Gurobi Optimization

[Juvencio Parise]

Withour powerful algorithms, you can add complexity to your model to better represent the real world, and still solve your model within the available time. The performance gap grows as model size and difficulty increase. Gurobi has a history of making continual improvements across a range of problem types, with a more than 75x speedup on MILP since version 1.1. Gurobi is tuned to optimize performance over a wide range of instances. Gurobi is tested thoroughly for numerical stability and correctness using an internal library of over 10,000 models from industry and academia.

Deploy your model where and how you want. One user can solve a single model on one machine, or many users can solve multiple models using many machines. You can also solve models locally or on an internal or public cloud. Each license can be used for both development and deployment. Each license can run multiple applications. Licenses can be transferred from consulting developer to end user. License grows as chip capabilities grow.

Our Python API includes higher-level modeling constructs that make it easier to build optimization models. Choose from Anaconda Python distributions with pre-built libraries to support application development, Spyder for graphical development, and Jupyter for notebook-style development. Python interactive interface for powerful prototyping and quick testing Language extensions for easier coding of a model Documented best practices to get you started quickly Pre-built Python libraries support full application development Python Matrix API for matrix-oriented modeling using NumPy or SciPy matrices

When confronted with the task of choosing parameter values that might lead to better performance on a model, the long list of Gurobi parameters may seem intimidating. To simplify the process, we include a simple automated parameter tuning tool

The Gurobi Optimizer is a state-of-the-art solver for mathematical programming. The solvers in the Gurobi Optimizer were designed from the ground up to exploit modern architectures and multicore processors, using the most advanced implementations of the latest algorithms.

The Gurobi interface for MATLAB allows users to build an optimization model, pass the model to Gurobi, and obtain the optimization result, all from within the MATLAB environment. It can be used to solve optimization problems using any of the following forms: linear constraints, bound constraints, integrality constraints, cone constraints, and quadratic constraints.

I'm working on solve an optimization model. Linear decision rule is also used. Generally, when 2nd-stage variable is adapted to more uncertainty data, the results are expected to be better(monotonically increasing or decreasing). However, based on my results, the results show up and down as I increase the number of uncertainty data in adaptation. Could anyone explain about it? Or where am I wrong? Thank you!

Gurobi strives to help companies make better decisions through the use of prescriptive analytics. We provide the best math programming solver, tools for distributed optimization, optimization in the cloud, and outstanding support. We are committed to improving our solver performance and developing tools to help you use Gurobi with more ease. Founded in 2008 by arguably the most experienced and respected team in optimization circles, we have successfully expanded to serving over 2,500 companies from a wide range of industries, by way of providing the right mix of advanced developments and technologies, world-class support and flexible licensing.

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Sidley represented Gurobi Optimization (Gurobi), a provider of decision intelligence technology and portfolio company of Thompson Street Capital Partners, in connection with its acquisition of October Sky, a Japan-based provider of mathematical optimization services and custom solutions. The combined forces of Gurobi and October Sky will empower customers across industries to tackle complex business challenges efficiently and effectively. As the sole distributor of Gurobi in Japan, October Sky has fostered a strong and collaborative partnership with Gurobi for more than 13 years.

In recent years, machine learning has become a prevalent tool to

provide predictive models in many applications. In this talk, we are

interested in using such predictors to model relationships between

variables of an optimization model in Gurobi. For example, a

regression model may predict the demand of certain products as a

function of their prices and marketing budgets among other features.

We are interested in being able to build optimization models that

embed the regression so that the inputs of the regression are decision

variables, and the predicted demand can be satisfied.

We propose a python package that aims at making it easy to insert

regression models trained by popular frameworks (e.g., scikit-learn,

Keras, PyTorch) into a Gurobi model. The regression model may be a

linear or logistic regression, a neural network, or based on decision

trees. The resulting optimization models are often hard to solve with

the current technology. We also present computational results on

improvements that are specifically targeted for those types of models.

In particular, we consider optimization models with embedded neural

networks.

N2 - The primary drivers for buying a ship from a certain yard are price, delivery time and quality. In order to decrease construction time and costs, shipbuilding companies are exploring the development of product-families to include family wide modularity and cross family standardization. Standardization is the use of identical components across multiple products, while modularity combines parts to create 'building-blocks'. This creates an opportunity for less inventory, a more efficient supply chain and shorter delivery times. Considering a network of suppliers and shipyards, the shipbuilder has to answer the following question: Which components and pre-assembled modules should be available in which inventory? Since the exact ship orders are not known, this can be seen as an optimization problem with uncertainty. To solve it, it is formulated as an integer linear program (ILP), and to handle the uncertainty, the Sampling Average Approximation (SAA) method is used. Several smaller instances are solved to optimality by Gurobi optimization software and the performance of this approach is evaluated along with the convergence of the SAA method. The results show convergence of the SAA method although only relatively small instances can be solved to optimality by the ILP.

AB - The primary drivers for buying a ship from a certain yard are price, delivery time and quality. In order to decrease construction time and costs, shipbuilding companies are exploring the development of product-families to include family wide modularity and cross family standardization. Standardization is the use of identical components across multiple products, while modularity combines parts to create 'building-blocks'. This creates an opportunity for less inventory, a more efficient supply chain and shorter delivery times. Considering a network of suppliers and shipyards, the shipbuilder has to answer the following question: Which components and pre-assembled modules should be available in which inventory? Since the exact ship orders are not known, this can be seen as an optimization problem with uncertainty. To solve it, it is formulated as an integer linear program (ILP), and to handle the uncertainty, the Sampling Average Approximation (SAA) method is used. Several smaller instances are solved to optimality by Gurobi optimization software and the performance of this approach is evaluated along with the convergence of the SAA method. The results show convergence of the SAA method although only relatively small instances can be solved to optimality by the ILP.

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