Simranjit Kaur

Hi Fei Wang, The majority of the time spent in adding constraints to your model is perhaps consumed in the for loops like the one below: for k in range(self.r): model.addConstr( w[E_index_dict[(...

Hi Iason, You can start by first determining if your model is infeasible or unbounded. Please see the article How do I resolve the error "Model is infeasible or unbounded"? for more details. If the...

At present, obtaining n best solutions for each scenario in a multi-scenario model through solution pools is not supported.

Hi Felix, Thanks for letting us know about the missing documentation. The function gurobi_presolve creates presolved version of the initial model. If you save the presolved model and optimize it se...

Hi Asrat, You may find the example of a two-stage stochastic model implemented in Gurobi given here useful. Regards, Simran

Hi Fei Wang, Can you please share an example code showing how you build your model? The article How do I improve the time to build the model may be helpful for you. It contains several tips to spee...

Hi Jizhou Lu, Let me first touch on how the solution pool is populated and updated during the optimization process. Gurobi will keep adding solutions to the solution pool until it reaches the maxim...

Hi Jirayu, One can only sum constants, Var objects, LinExpr objects, or QuadExpr objects using the quicksum() method. You are getting the error TypeError: unsupported operand type(s) for +=: 'gurob...

Hi Jirayu, The error you are seeing is coming from another part of your code. First, let's note that gp.max_ method takes a variable, a list of variables, or a tupledict of variables as arguments. ...

Hi, To find the maximum completion time for each machine, you can use the following code: Max_Completion_Time = m.addVars(Machines, vtype=GRB.CONTINUOUS, name="Max_Completion_Time")completion_ti...