Campus Units
Industrial and Manufacturing Systems Engineering, Bioeconomy Institute (BEI)
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
3-11-2020
Journal or Book Title
Computers & Industrial Engineering
First Page
106397
Research Focus Area(s)
Operations Research
DOI
10.1016/j.cie.2020.106397
Abstract
This paper proposes an integrated model for a multi-period reverse logistics (RL) network design problem under return and demand uncertainty. The reverse logistics network is modeled as a two-stage stochastic programming model to make strategic and tactical decisions. The strategic decisions are the first stage decisions in establishing network’s facilities and tactical decisions are the second stage decisions on material flow, inventory, backorder, shortage, and outsourcing. The uncertainties considered in this study are the primary market return and secondary market demand. The model aims to determine optimal numbers of sorting centers and warehouses, optimal lot sizes, and transportation plan that minimize the expected total system cost over the planning horizon. A case study was conducted to validate the proposed model. Numerical results indicate that the stochastic model solution outperforms result of expected value solution.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Copyright Owner
Elsevier Ltd.
Copyright Date
2020
Language
en
File Format
application/pdf
Recommended Citation
Azizi, Vahid; Hu, Guiping; and Mokari, Mahsa, "A two-stage stochastic programming model for multi-period reverse logistics network design with lot-sizing" (2020). Industrial and Manufacturing Systems Engineering Publications. 235.
https://lib.dr.iastate.edu/imse_pubs/235
Comments
This is a manuscript of an article published as Azizi, Vahid, Guiping Hu, and Mahsa Mokari. "A two-stage stochastic programming model for multi-period reverse logistics network design with lot-sizing." Computers & Industrial Engineering (2020): 106397. DOI: 10.1016/j.cie.2020.106397. Posted with permission.