Capacity modeling in aggregate production planning: Multi-dimensional clearing functions and iterative linear programming-simulation approaches

In this dissertation, the capacity representation in aggregate production planning models (APPM) is investigated by focusing on two main capacity modeling philosophies in the literature, namely the clearing functions (CF) and iterative linear programming-simulation approaches (IA). The underlying strength of these approaches comes from facilitating the mutual link between capacity and state of the shop floor (SF). This thesis study contributes to both CF and IA techniques. The contribution to the former field has been the introduction of product based multi-dimensional disaggregated clearing functions (MDCFs). Several forms of MDCFs are developed and incorporated into the APPMs as the capacity modeling module. As a proof of concept, postulated forms are first tested on a single machine multi-product (SMMP) system under several experimental settings. The results reveal that new MDCF forms show more accurate prediction of product-level throughput hence generate better (i.e. more profitable) plans than the existing CF approaches and the classical linear programming approach. Then, postulated forms are extended to model the capacity in multi-machine multi-product (MMMP) systems and are tested under different aggregations, manufacturing flexibility levels and execution policies. As a contribution to the field of IA, a new and more robust mechanism is proposed based on rigorous experimental analysis of the convergence behavior of an existing IA based capacity modeling mechanism. The findings in this study support the hypothesis that MDCF based APPMs lead to better production and release plans compared to the ones based on single dimensional aggregated CFs and to the models enhanced with IA.