Short-term scheduling of make-and-pack processes in the consumer goods industry using discrete-time and precedence-based MILP models

Christian Klanke, Vassilios Yfantis, Francesc Corominas, Sebastian Engell

In: Computers & Chemical Engineering 154 Seite 107453 Elsevier 2021.


This work deals with the short-term scheduling of a two-stage continuous make-and-pack process with finite intermediate buffer and sequence-dependent changeovers from the consumer goods industry. In the present coupled layout of the plant under consideration, the two stages, product formulation and packaging, are directly coupled, i.e. the products of the formulation stage go directly to their dedicated unit in the packaging stage. As for different products either the formulation or the packaging stage can be the bottleneck due to a stage and product dependent processing rate. A gain in productivity can be obtained if the two stages are decoupled by a buffer so that the formulation lines and the packaging lines can both run at full capacity. To evaluate the benefit of introducing a buffer between the stages, a rigorous discrete-time mixed-integer linear programming (MILP) model was developed. As the results of the discrete-time model were unsatisfactory with respect to total plant downtime due to changeovers and idle times, a two-step solution strategy including a second immediate precedence-based MILP model was developed. As the problem is intractable for the planning horizons of interest, an order-decomposition strategy for both models that is enhanced by several heuristics, was incorporated in the solution strategy. It is demonstrated that the redesign of the production plant yields significant productivity improvements that can be realized using the proposed scheduling approach. The computational results on several real cases show that the increased modeling and development effort of the two-step solution strategy pays off in terms of solution quality and computation times.

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence