Genetic algorithm approach to production ordering problems with inconsistent constraints

A method for solving a scheduling problem with many complicated constraints is proposed. For a case study of such problems, the authors deal with an optimal production ordering problem in the acid rinsing process of a steelmaking plant. Strong requirements assigned to the constraints usually deteriorate the objective value, and in many cases it is impossible to satisfy all the constraints completely. Therefore, fundamental constraints of the problem are treated as hard constraints, the other constraints as soft or relaxable constraints. The genetic algorithm is applied to this problem. A two-phase method is proposed in such a way that: in Phase 1, relaxable constraints conflicting strongly with the objective function are found; and in Phase 2, the Pareto optimal solutions for representing the tradeoff between the objective value and the relaxation of the constraints are obtained. This method is effective because the computation load for tuning penalty parameters is decreased. Furthermore, the method gives a better result than the real operation data