Efficient mathematical frameworks for detailed production scheduling in food processing industries

The production scheduling of a real-world multistage food process is considered in this work. An efficient mixed integer programming (MIP) continuous-time model is proposed to address the production problem under study. The overall mathematical framework relies on an efficient modeling approach of the sequencing decisions, the integrated modeling of all production stages, and the inclusion of a set of strong tightening constraints. The simultaneous optimization of all processing stages aims at facilitating the interaction among the different departments of the production facility. Moreover, an alternative MIP-based solution strategy is proposed for dealing with large-scale food processing scheduling problems. Although this method may no guarantee global optimality, it favors low computational requirements and solutions of very good quality. Several problem instances are solved to reveal the salient computational performance and the practical benefits of the proposed MIP formulation and solution strategy.