Gurobi Optimizer

What do you like best about Gurobi Optimizer?

Ability to solve wide range of optimization problems such as Linear Programming, Mixed-Integer Linear Programming, Convex Non-Linear Programming,etc. which makes any company able to solve complex supply chain problems

What do you dislike about Gurobi Optimizer?

Its inability to solve non-convex optimization problems, prerequiste of mathemtical modelling backgroung leading to intensive training requirements, expensive commerical license

What problems is Gurobi Optimizer solving and how is that benefiting you?

In our company, we use it for solving complex supply chain problems such as facility location problem, vehicle routing problem and network scheduling problems which use the baseline of linear and mixed-integer linear programming problems

What do you like best about Gurobi Optimizer?

If you are a cplex user, you can easily use gurobi package

What do you dislike about Gurobi Optimizer?

Some functions show low performance such as the function related to the hint of opt solution.

Recommendations to others considering Gurobi Optimizer:

It is not an awesome but a good package.

What problems is Gurobi Optimizer solving and how is that benefiting you?

Plan and schedule related to SCM

What do you like best about Gurobi Optimizer?

It is convenient to use with a great group of community and regular updates. I have had experience using Gurobi Optimizer in the process of learning optimization (from the most basic integer optimization to linear optimization, and more complex ones), and used it to solve many homework problems and course project.

Later in the stage, I got the chance to work on a data science project that involves intensive work on optimization and got to again use Gurobi Optimizer. In most of the cases, linear optimization or even integer optimization is already good enough to solve the real business issues. It happened rarely that more complicated models are required, but they could also be converted to linear ones with some tricks or so.

Gurobi Optimizer is very time-efficiently that it can solve an optimization problem with thousands of variables and tens of thousands of constraints in just a few minutes. This is quite essential in practice because it can not only make the building process much easier (you can do multiple trial and error in a relatively short time period and gives yourself less time pressure..) but also make your algorithm to be deployable in reality. especially in the case that the company needs to get some optimal decision in a timely manner (say need to run the optimization every week, every day, or even every hour)!

What do you dislike about Gurobi Optimizer?

The syntex could have been more straightforward.

Recommendations to others considering Gurobi Optimizer:

Definitely go to the Gurobi Optimizer documentation, you will learn how to play with it from there. Gurobi Optimizer is also very time-efficient to run and apply in practice.

What problems is Gurobi Optimizer solving and how is that benefiting you?

Assortment optimization could be solved with Gurobi Optimizer. It runs efficiently comparing to other solvers.

What do you like best about Gurobi Optimizer?

Gurobi APIs for Python is easy to implement. The performance on MILP and Quadratic Programming models are very good. There are performance improvements with the newer version 9.1.

What do you dislike about Gurobi Optimizer?

Need to convert non-linear problems into a Linear or Quadratic formulation. Complex non-linear models might need decomposition techniques to overcome scaling issues for problems of bigger size.

What problems is Gurobi Optimizer solving and how is that benefiting you?

I use Gurobi Optimizer API for python for Assortment Optimisation problems with linear as well as non-linear objective functions. For the later I've been exploring various decomposition techniques to help scale for larger sized models. The API is well documented, easy to use and very flexible. The Gurobi support is responsive whenever we have any questions. Their cloud solutions are good for R&D purpose too, e.g. when you want to test big models on big machines without the hassle of spinning and installing on a big VMs.

What do you like best about Gurobi Optimizer?

Ability to solve diverse

optimization problems like linear programming (LP),

quadratic programming (QP), mixed-integer linear

programming solver (MILP), mixed-integer programming

(MIP) very quickly and in an efficient manner

What do you dislike about Gurobi Optimizer?

I didn’t find any. I was fully satisfied

Recommendations to others considering Gurobi Optimizer:

I found the tool very useful which I used to work on my master thesis. I used this tool together with YALMIP ( a toolbox in MATLAB) as an external solver to solve diverse optimization problem like LP, MILP etc. The simulation was quick and I obtained intended results. I am very happy with the tool and strongly recommend this.

What problems is Gurobi Optimizer solving and how is that benefiting you?

While working with my masters thesis in Electrical power systems engineering field, I used a Gurobi together with YALMIP ( a Matlab toolbox) to solve optimization problems (e.g. Linear programming, mixed integer programming etc.). The simulation time was very quick and intended results were obtained.