An Efficient Procedure to Increase Robustness of Production Plans Embedded in an Integrated Multi-scale Planning and Scheduling Approach

In these presentation records we propose a procedure to increase robustness of production plans embedded in a modelling approach based on integrated multi-scale optimization models and solution methods for planning and scheduling of a make to order production process under uncertain and varying demand conditions. As an inspiration we have a large real world problem originating from a complex pharmaceutical enterprise. The approach is based on a hierarchically structured moving horizon algorithm. On each level in the algorithm we propose optimization models to provide support for the relevant decisions and the models are solved with decomposition heuristics. The levels are diverse regarding the time scope, aggregation, update rate and availability of data at the time applied. The maximum effective time horizon of the multi-scale approach is one year and we use sales forecasts as input demands instead of actual orders which are usually only available 3 months ahead in time although raw materials need to be procured up to one year in advance. The sale forecasts have historically proven to be rather uncertain and to increase the robustness of our long-term plans we use an iterative procedure where the first step is to use a MILP model to obtain a solution based on the sale forecasts. In the next step we generate a number of alternative demand scenarios and run LP models to test the robustness of the MILP solution for each of the demand scenarios. If the long-term production plan is feasible for enough of the demand scenarios, depending on our robustness criteria, then we use the current plan, but if not then we change the demand forecast and run the MILP model again iteratively until the robustness criterion has been met. The demand samples are generated with tailor-made methods based on statistical error analysis. The approach has been tested and implemented with industrial data from a pharmaceutical enterprise and has proved to be capable of obtaining realistic and profitable solutions within acceptable computational times.