A model predictive framework for planning and scheduling problems: a case study of consumer goods supply chain

Abstract

Model Predictive Control is a well established technique for the control of processes and plants. We present a similar concept for planning and scheduling problems. There have mainly been two approaches to solve the planning and scheduling problems. The first approach is to model the planning and scheduling as one monolithic problem and solve it for the entire horizon. Needless to say, this approach requires an extensive computational effort and becomes impossible to solve in the case of large-scale scheduling problems. The other approach is to hierarchically-decompose the problem into a planning level problem and a scheduling level problem. This approach leads to tractable problems. Neither of these approaches provide the framework for incorporating uncertainties in the processing time of batches, or random equipment breakdowns, or demand uncertainties in the future. Furthermore, these approaches only provide ‘one snapshot’ of the planning problem and not a ‘walk through the timeline’. Model predictive planning and scheduling provides a framework for studying dynamics. Model predictive planning and scheduling requires a forecasting model and an optimization model. Both these models work in tandem in a simulation environment that incorporates uncertainty. The similarity with the model predictive approach which is widely used in process-control is that in each period, the forecasting model calculates the target inventory (controlled variable) in the future periods. These inventory levels ensure desired customer service level while minimizing average inventory. The scheduling model then tries to achieve these target inventory levels in the future periods by scheduling tasks (manipulated variables).