English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 10 Hours | 5.83 GB

Optimization with Gurobi, CBC, IPOPT. Using linear programming, nonlinear, genetic algorithm. In Excel, without coding

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.

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)

## Table of Contents

**Introduction**

1 Introduction

2 How to solve Optimization Problems and Limitations of using Excel

3 Preview of the course

**Introduction to Excel**

4 Excel – the basics

5 Sum, If, SumIf, SumIfs

6 SumProduct

7 SumProduct with Filters

8 Vlookup

9 Replicate and Lock Formulas

10 Limitations of the standard solver from Excel (we will not use this solver!)

**Introduction to Mathematical Modeling**

11 What is mathematical modeling

12 How we solve optimization problems

13 Type of variables and what is parameters, indexes and sets

14 Objective function and constraints

15 How to model

16 Example 1 – Investment Problem

17 Example 2 – Investment Problem, nonlinear

18 Example 3 – Cost of production

19 Example 4 – Routing problem

20 Example 5 – Team assignment in a construction company

21 Example 6 – Team assignment with condition

22 Example 7 – Job scheduling

23 Example 8 – Job scheduling with limit

24 References for VRPTW, Jobshop, and TSP

25 How to learn more

**Linear Programming (LP) and installation of what you need**

26 LP – Introduction

27 Installing OpenSolver

28 Issues with OpenSolver

29 LP – Example 1 – Base Case

30 LP – Example 2 – Power generation

31 LP – Example 3 – Power generation multiperiod

32 Installing Gurobi

33 Selecting different solvers

34 Formulas and limits for Excel

35 LP – Concepts

**Mixed-Integer Linear Programming (MILP)**

36 MILP – Introduction

37 MILP – Example 1 – Base Case

38 MILP – Example 2 – Job Scheduling

39 MILP – Example 3 – Routing Problem

40 MILP – Example 3 – Routing Problem – Solution

41 MILP – Example 4 – Large Routing Problem

42 MILP – Concepts

**Solver Parameters and Tips**

43 Defining parameters for the solver

44 How to speed up the construction of problem

45 See the progress of the solver

**Template**

46 The template [download]

47 Template – Working with variables

48 Template – Working with parameters

49 Template – Working with the objective function

50 Template – Working with constraints

51 Example – Job scheduling – 100 jobs in 10 days

52 Example – Job scheduling – 100 jobs in 10 days – Variables and parameters

53 Example – Job scheduling – 100 jobs in 10 days – Objective function

54 Example – Job scheduling – 100 jobs in 10 days – Constraints

55 Example – Job scheduling – 100 jobs in 10 days – Model and Solution

**NonLinear Programming (NLP)**

56 NLP – Introduction

57 NLP – Example 1 – Base Case

58 NLP – Example 2 – Cosines

59 NLP – Example 3 – Investment

60 NLP – Concepts

**Mixed-Integer NonLinear Programing (MINLP)**

61 MINLP – Introduction

62 MINLP – Example 1 – Base Case

63 MINLP – Example 2 – Production Cost

64 MINLP – Example 2 – Production Cost – Solution

**Genetic Algorithm (GA)**

65 GA – Introduction

66 GA – Example 1 – Base Case

67 GA – Example 2 – Production Cost

**Vehicle Routing Problem with Time Window (VRPTW)**

68 VRPTW – Introduction

69 VRPTW – Example

70 VRPTW – Processing Time Issues

**Practical Problems**

71 Introduction

72 A Revenue Problem – Modeling

73 A Revenue Problem – Solution

74 A Maintenance Planning Problem – Business Concept

75 A Maintenance Planning Problem – Modeling

76 A Maintenance Planning Problem – Solution

77 Optimal Power Flow – Business Concept

78 Optimal Power Flow – Modeling

79 Optimal Power Flow – Solution

**Congratulations and Keep Learning**

80 If you want, where could you learn a programming language to solve optimization

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