CONTINUOUSLY IMPROVE THE PERFORMANCE OF PLANNING AND SCHEDULING MODELS WITH PARAMETER FEEDBACK
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Continuously improving the accuracy and precision of planning and scheduling models is not new; unfortunately it is not institutionalized in practice. The intent of this paper is to highlight a ...
Continuously improving the accuracy and precision of planning and scheduling models is not new; unfortunately it is not institutionalized in practice. The intent of this paper is to highlight a relatively simple approach to historize or memorize past and present actual planning and scheduling data collected into what we call the past rolling horizon (PRH). The PRH is identical to the future rolling horizon (FRH) used in hierarchical production planning and model predictive control to manage omnipresent uncertainty in the model and data. Instead of optimizing future decisions such as throughputs, operating-modes and conditions we now optimize or estimate key model parameters. Although bias-updating using a single time-sample of data is common practice in advanced process control and optimization to incorporate “parameter” feedback, this is only realizable for real-time applications with comprehensive measurement systems. Proposed in this paper is the use of multiple synchronous or asynchronous time-samples in the past in conjunction with simultaneous reconciliation and regression to update a subset of the model parameters on a past rolling horizon basis to improve the performance of planning and scheduling models.
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