How can Gurobi optimize your Operations Research project?

Gurobi can optimize your OR project by providing a powerful optimization solver with expert support and straightforward pricing. It offers the ability to generate globally optimal solutions, supports a wide range of problem types, and delivers unmatched performance, as shown by public benchmarks. Gurobi's optimizer software can be used to input complex business challenges as mathematical models and output detailed action plans. It also provides features such as infeasibility analysis, multi-objective optimization, multiple scenario analysis, parameter tuning, and solution pools. Additionally, Gurobi supports the modeling of problems in various forms, including LP, MILP, MIQP, QCP, and MIQCP, as well as SOCP and bi-linear problems.

The current softwares for optimizing problems in large dimensions are inefficient and do not have the ability to solve convex and nonlinear problems. Gurobi optimization software which is currently the newest and most powerful optimizer.

Gurobi is a very convenient tool for optimization/ programming. I love that I could integrate it's python package on Jupyter notebook for small scale project using an academic license. I would love to venture into it's application in my current field (healthcare).

the reference to parallelism is a bit misleading. MIPs do not parallelize well, and performance does not scale beyond 4 cores.

If you are a commercial customer, Gurobi's technical support is first-class. But also the non-commercial users can benefit from the Gurobi Support Forum, where questions are answered regularly.

Gurobi also has great documentation available online. As mentioned in the first section, it supports a variety of programming languages, making it friendly for a variety of developers.

Gurobi is easy to learn and use. - Documentation on their website, examples available on their GitHub repo, recorded training videos on YouTube, and free training that Gurobi delivers from time to time. - With few lines of code, you can fully define an optimization problem (objective function, variables, and constraints) - Gurobi is computationally efficient making it relatively easy to run the code in real time and quickly return optimal solution.

Gurobi very much leads the pack and is the preferred tool of choice for industrial giants such as SAP, airlines etc. If open source solvers such as pyomo, OR Tools aren’t enough, Gurobi should be looked at for sure.

Best way to start is reading the online documentation. It is usually very clear and concise. They provide tons of examples, wich makes lifes a lot easier.

In my experience, Gurobi stands out for its fast and efficient optimization solver, rivaling other software such as CPLEX and SCIP. The selection between them depends on the nature of the problem and user preferences.

I also appreciate the periodic webinars showing Gurobi's industrial applications. I don't know if they have a dedicated portal for open source projects for learning purpose

this list shouldinclude HiGHS for MIP solvers. there are also non-MIP solvers that offer significant advantages, for example for parallel, stochastic, and multi-objective optimization, for example InsideOpt Seeker

Example problems: -Assigning workers to tasks -Determining where to locate facilities to maximize coverage -Creating schedules for a sports league -Determining product portfolios -Routing problems -Inventory problems -Scheduling operating rooms -Creating schedules for workers

this fails to address the main shortcoming of MIP solvers, namely that they do not directly support modeling with uncertain, distributional data. in most applications, the data we feed into a model is estimated. by ignoring that fact, MIP solvers provide brittle solutions that are over-tailored to fit one point-estimated future. one can use stochastic optimization on top of mip, of course, but that does not scale to any meaningful number of scenarios. solvers like InsideOpt Seeker provide 1st class modeling concepts for stochastic data and lead to significantly (in excess of 20%) better operational results

There are plenty of use cases where mathematical optimization is the best way to tackle them, Some projects we have worked using GUROBI are: - Unit commitment - Concrete distribution - workforce optimization

Examples of Gurobi applications in OR include optimizing inventory, transportation, and production in supply chain management. In finance, it excels in portfolio optimization and risk management. For telecommunications, Gurobi ensures efficient network design, while in manufacturing, it optimizes facility location and capacity planning. In healthcare, Gurobi aids in resource allocation, and in agriculture, it optimizes crop yield planning. These examples illustrate Gurobi's versatile role in solving complex optimization challenges across multiple industries.

Take a look at Google’s OR Tools, Pyomo, solver.com etc. These are some good free solvers to leverage before exploring Gurobi

Excel is often overlooked in the realm of Operations Research (OR), but it's a surprisingly effective for solving simple Mixed Integer Programming (MIP) problems. Excel's Solver add-in, a linear and non-linear problem solving tool, is particularly useful for MIP. Solver uses a branch-and-bound algorithm for MIP, which is effective for a wide range of applications such as resource allocation, production scheduling, and logistics planning. While Excel may not match the capabilities of specialized packages like Gurobi for large-scale problems, its accessibility and ease of use make it an excellent choice for experimentation and simple applications in operations research.

It's good to know there are other easy to use OR tools. I have not heard of Google OR tools. Are these also downloadable software?