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ENME 741
Operations Research Models in Engineering

Course Syllabus

Course Description

The course will cover the fundamentals of Management Science techniques in Project Management including: linear and integer programming, goal programming, multi-objective optimization, simulation, Analytic Hierarchy Process (AHP), deterministic and stochastic dynamic programming.  Applications will be drawn from the Critical Path Method (CPM), resource management, and other areas of Project management.

Course Prerequisites

  • ENCE 302 Probability and Statistics for Civil Engineers or some exposure to probabilistic modeling
  • MATH 240 Introduction to Linear Algebra or some exposure to vectors and matrices
  • MATH 140, Calculus I or equivalent

Required Course Text

  • W. L. Winston,
    Operations Research Applications and Algorithms (Fourth Edition)

Supplemental Course Texts (not required but helpful)


Course Objective

Provide and overview of techniques and models used in decision modeling contexts.


Dr. Steven A. Gabriel


The overall course grade will be derived from tthe following areas:

  • Homeworks, 5% of course grade. Homeworks will be assigned and due every week as indicated. They will be reviewed for effort and for each problem get either 0 or the full set of points. They are practice for the two exams and you may work with others on the homeworks but must submit the homeworks separately. Please indicate who you worked with as part of the homework. We will use the class times to solve new problems together interactively as well as go over some of the homework problem solutions posted on ELMS. Note: the two mid-terms will strongly reflect the homeworks so it is in the student's interest to try as many of the homeworks as possible.
  • Quizzes about the lectures, 5% of the course grade-- these will be short true-false/multiple-choice quizzes that you can take any number of times to actively listen to the lectures prior to class time.
  • Mid-term #1, 30% of course grade (this is to be only the work of the student, no collaborating)
  • Mid-term #2, 30% of course grade (this is to be only the work of the student, no collaborating)
  • Project presentation, 5% of course grade (you can work with a team of 2-3 students)
  • Project report, 25% of course grade (you can work with a team of 2-3 students)-- topic to be discussed later in the course but most likely it will involve complementarity problems (the last topic) which generalizes convex optimization and game theory.
  • Homework solutions will be posted on ELMS 9:30am the day they are due.

Course Policies

Students are strongly encouraged to review all the video lectures before the class times. The class times will be used for real-time problem-solving and review of homwork problems. This is the "flipped classroom".

It is assumed that students will work on the exams by themselves.

The course is subject to the Code of Academic Integrity available on the web. The Code prohibits students from cheating on exams, plagiarizing papers, submitting the same paper for credit in two courses without authorization, buying papers, submitting fraudulent documents, and forging signatures.

The University has a legal obligation to provide appropriate accommodations for students with disabilities. Please inform Dr. Gabriel of any accommodations needed relative to disabilities. Also, University of Maryland policy states that students should not be penalized due to observances of their religious beliefs. Please inform Dr. Gabriel of such instances well in advance so that appropriate steps can be taken.

Short Bio on Dr. Gabriel

Academic Experience: Besides teaching at University of Maryland, Dr. Gabriel has held appointments in the Mathematical Sciences Department at The Johns Hopkins University, and in the Engineering Management and Systems Engineering Department at The George Washington University. In addition, he has served as a postdoctoral researcher in the Mathematics and Computer Science Division at Argonne National Laboratory. Besides being a faculty member in the Department of Mechanical Engineering at UMD, he is also part of the faculty in Applied Mathematics, Statistics, and Scientific Computing.  Also, he has also been: Director of the Master of Engineering and Public Policy Program ( and Group Coordinator for the Civil Systems Program ( within the Department of Civil & Environmental Engineering where he was a faculty member 2000-2015.

Industry Experience: Dr. Gabriel has over 35 years of industry and academic experience involving mathematical modeling of engineering-economic systems with applications in energy, transportation, service performance, and operations management. His specialties include optimization/equilibrium modeling. 

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