Course Syllabus
EngrCEE 111
Methods IV: Systems Analysis and Decision-Making
(This is living document that will be updated semi-regularly as I adjust the course throughout the quarter)
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Canvas Link |
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Zoom Meeting ID |
939 2278 6935
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Zoom Meeting Link |
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Classroom |
Engineering Lecture Hall 100 |
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Class Time |
MWF 4:00-4:50pm |
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Section #: |
A |
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Units |
4 |
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Course Code |
15460 |
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Instructor |
Michael Hyland |
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Zoom Office Hours Link |
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Instr. Office Hours: |
TW 9:00-10:00am |
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Instr. E-mail: |
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Teaching Assistant |
Younghun Bahk |
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TA Email |
ybahk@uci.edu |
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Canvas Notification Settings
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Course Description (from UCI General Catalogue)
Analysis and optimization for decision-making in civil and infrastructural systems. Topics include: linear programming formulations and solution algorithms, network models, and logistical models. Emphasis is on project-level and managerial decision-making and selection from alternative designs.
Expanded Course Description
Optimization (aka Math Programming or Systems Analysis) is a very powerful tool for the analysis and design of systems, including civil and environmental engineering systems. Optimization is broadly and extensively used in industrial, commercial, and public sector settings to inform (i.e., support) decision making and design. The significant breadth of optimization applications should become clear after the first few lectures.
As the title of the course indicates, this is an engineering ‘methods’ course. During the quarter, you will learn some of the theory behind optimization; however, most of our time will be spent conceptualizing optimization problems (i.e., converting real-world problems into the format of an optimization problem), modeling optimization problems as mathematical programs, and, to a lesser extent, solving optimization problems/mathematical programs.
The course covers linear and nonlinear optimization, as well as optimization with continuous and discrete variables. Fortunately, the techniques for conceptualizing and modeling deterministic optimization problems are independent of linearity and variable type. Moreover, the main solution approach for the simplest case—continuous linear optimization—underpins the solution approaches for the other combinations of optimization problems, namely, discrete-linear, continuous-nonlinear, discrete-nonlinear.
Like most engineering topics, optimization is not a spectator sport. You need to practice conceptualizing, modeling, and to a lesser extent solving optimization problems. As undergraduate engineering students, it may be weird/different that this course does not emphasize solving problems. The reason for this is that if you can formulate/model the systems analysis problems correctly, then you can rely on readily available software to solve most optimization problems.
Course Objectives (from UCI General Catalogue)
To prepare students to cope with the fundamental issues of designing, for optimal performance, infrastructural and other civil engineering systems. Moreover, to expose students to (i) the challenges of and techniques for modeling optimization problems and (ii) designing under competing and/or conflicting goals and objectives.
Expanded Course Objectives
Students taking this course will:
- Obtain foundational knowledge related to the conceptualization, modeling, and solving of optimization/systems analysis problems
- Apply this foundational knowledge to the design and analysis of CEE systems, with specific attention to logistics systems and multiple objective problems
- Integrate knowledge of optimization/systems analysis with your prior knowledge of engineering analysis, to address real world challenges
- Become better C/E engineers through knowledge and application of a new engineering method.
- Become better C/E engineers through working on a team to address problems of varying degrees of difficulty
- Transition away from simple engineering problem solving and transitioning toward quantitative analysis to support design and decision-making
Prerequisite Courses
- MATH 3A: Introduction to Linear Algebra
- MATH 3D: Elementary Differential Equations
Prerequisite Topics
- Basic linear algebra <-- Definitely!
- Basic probability and statistics <-- It won’t hurt to brush up on this
- Engineering economics concepts <-- It won’t hurt to brush up on this
- Basic spreadsheet analysis and programming skills <-- Definitely!
- Fundamentals of civil engineering analysis and design <-- Important for a couple homework problems
Student/Course Learning Outcomes
After successful completion of this course, students should be able to:
- Apply the systems-level approach to the analysis and design of civil infrastructure (systems)
- Formulate linear models of applications in civil engineering systems
- Solve linear models of applications in civil engineering systems
- Systematically evaluate design options involving competing objectives
Topics Covered
- Basic Systems Analysis Concepts and Solvers
- Civil Engineering Analysis and Design of Linear Systems
- The Simplex Algorithm for Solving Linear Programs
- Analysis and Design under Competing/Conflicting Objectives
- Network Flow Models
- Models with Integer Solutions
- Civil Engineering Analysis and Design of Non-linear Systems
Design Content
This course incorporates design in the following manner:
Approach
Utilize the systems analysis tools covered in this course and apply them to analyze and design civil engineering systems.
Lectures
Many lectures cover, or lead to lectures that cover, the design of a civil engineering system. This course focuses heavily on mathematically modeling design problems, rather than solving optimization problems. Since this is a civil (and environmental) engineering course and civil/environmental engineers deal with design problems in the public sphere that almost always involve multiple, conflicting and/or competing objectives, several of the examples in this course will cover multi-objective problems.
Term Project
This course involves a quarter-long group design project. Student groups will act as consultants and prepare a report to their client that defines the design problem and presents the mathematical formulation of the design problem. More importantly, the report will describe and discuss multiple design alternatives to the client that are pareto-optimal and recommend one of the pareto-optimal design alternatives to the client.
