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

Author

Listed:
  • W. A. Rijpkema

    (Wageningen University)

  • E. M. T. Hendrix

    (University of Málaga)

  • R. Rossi

    (University of Edinburgh)

  • J. G. A. J. Vorst

    (Wageningen University)

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.

Suggested Citation

  • W. A. Rijpkema & E. M. T. Hendrix & R. Rossi & J. G. A. J. Vorst, 2016. "Application of stochastic programming to reduce uncertainty in quality-based supply planning of slaughterhouses," Annals of Operations Research, Springer, vol. 239(2), pages 613-624, April.
    • Handle: RePEc:spr:annopr:v:239:y:2016:i:2:d:10.1007_s10479-013-1460-y
    DOI: 10.1007/s10479-013-1460-y
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    References listed on IDEAS

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    1. Brunsø, Karen & Fjord, Thomas Ahle & Grunert, Klaus G., 2002. "Consumers' food choice and quality perception," MAPP Working Papers 77, University of Aarhus, Aarhus School of Business, The MAPP Centre.
    2. Willem Klein Haneveld & Maarten van der Vlerk, 1999. "Stochastic integer programming:General models and algorithms," Annals of Operations Research, Springer, vol. 85(0), pages 39-57, January.
    3. Rong, Aiying & Akkerman, Renzo & Grunow, Martin, 2011. "An optimization approach for managing fresh food quality throughout the supply chain," International Journal of Production Economics, Elsevier, vol. 131(1), pages 421-429, May.
    4. S. Tarim & Brahim Hnich & Steven Prestwich & Roberto Rossi, 2009. "Finding reliable solutions: event-driven probabilistic constraint programming," Annals of Operations Research, Springer, vol. 171(1), pages 77-99, October.
    5. Grunert, Klaus G., 2006. "How changes in consumer behaviour and retailing affect competence requirements for food producers and processors," Economia Agraria y Recursos Naturales, Spanish Association of Agricultural Economists, vol. 6(11), pages 1-20.
    6. Roberto Rossi & S. Tarim & Brahim Hnich & Steven Prestwich, 2012. "Constraint programming for stochastic inventory systems under shortage cost," Annals of Operations Research, Springer, vol. 195(1), pages 49-71, May.
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    Cited by:

    1. Mohebalizadehgashti, Fatemeh & Zolfagharinia, Hossein & Amin, Saman Hassanzadeh, 2020. "Designing a green meat supply chain network: A multi-objective approach," International Journal of Production Economics, Elsevier, vol. 219(C), pages 312-327.
    2. Aljuneidi, Tariq & Punia, Sushil & Jebali, Aida & Nikolopoulos, Konstantinos, 2024. "Forecasting and planning for a critical infrastructure sector during a pandemic: Empirical evidence from a food supply chain," European Journal of Operational Research, Elsevier, vol. 317(3), pages 936-952.

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