Campus Units
Industrial and Manufacturing Systems Engineering, Bioeconomy Institute (BEI)
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
1-10-2020
Journal or Book Title
European Journal of Operational Research
Research Focus Area(s)
Operations Research
DOI
10.1016/j.ejor.2019.12.030
Abstract
Uncertainty is among the significant concerns in production scheduling. It has become increasingly important to take uncertainties into consideration for lot-sizing and scheduling. In this paper, we adopt the Hybrid Stochastic and Robust Optimization (HSRO) approach in lot-sizing and scheduling problems in which suppliers have the flexibility of satisfying a fraction of demand based on the market and their policies. Two types of uncertainties have been considered simultaneously: demand and overtime processing cost. Robust optimization is adopted for uncertain demand and Sample Average Approximation (SAA) technique is applied to solve the stochastic program for uncertain overtime processing cost. Numerical results based on a manufacturing company has been conducted to not only validate the proposed hybrid model but also quantitatively demonstrate the merit of our approach. Sample size stability test and sensitivity analyses on various parameters have also been conducted.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Copyright Owner
Elsevier B.V.
Copyright Date
2019
Language
en
File Format
application/pdf
Recommended Citation
Hu, Zhengyang and Hu, Guiping, "Hybrid stochastic and robust optimization model for lot-sizing and scheduling problems under uncertainties" (2020). Industrial and Manufacturing Systems Engineering Publications. 223.
https://lib.dr.iastate.edu/imse_pubs/223
Comments
This is a manuscript of an article published as Hu, Zhengyang, and Guiping Hu. "Hybrid stochastic and robust optimization model for lot-sizing and scheduling problems under uncertainties." European Journal of Operational Research (2020). DOI: 10.1016/j.ejor.2019.12.030. Posted with permission.