Production scheduling faces three challenges, two of which are trade-offs and the third is processing time uncertainty. The two sources of tradeoffs 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 tradeoffs 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.
|Number of pages||8|
|State||Published - 2021|
|Event||49th SME North American Manufacturing Research Conference, NAMRC 2021 - Cincinnati, United States|
Duration: Jun 21 2021 → Jun 25 2021
Bibliographical noteFunding Information:
We appreciate support from the Department of Mechanical Engineering at University of Kentucky, the Natural Sciences and Engineering Research Council of Canada, and the Haskayne School of Business at University of Calgary.
© 2021 The Authors. Published by Elsevier B.V.
- Key performance indicators
- Modern portfolio theory
- Statistical process control
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Artificial Intelligence