Hybrid Genetic Algorithms Applied to the Glass Container Industry Problem

The present paper proposes two hybrid genetic algorithms as decision-making techniques for operational level decisions in the Glass Container Industry (GCI). The proposed methods address a production scenario where one new furnace and the related machines must be added to the current industrial plant. The configurations for each machine connected in a furnace is a decision to be taken, which depends on demand forecasts for glass containers within a time horizon. It is a tactical and operational level decisions that must be efficiently made. A mathematical formulation is first presented to describe precisely the objective and constraints for such problem. The formulation will also allow solving the problem instances by applying an exact method. Next, a hybrid approach combining genetic algorithms with mathematical programming techniques, and a greedy filter heuristic is proposed to solve the same problem instances. The set of instances is generated with data provided by a GCI located in Portugal and Brazil. The results reported indicate that the hybrid genetic algorithms return solutions able to support the operational and tactical decisions.