How to fix the root relaxation objective value so i can find the optimale solution.
Awaiting user inputHello everyone,
I have a question regarding the root relaxation objective value.
The problem I am trying to solve is an integer program(IP)(Bin packing problem). I modeled my problem as integer program and let the stateofart solver Gurobi to solve it.
I get the following results :
Reading time = 0.06 seconds
x8269: 833 rows, 8269 columns, 49453 nonzeros
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 2 physical cores, 4 logical processors, using up to 4 threads
Optimize a model with 833 rows, 8269 columns and 49453 nonzeros
Model fingerprint: 0x957b122c
Variable types: 1 continuous, 8268 integer (8268 binary)
Coefficient statistics:
Matrix range [1e+00, 2e+01]
Objective range [2e+04, 3e+04]
Bounds range [1e+00, 1e+00]
RHS range [1e+00, 2e+00]
Found heuristic solution: objective 3822000.0000
Presolve removed 625 rows and 157 columns
Presolve time: 0.03s
Presolved: 208 rows, 8112 columns, 16224 nonzeros
Variable types: 0 continuous, 8112 integer (8112 binary)
Explored 0 nodes (0 simplex iterations) in 0.04 seconds (0.03 work units)
Thread count was 4 (of 4 available processors)
Solution count 1: 3.822e+06
Optimal solution found (tolerance 1.00e04)
Best objective 3.822000000000e+06, best bound 3.822000000000e+06, gap 0.0000%
My question is:
How can set the Root relaxation in a way that i can always find the optimal solution for my problem?
If there are other methods to find the optimal solution, please tell me how. Thank you for your help.
Some lines of my code:
#####################################################################
# Variables creation
#y[i] = 1 if the vehicle i is used and 0 otherwise
#x[l,i] = 1 if the palette l is packed into the vehicle
#####################################################################
model.x =pyo.Var(dilevery_palette_variation,upper_bounds,within = Binary)
model.y =pyo.Var(upper_bounds,within = Binary)
x=model.x
y=model.y
#Define the objective
model.obj = Objective(expr = sum(vehcile_props(i,data_demande)['Cost']*y[i] for i in upper_bounds) , sense = minimize )
opt = SolverFactory('gurobi')
results = opt.solve(model, tee=True)

Thank you for your help .
0 
How can set the Root relaxation in a way that i can always find the optimal solution for my problem?
If there are other methods to find the optimal solution, please tell me how.
I don't understand your question. According to the log snippet you posted, your model has been solved to optimality and the optimal solution value is 3822000.
Could you please try to explain in more detail what you are trying to achieve?
Best regards,
Jaromił0
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