***The term project in 2022 will focus on the design and analysis of the logistics components of the COVID-19 vaccine supply chain.
Course Format
The course will be taught remotely/virtually during the first two weeks of the quarter, per University policy. These lectures will be synchronous but recorded and posted to Canvas.
Once allowed, we will return to a physical classroom. These class sessions will also be recorded and posted to Canvas.
One important thing to note is that my daughter started daycare in October and our family has been sick a lot between October and December. If this continues, I will probably have to move some of our classes to Zoom, in order to be safe.
Textbooks
This course covers one of the most important and ubiquitous topics across the engineering sciences, known as mathematical programming, which includes linear programming and nonlinear program with continuous, discrete, or mixed (i.e., continuous and mixed) variables. As such, there are dozens of high-quality textbooks (and online courses) that cover the requisite analytical methods in mathematical programming. The main differentiating feature of CEE111 is the emphasis on the design of civil engineering systems and also multi-objective math programs.
I have placed the following textbook in the bookstore.
Civil and Environmental Systems Engineering, 2nd Edition. Charles S. Revelle, Earl Whitlatch, Jeff Wright. Pearson Publishing, 2003.
This is the only book that focuses on ‘civil engineering systems’ in terms of example problems. The textbook also emphasizes multi-objective optimization topics that are crucial to the design of civil engineering systems.
Another good textbook is the one below.
Operations Research Models and Methods, 1st Edition. Paul A. Jensen, Jonathan F. Bard. John Wiley & Sons Inc., 2003
I find this textbook provides the most straightforward, comprehensive, and clear description of the systems analysis tools covered in this course, outside of multi-objective optimization.
In addition to these two textbooks, the library has several books on mathematical programming, optimization, systems analysis, linear programming, etc. that you can use as references for this course. I am also placing the pdf copy of several textbooks on Canvas:
- Chong, Edwin KP, and Stanislaw H. Zak. An introduction to optimization. Vol. 76. John Wiley & Sons, 2013.
- This textbook provides 5 introductory chapters related to Math 3A and 3D including vector spaces and matrices, matrix transformations, geometry, and calculus.
- Bradley, Stephen P., Arnoldo C. Hax, and Thomas L. Magnanti. "Applied mathematical programming." (1977).
- This book is available online: http://web.mit.edu/15.053/www/AMP.htm
- Carter, Michael W., and Camille C. Price. Operations research: a practical introduction. Crc Press, 2017.
- Bierlaire, Michel. Optimization: Principles and Algorithms. EPFL Press. 2018
Contact
The best way to contact me is via email. My email address is hylandm@uci.edu. I will try to answer emails within 24 hours. Please feel free to follow-up if I do not respond to your email within 36 hours.
It would be helpful if the subject line of your emails began with [CEE 111].
Academic Integrity
All students are expected to adhere to the UCI Academic Dishonesty Policies. For more information, please visit https://aisc.uci.edu/students/academic-integrity/index.php.
Disability Services
University of California, Irvine is committed to providing a barrier free environment for persons with documented disabilities. If you have a disability and feel you need accommodations in this course, please contact the Disability Services Center, located in Building 313, or apply for services online at www.dsc.uci.edu. DSC approved accommodations will be provided for students who present a Faculty Notification Letter from the DSC.
Student Evaluation
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Homework Assignments |
10% |
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Design Project |
30% |
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Lab Assignments |
10% |
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Midterm Exam |
20% |
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Final Exam |
30% |
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Total |
100% |
These are subject to change, if the midterm or final exam must be taken remotely.
Homework Assignments
Homework assignments aim to cement the material covered in the readings and during lecture. The homework assignments involve conceptualizing, modeling, and (sometimes) solving systems analysis and design problems.
Homework assignments make up a small percentage of your final grade—only 10%. Some of the problems are quite challenging. The students who work diligently to solve these problems will be in good shape for the exams as well as the final project.
The number of homework assignments is expected to be 4 or 5. All homework assignments will be group homework assignments. You can either choose two partners or let me put you in a group of three. Each group will submit one assignment with their group number and the names of all team members written at the top of the assignment.
Assignments are expected to be formatted professionally. Ideally, the assignments will be typed. However, assignments written legibly will be accepted.
Regarding late submissions, if an assignment is not submitted before the deadline, your group loses 10 points. For every 12 hours the assignment is late, your group will lose 10 additional points. After 36 hours, assignments will not be accepted.
Assignments must be submitted via Canvas.
Design Project
This course involves a quarter-long group design project. Student groups will act as consultants and prepare a report to their client that defines the design problem and presents the mathematical formulation of the design problem. More importantly, the report will describe and discuss multiple design alternatives to the client that are pareto-optimal and recommend one of the pareto-optimal design alternatives to the client.
Design projects will also be completed in groups, specifically, the same groups as for homework assignments.
Your final report will include all previous deliverables in a coherent technical report.
Lab Assignments
There will be a few lab assignments throughout the quarter. These assignments will be graded for ‘completeness’ not ‘correctness’. That is, you need to make an honest effort on each question in the lab assignments. If you do, you will receive full credit.
Midterm and Final Exams
The midterm and final exams are designed to evaluate students based on their (1) understanding of and (2) ability to apply the systems analysis methods and design principles covered during lectures, lab sessions, and homework assignments.
Course Summary:
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