
learn to solve optimization problems using Excel solver while recognizing its limitations; identify when to switch to programming languages or other solvers for complex, large, or integrated problems.
Learn to replicate and lock formulas in Excel by using dollar signs to fix ranges while dragging, understanding how to lock rows and columns with the F key.
Model a three-machine production problem to minimize total cost by assigning integer products P_A, P_B, P_C to meet a demand of 10000, using a balance constraint and machine-on costs.
Maximize revenue by selecting a subset of five construction projects under a five-team limit using binary decision variables. The example shows choosing projects B and C under this constraint.
Explore linear programming basics with two variables, defining a linear objective and linear constraints within a feasible domain. Solve these problems with a linear solver or Excel, without coding.
Learn to troubleshoot open solver issues in Excel with an alternative approach: open Excel, use the data menu and add-ins, then browse to the open solver file from the zip.
Install and configure Gurobi for optimization workflows, including obtaining an academic license, downloading and installing the software, restarting, and setting the GRB_LICENSE_FILE environment variable.
Apply the base case MILP by converting a linear program to mixed integer with X as integer, defining constraints, and solving using open solver and CBC.
Manipulate template variables in Excel by adding, deleting, or renaming indices and creating new variables with copy-paste and right-click insertion.
optimize job scheduling with excel by formulating a one day assignment problem for 100 jobs over 10 days, maximizing revenue while respecting eight-hour daily limits and travel time.
Solve a 100-job, 10-day scheduling problem by building variables X and day indices in an Excel template, with parameters, durations, and input data.
Create a binary integer programming model in Excel to maximize revenue by selecting 100 jobs across 10 days, verify constraints, and obtain the optimal solution with the CBC solver.
Explore genetic algorithms and evolutionary methods for optimization in Excel without coding. Understand fitness functions, chromosomes, genes, initial population, mutation, crossover, and generations that drive search for the best solution.
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