Balancing trade-offs in one-stage production with processing time uncertainty

Abstract

Production scheduling faces three challenges, two of which are trade-offs and the third is processing time uncertainty. The two sources of trade-offs are between inconsistent key performance indicators (KPIs), and between the expected return and the risk of KPI portfolios. Given the KPIs of total completion time (TCT) and variance of completion times (VCT) are inconsistent for one-stage production, we propose our trade-off balancing (ToB) heuristics. Based on comprehensive case studies, we show that our ToB heuristics efficiently and effectively balance the trade-offs from these two sources. Daniels and Kouvelis (DK) proposed a scheduling scheme to optimize the worst-case scenarios against processing time uncertainty, and they designed the endpoint product (EP) and endpoint sum (ES) heuristics for robust scheduling accordingly. Using 5 levels of coefficients of variation (CVs) to represent processing time uncertainty, we show that our ToB heuristics are robust as well, and even better than the EP and ES heuristics at high levels of processing time uncertainty. In addition, our ToB heuristics generate undominated solution spaces of KPIs, which provides a solid base in deciding control and specification limits for stochastic process control (SPC). Moreover, based on the normalized deviations from optima, our trade-off balancing scheme can be generalized to balance any inconsistent KPIs.