Application of stochastic programming to reduce uncertainty in quality-based supply planning of slaughterhouses
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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.
KeywordsSupply chain Uncertainty Food supply chain networks Stochastic programming Allocation planning Quality controlled logistics
The authors gratefully acknowledge the financial support of the European Community under the Sixth Framework Programme for Research, Technological Development and Demonstration Activities, for the Integrated Project Q-PORKCHAINS FOOD-CT-2007-036245. This paper has been supported by the Spanish state (project TIN2012-37483-C03-01) and Junta de Andalucía (P11-TIC-7176), in part financed by the European Regional Development Fund (ERDF).
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