Oct

12

2021

Optimization with Excel: Operations Research without Coding

supnatural 12 Oct 2021 12:18 LEARNING » e-learning - Tutorial

Optimization with Excel: Operations Research without Coding
Optimization with Excel: Operations Research without Coding
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 80 lectures (9h 58m) | Size: 5.22 GB

Solve problems with Gurobi, CBC, IPOPT... using linear programming, nonlinear, genetic algorithm... without coding
What you'll learn:
Solve optimization problems in a very easy way! Using the Excel along with well-known solvers without coding
Nice introduction on mathematical modeling
Gurobi, CBC, IPOPT, Bonmin, Couenne
LP, MILP, NLP, MILNP
Genetic Algorithm and Vehicle Routing Problem (VRPTW)

Requirements
Some knowledge in Excel, however, I provide a fast introduction using the basic formulas you need for the course.
Why and where to use optimization
You do NOT need to know how to code in this course (we are NOT gonna use any programming language)

Description
Operational planning and long term planning for companies are more complex in recent years. Information changes fast, and the decision making is a hard task. Therefore, optimization algorithms (operations research) are used to find optimal solutions for these problems. Professionals in this field are one of the most valued in the market.

And if you do not known how to code and/or if you wish to solve optimization problems using Excel, this is a perfect course for you.

In this course you will learn what is necessary to solve problems applying (without any coding):

Linear Programming (LP)

Mixed-Integer Linear Programming (MILP)

NonLinear Programming (NLP)

Mixed-Integer Linear Programming (MINLP)

Genetic Algorithm (GA)

And how to solve Vehicle Routing Problems with Time Window (VRPTW)

The following solvers will be explored: Gurobi - CBC - IPOPT - Bonmin - Couenne

We will also use CPLEX, but a limited version from NEOS server.

Also, I provide workbooks for you that will facilitate to solve these problems. GA and VRPTW will be solved using workbooks that are very easy to work with.

The course has a nice introduction on mathematical modeling and the main formulas from Excel. Thus, you can easily follow the classes.

In addition to the classes and exercises, the following problems will be solved step by step:

Route optimization problem

Maximize the revenue in a rental car store

Maintenance planning problem

Optimal Power Flow: Electrical Systems

Many other examples, some simple, some complexes, including summations and many constraints.

You should NOT solve optimization problems in Excel for:

Complex problems that requires decompositions and iterations. Since we do not use any programming language in the course, our approach would not be recommended to solve problems that requires iterations, such as Benders.

Operational problems for real-time execution.

Large problems that require fast solutions. The approach from the course does not have a limitation, but large problems may take a while to be converted from the Excel's formulas to the solver.

Solve multi-objective problems.

Attention:

The approach in this course is NOT using the standard solver from Excel, our approach here has NO limitations on the number of variables or constraints.

I do NOT show you how to install Excel. But I teach how to install the required tools.

To follow the course you will need Excel installed on your computer. Moreover, the tools from the courses have been tested in Windows and MAC only.

The classes use examples that are created step by step, from the business concept to the resolution.

I hope this course can help you in your carrier. Yet, you will receive a certification from Udemy.

Operations Research | Operational Research | Mathematical Optimization

Who this course is for
Undergrad, graduation, master program, and doctorate students
If you want or need to solve optimization problems but is not very good with programming languages
People interested in solving complex problems

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