Application of stochastic programming to reduce uncertainty in quality-based supply planning of slaughterhouses

W. A. Rijpkema (), E. M. T. Hendrix, R. Rossi and J. G. A. J. Vorst
Additional contact information
W. A. Rijpkema: Wageningen University
E. M. T. Hendrix: University of Málaga
R. Rossi: University of Edinburgh
J. G. A. J. Vorst: Wageningen University

Annals of Operations Research, 2016, vol. 239, issue 2, No 13, 613-624

Abstract: Abstract To match products of different quality with end market preferences under supply uncertainty, it is crucial to integrate product quality information in logistics decision making. We present a case of this integration in a meat processing company that faces uncertainty in delivered livestock quality. We develop a stochastic programming model that exploits historical product quality delivery data to produce slaughterhouse allocation plans with reduced levels of uncertainty in received livestock quality. The allocation plans generated by this model fulfil demand for multiple quality features at separate slaughterhouses under prescribed service levels while minimizing transportation costs. We test the model on real world problem instances generated from a data set provided by an industrial partner. Results show that historical farmer delivery data can be used to reduce uncertainty in quality of animals to be delivered to slaughterhouses.

Keywords: Supply chain; Uncertainty; Food supply chain networks; Stochastic programming; Allocation planning; Quality controlled logistics (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-013-1460-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:239:y:2016:i:2:d:10.1007_s10479-013-1460-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-013-1460-y

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().