Optimization with Python: Solve Operations Research Problems

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.In this course you will learn what is necessary to solve problems applying Mathematical Optimization and Metaheuristics:Linear Programming (LP)Mixed-Integer Linear Programming (MILP)NonLinear Programming (NLP)Mixed-Integer Linear Programming (MINLP)Genetic Algorithm (GA)Multi-Objective Optimization Problems with NSGA-II (an introduction)Particle Swarm (PSO)Constraint Programming (CP)Second-Order Cone Programming (SCOP)NonConvex Quadratic Programming (QP)The following solvers and frameworks will be explored:Solvers: CPLEX – Gurobi – GLPK – CBC – IPOPT – Couenne – SCIP Frameworks: Pyomo – Or-Tools – PuLP – PymooSame Packages and tools: Geneticalgorithm – Pyswarm – Numpy – Pandas – MatplotLib – Spyder – Jupyter NotebookMoreover, you will learn how to apply some linearization techniques when using binary variables.In addition to the classes and exercises, the following problems will be solved step by step:Optimization on how to install a fence in a gardenRoute optimization problemMaximize the revenue in a rental car storeOptimal Power Flow: Electrical SystemsMany other examples, some simple, some comp