Operations Research (1): Models and Applications

  • 4.8
Approx. 11 hours to complete

Course Summary

This course introduces students to the principles of operations research and how to apply them to real-world problems through modeling and optimization techniques.

Key Learning Points

  • Learn how to model and solve a wide range of real-world problems using operations research techniques
  • Understand the importance of optimization in the decision-making process
  • Develop critical thinking skills to analyze and interpret results

Related Topics for further study


Learning Outcomes

  • Develop the ability to model and solve real-world problems using operations research techniques
  • Understand the importance of optimization in the decision-making process
  • Develop critical thinking skills to analyze and interpret results

Prerequisites or good to have knowledge before taking this course

  • Basic knowledge of calculus and linear algebra
  • Familiarity with Excel or another spreadsheet software

Course Difficulty Level

Intermediate

Course Format

  • Online
  • Self-paced

Similar Courses

  • Introduction to Linear Models and Matrix Algebra
  • Supply Chain Analytics
  • Data-Driven Decision Making

Related Education Paths


Related Books

Description

Operations Research (OR) is a field in which people use mathematical and engineering methods to study optimization problems in Business and Management, Economics, Computer Science, Civil Engineering, Industrial Engineering, etc. This course introduces frameworks and ideas about various types of optimization problems in the business world. In particular, we focus on how to formulate real business problems into mathematical models that can be solved by computers.

Knowledge

  • Formulate different types of mathematical models to tackle optimizationproblems with business applications.
  • Technically, the concepts and applicationsof Linear Programming, Integer Programming, and Nonlinear Programming will be delivered.
  • Solve an optimization problem with oneof the most accessible software: Microsoft Excel.

Outline

  • Course Overview
  • Prelude
  • 1-1: Motivation.
  • 1-2: Business analytics.
  • 1-3: Mathematical programming.
  • 1-4: History.
  • 1-5: Preview for this course.
  • NTU MOOC course information
  • Quiz for Week 1
  • Linear Programming
  • 2-0: Opening.
  • 2-1: Introduction.
  • 2-2: Elements of a mathematical program (1).
  • 2-3: Elements of a mathematical program (2).
  • 2-4: Linear programming.
  • 2-5: Graphical approach.
  • 2-6: Three types of LPs.
  • 2-7: Simple LP formulation - Product mix.
  • 2-8: Simple LP formulation - Production and inventory.
  • 2-9: Simple LP formulation - Personnel scheduling.
  • 2-10: Compact LP formulation - Production and Inventory.
  • 2-11: Compact LP formulation – Product mix.
  • 2-12: Computers – The Solver add-in and Example 1 – producing desks and tables.
  • 2-13: Computers – Example 2: personnel scheduling.
  • 2-14: Closing remarks.
  • Quiz for Week 2
  • Integer Programming
  • 3-0: Opening.
  • 3-1: Introduction.
  • 3-2: IP formulation (1).
  • 3-3: IP formulation (2).
  • 3-4: Facility location – Overview.
  • 3-5: Facility location – Covering.
  • 3-6: Facility location - UFL.
  • 3-7: Machine scheduling - Overview.
  • 3-8: Machine scheduling - Completion time minimization.
  • 3-9: Machine scheduling - Makespan minimization.
  • 3-10: Traveling salesperson problem - Basics.
  • 3-11: Traveling salesperson problem - Subtour elimination.
  • 3-12: Computers – Example 1 – personnel scheduling.
  • 3-13: Computers – Example 2 – facility location.
  • 3-14: Closing remarks.
  • Quiz for Week 3
  • Nonlinear programming
  • 4-0: Opening.
  • 4-1: Introduction.
  • 4-2: The EOQ problem.
  • 4-3: Formulating the EOQ model.
  • 4-4: The portfolio optimization problem.
  • 4-5: Portfolio optimization.
  • 4-6: Linearizing an absolute value function.
  • 4-7: Linearizing max_min functions.
  • 4-8: Linearizing products 1A.
  • 4-9: Linearizing products 1B 1C and 1D.
  • 4-10: Linearizing products 2A.
  • 4-11: Linearizing products 2B, 2C, and 2D.
  • 4-12: Remarks - why linearization.
  • 4-13: Computers – Portfolio optimization problem.
  • 4-14: Closing remarks.
  • Quiz for Week 4
  • Case Study: Personnel Scheduling
  • 5-0: Opening.
  • 5-1: Background and motivation.
  • 5-2: Research objective.
  • 5-3: Problem description - objective.
  • 5-4: Problem description - constraints.
  • 5-5: Model formulation - objective.
  • 5-6: Model formulation - constraints.
  • 5-7: Results.
  • 5-8: Closing remarks.
  • Quiz for Week 5
  • Course Summary and Future Directions
  • 6-1: Review for this course.
  • 6-2: Preview for the next course.
  • A story that never ends.
  • Quiz for Week 6

Summary of User Reviews

Operations Research Modeling course on Coursera has received positive reviews for its comprehensive content and engaging teaching style. This course covers various modeling techniques and their applications in operations research. A key aspect that many users found good is the practical approach of the course that enables them to apply the learned concepts in real-world scenarios.

Pros from User Reviews

  • Comprehensive content covering various modeling techniques
  • Engaging teaching style with practical examples
  • Well-structured course with clear explanations
  • Useful assignments and quizzes to reinforce concepts
  • Helpful instructor support and community forum

Cons from User Reviews

  • Some users found the course challenging and time-consuming
  • Limited coverage of advanced topics
  • Lack of flexibility in the course schedule
  • Some technical issues with the platform
  • Not suitable for beginners without prior knowledge in operations research
English
Available now
Approx. 11 hours to complete
孔令傑 (Ling-Chieh Kung)
National Taiwan University
Coursera
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