# Problems finding a feasible solution in a MIP

I am using CPLEX with Julia using the package JuMP to solve a MIP problem. In a small instance, I have tested my problem but, after 10 minutes, nothing happens. I have defined the following parameters in CPLEX:

1. set_optimizer_attribute(modelo,"CPX_PARAM_ZEROHALFCUTS",2) -->> Generate zero-half cuts aggressively
2. set_optimizer_attribute(modelo,"CPXPARAM_MIP_Cuts_Covers",2) -->> Generate cover cuts aggressively
3. set_optimizer_attribute(modelo,"CPXPARAM_MIP_Strategy_SubAlgorithm",1) -->> Emphasize feasibility over optimality

The branch-and-bound does not even begin. Look the CPLEX report:

CPXPARAM_TimeLimit                               600
CPXPARAM_MIP_Cuts_Covers                         2
CPXPARAM_MIP_Tolerances_MIPGap                   0.01
CPXPARAM_MIP_Strategy_SubAlgorithm               1
CPXPARAM_MIP_Cuts_ZeroHalfCut                    2
Warning:  Non-integral bounds for integer variables rounded.
Warning:  No solution found from 1 MIP starts.
Retaining values of one MIP start for possible repair.

Root node processing (before b&c):
Real time             =  600.11 sec. (206099.96 ticks)
Real time             =    0.00 sec. (0.00 ticks)
Sync time (average)   =    0.00 sec.
Wait time (average)   =    0.00 sec.
------------
Total (root+branch&cut) =  600.11 sec. (206099.96 ticks)


In another test, I used all of the default parameters but the result was the same.

My problem does not have any big-$$M$$ usage in the formulation.

Is there a combination of CPLEX parameters that accelerate obtaining an integer feasible solution?