# Optimization with Excel: Operations Research without Coding 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)

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

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
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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