Multi-scale planning and scheduling in the pharmaceutical industry

Abstract Most sophisticated planning and scheduling approaches for the process industry consider a fixed time horizon and assume that all data is given at the time of application. In this contribution we propose a planning and scheduling approach for a continuous and dynamic decision process where decisions have to be made before all data are available. As an inspiration we have a real world problem originating from a complex pharmaceutical production plant. The approach we propose is based on a hierarchically structured moving horizon framework. At each level we propose optimisation models to provide support for the relevant decisions. The levels are diverse regarding time scope, aggregation, update rate and availability of data at the time applied. The framework receives input data piece by piece and has to make decisions with only a partial knowledge of the required input. Solution procedures have been developed and the optimisation models have been tested with data from the real world problem. The solution procedures were able to obtain solutions of good quality within acceptable computational times.