Evaluation of Clearing Functions' Fitting Methodology and Performance for Production Planning Models

Abstract

It is well known that cycle times in capacitated production systems increase nonlinearly with resource utilization, which creates considerable difficulty for the traditional linear programming (LP) models used for production planning. Clearing Functions capture this nonlinear relationship and embed it in the optimization model. In this thesis, we evaluate the fitting methodology for clearing functions and show the importance of the fitting methodology on the production planning. We then perform a systematic comparison of the production planning models incorporating the clearing functions with the conventional linear programming models for production planning under different scenarios. The computational experiments applied to a scaled-down semiconductor manufacturing line illustrate the benefits of clearing function approach compared to fixed lead time approaches.