procedures including discriminate analysis, cluster analysis and multidimensional scaling. Module III: multivariate expressional to the analysis of variance and the general linear model.

STAT 885: Applied Statistics I (3 credits) General linear models for estimation and testing problems analysis and interpretation for various experimental designs.

STAT 886: Applied Statistics II (3 credits) Time series: introduction to model building and forecasting. Multivariate analysis methods: multivariate distributions, inference on correlations, regression, mean vectors and covariance matrices; tests of independence; canonical correlation; classification and discriminate analysis; principle component analysis.

STAT 887: Applied Statistics III (3 credits) Sampling techniques: simple random sampling, sampling proportions, estimation of sample size, stratified random sampling, ratio and regression estimates. Nonparametric methods: order statistics, tests for goodness of fit, linear rank tests, asymptotic relative efficiency, means of association.

Area 3 Courses:

EDPS 969: Nonparametric Statistical Methods (3 credits) Presentation of statistical procedures that do not require fundamental assumptions about the distribution property of the variables to be analysis. Chi Square tests, rank test of location (Wilcoxen, Mann Witney, Kruskal-Wallis, Friedman), tests of goodness of fit (Chi Square, Kolmogorov-Smirnoff), tests of randomness (Runs).

MATH 814: Applied Linear Algebra (Matrix Theory) (3 credits) Similarity of matrices, diagonalization of symmetric matrices, canonical form, eigenvalues, quadratic form, vectors, and application to linear systems. Note: a term paper and/or special project is required for graduate credit

STAT 883: Mathematical Statistics II - Statistical Inference (3 credits) Interval estimation; point estimation, sufficiency and completeness; Bayesian procedures; uniformly most powerful tests, sequential probability ratio test, likelihood ratio test, goodness of fit tests; elements of analysis of variance and nonparametric tests.

STAT 886: Applied Statistics II (3 credits) Time series: introduction to model building and forecasting. Multivariate analysis methods: multivariate distributions, inference on correlations, regression, mean vectors and covariance matrices; tests of independence; canonical correlation; classification and discriminate analysis; principle component analysis.

STAT 887: Applied Statistics III (3 credits) Sampling techniques: simple random sampling, sampling proportions, estimation of sample size, stratified random sampling, ratio and regression estimates. Nonparametric methods: order statistics, tests for goodness of fit, linear rank tests, asymptotic relative efficiency, means of association.

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Dissertation:

The dissertation should treat a subject from Industrial, Management Systems, and/or Manufacturing Engineering approved by the supervisory committee. It should include theoretical development and analysis, show the evidence of technical mastery of Industrial Engineering, and advance or modify former knowledge; i.e., it should treat new material, find new results, draw new conclusions, or interpret previous material in a new light.

Final Examination:

The Dissertation Defense is conducted after the draft dissertation has been approved by the readers of the Supervisory Committee. An oral examination will be administered by the Supervisory Committee, and, preferably, a non-voting external receiver.

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Summary:

The steps to complete the Ph.D. degree:

• Admission to Ph.D. Program
  
• Qualifying Examination
  
• Selection of Thesis Advisor
  
• Formation of Supervisory Committee
  
• Program of Study Comprehensive Examination
  
• Application for Advanced Degree
  
• Submission of Dissertation to Supervisory Committee
  
• Final Examination
  
• Final Approval and Submission of Dissertation