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

W.A. Rijpkema, E.M.T. Hendrix, R. Rossi, J.G.A.J. van der Vorst

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)

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.
LanguageEnglish
Pages613-624
Number of pages12
JournalAnnals of Operations Research
Volume239
Issue number2
DOIs
Publication statusPublished - 2016

Fingerprint

Uncertainty
Stochastic programming
Supply planning
Livestock
Product quality
Animals
Quality information
Supply uncertainty
Transportation costs
Logistics
Meat
Decision making
Farmers
Demand model
Service levels

Cite this

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Application of stochastic programming to reduce uncertainty in quality-based supply planning of slaughterhouses. / Rijpkema, W.A.; Hendrix, E.M.T.; Rossi, R.; van der Vorst, J.G.A.J.

In: Annals of Operations Research, Vol. 239, No. 2, 2016, p. 613-624.

Research output: Contribution to journalArticleAcademicpeer-review

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