# MIP gives out zero as best solution when trying to optimize MIP model for flexible job-hop

I have implemented an existing MIP Model into python with the python-mip solver and somehow I only get '0' as a solution to my minimization problem. What could be the problem? I am fairly new to LP and cannot really grasp, how I can fix this solution.

Welcome to the CBC MILP Solver
Version: Trunk
Build Date: Nov 15 2020

Starting solution of the Linear programming relaxation problem using Dual Simplex

Coin0506I Presolve 62 (-30) rows, 49 (-49) columns and 292 (-120) elements
Clp0000I Optimal - objective value 0
Coin0511I After Postsolve, objective 0, infeasibilities - dual 0 (0), primal 0 (0)
Clp0032I Optimal objective 0 - 0 iterations time 0.002, Presolve 0.00

Starting MIP optimization
Cgl0002I 4 variables fixed
Cgl0004I processed model has 62 rows, 49 columns (44 integer (44 of which binary)) and 292 elements
Coin3009W Conflict graph built in 0.000 seconds, density: 0.907%
Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated
Cbc0038I Initial state - 0 integers unsatisfied sum - 0
Cbc0038I Solution found of 0
Cbc0038I Relaxing continuous gives 0
Cbc0038I Before mini branch and bound, 44 integers at bound fixed and 5 continuous
Cbc0038I Mini branch and bound did not improve solution (0.00 seconds)
Cbc0038I After 0.00 seconds - Feasibility pump exiting with objective of 0 - took 0.00 seconds
Cbc0012I Integer solution of 0 found by feasibility pump after 0 iterations and 0 nodes (0.00 seconds)
Cbc0001I Search completed - best objective 0, took 0 iterations and 0 nodes (0.00 seconds)
Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost
Total time (CPU seconds):       0.00   (Wallclock seconds):       0.00

• Welcome to OR SE. To get any traction with this question, you would need to show us your model formulation (preferably in algebraic terms as opposed to code).
– prubin
Mar 28 at 18:26