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.

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.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier B.V.

Language

en

File Format

application/pdf

Available for download on Monday, January 10, 2022

Published Version

Share

COinS