Este entrenamiento ayuda a familiarizarse con los solvers de IBM CPLEX y GUROBI, adquirir los conocimientos necesarios para modelar y resolver problemas de optimización. Los participantes serán introducidos a la optimización matemática, es decir, algoritmos de modelado y solución, trabajando con ILOG CPLEX, GUROBI y Python. Varios ejercicios ayudaran a consolidar el contenido del curso. Después del curso usted estará en capacidad de modelar problemas de decisión de negocio con los elementos básicos de los modelos de optimización: índices, datos, parámetros, variables, restricciones y funciones objetivo.
Dirigido a: Científicos de datos, analistas, especialistas, desarrolladores, profesionales de optimización o personas interesadas en adquirir habilidades de modelaje matemático para resolver problemas de optimización de negocios usando IBM CPLEX o GUROBI.
In this example, you will learn how to define and solve a covering-type problem, namely, how to configure a network of cell towers to provide signal coverage to the largest number of people.
Companies across almost every industry are looking to optimize their marketing campaigns. In this Jupyter Notebook, we will explore a marketing campaign optimization problem that is common in the banking and financial services industry, which involves determining which products to offer to individual customers in order to maximize total expected profit while satisfying various business constraints.
This model is an example of the classic Markowitz portfolio selection optimization model: to allocate investments to minimize risk. This is best suited to a matrix formulation, so we use the Gurobi Python matrix interface. The basic model is fairly simple, so we also solve it parametrically to find the efficient frontier.
This model is an example of a supply network design problem. Given a set of factories, depots, and customers, the goal is to determine how to satisfy customer demand while minimizing shipping costs. This problem can be regarded as one of finding the minimum cost flow through a network.
Bienvenido a IBM® Decision Optimization Modeling con OPL y CPLEX en IBM® Decision Optimization on Cloud (DOcplexcloud) Esta biblioteca contiene varios ejemplos de modelos con diferentes tipos de archivos. Breves descripciones de estos modelos se proporcionan más adelante en este archivo.
IBM Decision Optimization on Cloud (DOcplexcloud) enables you to solve optimization problems on the cloud without installing or configuring a solver. We handle the connection so that you can jump into coding faster. This documentation describes the R API to access the service.
Welcome to IBM® Decision Optimization Modeling with OPL and CP Optimizer on DOcplexcloud. This library contains various model examples with different file types. For each sample you can find here.
Welcome to the IBM® OPL connector for Python. Licensed under the Apache License v2.0. With this library, you can quickly and easily add the power of optimization to your Python application. You can model your problems by using the OPL language and IDE, and integrate it in Python via Python/pandas/sql alchemy inputs/outputs.
IBM Decision Optimization on Cloud (DOcplexcloud) allows you to solve optimization problems on the cloud without installing or configuring a solver. We handle the connection so that you can jump into coding faster. This documentation describes the IBM DOcplexcloud Python Client samples.
This sample shows how you can leverage the power of the DOcplexcloud service directly from your Microsoft Excel workbook.
This DO for DSX example introduces basic concepts of model builder and dashboard using a python CPLEX model. It solves a simple Marketing Campaigns optimization example.
This DO for DSX example shows how to use optimization optimization position of SKUs on shelfs according to demand, availability and constraints. This example is migrated from an olde Decision Optimization Center demo.
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Sí, contamos con un equipo de profesionales en diferentes áreas que nos permite desarrollar proyectos con diferentes alcances y complejidades.
Nuestra misión es ayudar a nuestros clientes a generar valor en sus operaciones a través de la optimización de las decisiones soportadas en modelos matemáticos.
Nuestros desarrollos de soluciones de optimización son realizados utilizando Python como lenguaje de programación principal.