ENGM 184 INTRODUCTION TO OPTIMIZATION METHODS Fall 2023

ENGM 184
INTRODUCTION TO OPTIMIZATION METHODS
Fall 2023
Wednesday 1:00 - 2:50p EDT; Friday 11:30a-1:20p EDT
ECSC 008
Instructor: Eric Bish, Ph.D. (Murdough 325, Office hours - by appointment)
Lead Teaching Assistants: Anirudh Ganesh Sriraam, Tabish Javed Warsi
Teaching Assistants (graders): Deepika Reddy Baddam, Kajal Singh
Weekly TA office hours: TBD

Description

Optimization deals with design and operating decisions for complex systems, and this course provides the student with a collection of optimization modeling and solution tools that can be useful in a variety of industries and functions. The main topics covered are linear programming, nonlinear programming, integer programming, and heuristic programming. This course emphasizes model building rather than algorithms. Spreadsheet models will be the primary vehicles for building and solving optimization models, with solutions generated using Excel's Solver.

Principal Learning Objectives

  • Recognize existing applications of optimization techniques. 
  • Associate particular optimization models with types of decision problems.• Translate a description of a decision problem into a valid optimization model by identifying variables, constraints, and an objective function.
  • Express a given optimization model in an Excel spreadsheet, structured for use with Solver.
  • Assess the validity of a particular optimization model.
  • Find solutions to optimization problems using the most appropriate settings in Solver.
  • Interpret the meaning of optimization results as they apply to the motivating problem.

Text

Kenneth R. Baker, Optimization Modeling with Spreadsheets (Third Edition), John Wiley & Sons (2016).

Access to an electronic copy of the text can be found here. A hard copy of the text is on course reserve in the Feldberg-Tuck library.

The website for the text is http://faculty.tuck.dartmouth.edu/optimization-modelingthird/

This website contains some supplementary exercises which will be assigned from time to

time.

Class Format

Lectures and activities will be in-person. All participants are expected to understand and follow Dartmouth COVID-19 campus policies at all times.

Attendance

You are expected to attend class in person unless you have made alternative arrangements with the instructor in advance due to illness, medical reasons, or other unavoidable circumstances. For the health and safety of our class community, please do not attend class when you are sick or when you have been instructed by Student Health Services to stay home. You will be able to view class recordings in Canvas if you are unable to attend.

Technology Expectations

It is required that all students be able to participate in Zoom video conference calls with their instructor, TA, or teammates as needed. All assignments will be submitted electronically on Canvas as Excel spreadsheets. Let your instructor know what barriers you may have to accessing the necessary technology as soon as possible.

Digital Citizenship

Notification to Students

Notification to Faculty

Course Assignments (See Course Summary below)

Grading

Homework assignments (20%) must be submitted by midnight on the due date in electronic form on Canvas. Grades for late assignments will be reduced 20% for each day late.

Unannounced quizzes (5%) will be given in class. These are primarily intended to assess whether the student is engaged and following course material.

There will be a mid-term exam (35%) and a final exam (40%). These are openbook/open-notes exams. Material from the course folder is permitted, except files that originate with other students.

Statement of Academic Integrity

This course adheres to all Dartmouth policies pertaining to academic integrity including, but not limited to, the Academic Honor Principle.

Honor Principle

Students may work together on homework assignments, at least to the stage of reviewing class materials, agreeing on the general concepts, and sketching the general structure of a model. Most important here is the individual student's eventual understanding of the assignment and its solution. The Honor Principle applies to homework in the following way. Students must submit their own work, and that work should represent their own understanding of how to fulfill the assignment. Students must state what sources they have consulted and with whom they have collaborated or received help. Any copying (electronic or otherwise) of another person's work, in whole or in part, is a violation of the Honor Principle.

Students who have questions as to whether some action would be acceptable under the Academic Honor Code should speak to the instructor.

Instructional Resources

There are 4 teaching assistants (TAs) for the course, 2 lead TAs and 2 assignment graders.

Student Accessibility Services

Students with disabilities who may need disability-related academic adjustments and services for this course are encouraged to see me privately as early in the term as possible. Students requiring disability- related academic adjustments and services must consult the Student Accessibility Services office (Carson Hall, Suite 125, 646-9900). Once SAS has authorized services, students must show the originally signed SAS Services and Consent Form and/or a letter on SAS letterhead to me. As a first step, if students have questions about whether they qualify to receive academic adjustments and services, they should contact the SAS office. All inquiries and discussions will remain confidential.

Religious Observances

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance that conflicts with your participation in the course, please meet with me before the end of the second week of the term to discuss appropriate accommodations.

2023-10-14