Integer programming models for mid-term production planning for high-tech low-volume supply chains

Because of the integer production variables, size of realistic instances and complexity of the model, this model is not easily solved by a commercial solver. Applying Benders' decomposition results in alternative capacity constraints and a second formulation of the problem. Where the first formulation assigns resources explicitly to release orders, the second formulation assures that the available capacity in any subset of the planning horizon is sufficient. Since the number of alternative capacity constraints is exponential, we first solve the second formulation without capacity constraints. Each time an incumbent is found during the branch and bound process a maximum flow problem is used to find missing constraints. If a missing constraint is found it is added and the branch and bound process is restarted. Results from a realistic test case show that utilizing this algorithm to solve the second formulation is significantly faster than solving the first formulation.