Abstract
Clearing functions that describe the expected output of a production resource as a function of its expected workload have yielded promising production planning models. However, there is as yet no fully satisfactory approach to estimating clearing functions from data. We identify several issues that arise in estimating clearing functions such as sampling issues, systematic underestimation and model misspecification. We address the model misspecification problem by introducing a generalised functional form, and the sampling issues via iterative refinement of initial parameter estimates. The iterative refinement approach yields improved performance for planning models at higher levels of utilisation, and the generalised functional form results in significantly better production plans both alone and when combined with the iterative refinement approach. The IR approach also obtains solutions of similar quality to the much more computationally demanding simulation optimisation approaches used in previous work.
Acknowledgments
The authors would like to thank two anonymous referees for their many constructive suggestions which materially improved both the content and the presentation of this paper.
Disclosure statement
No potential conflict of interest was reported by the authors.