×

Application of stochastic programming to reduce uncertainty in quality-based supply planning of slaughterhouses. (English) Zbl 1337.90048

Summary: 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.

MSC:

90C15 Stochastic programming
90C90 Applications of mathematical programming
PDFBibTeX XMLCite
Full Text: DOI Link

References:

[1] Akkerman, R., Farahani, P., & Grunow, M. (2010). Quality, safety and sustainability in food distribution: a review of quantitative operations management approaches and challenges. OR Spektrum, 32(4), 863-904. · doi:10.1007/s00291-010-0223-2
[2] Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust optimization. Princeton series in applied mathematics. Princeton: Princeton University Press. · Zbl 1221.90001 · doi:10.1515/9781400831050
[3] Birge, J. R., & Louveaux, F. (1997). Introduction to stochastic programming. Springer series in operations research. New York: Springer. · Zbl 0892.90142
[4] Brunsø, K., Fjord, T. A., & Grunert, K. G. (2002). Consumers’ food choice and quality perception. Working paper 77, ISSN 0907 2101.
[5] Chai, J., Liu, J. N. K., & Ngai, E. W. T. (2013). Application of decision-making techniques in supplier selection: a systematic review of literature. Expert Systems with Applications, 40(10), 3872-3885. · doi:10.1016/j.eswa.2012.12.040
[6] Dabbene, F., Gay, P., & Sacco, N. (2008a). Optimisation of fresh-food supply chains in uncertain environments, part I: Background and methodology. Biosystems Engineering, 99(3), 348-359. · doi:10.1016/j.biosystemseng.2007.11.011
[7] Dabbene, F., Gay, P., & Sacco, N. (2008b). Optimisation of fresh-food supply chains in uncertain environments, part II: A case study. Biosystems Engineering, 99(3), 360-371. · doi:10.1016/j.biosystemseng.2007.11.012
[8] Grunert, K. G. (2006). How changes in consumer behaviour and retailing affect competence requirements for food producers and processors. Economia Agraria y Recursos Naturales, 6(11), 3-22.
[9] Grunert, K. G., Bredahl, L., & Brunsø, K. (2004). Consumer perception of meat quality and implications for product development in the meat sector—a review. Meat Science, 66(2), 259-272. · doi:10.1016/S0309-1740(03)00130-X
[10] Grunow, M., & van der Vorst, J. G. A. J. (2010). Food production and supply chain management. OR Spektrum, 32(4), 861-862. · doi:10.1007/s00291-010-0222-3
[11] Klein Haneveld, W., & van der Vlerk, M. H. (1999). Stochastic integer programming: general models and algorithms. Annals of Operations Research, 85(0), 39-57. · Zbl 0920.90110 · doi:10.1023/A:1018930113099
[12] Kumar, P., Reinitz, H. W., Simunovic, J., Sandeep, K. P., & Franzon, P. D. (2009). Overview of RFID technology and its applications in the food industry. Journal of Food Science, 74(8), 101-106. · doi:10.1111/j.1750-3841.2009.01323.x
[13] Peidro, D., Mula, J., Poler, R., & Lario, F. (2009). Quantitative models for supply chain planning under uncertainty: a review. The International Journal of Advanced Manufacturing Technology, 43(3), 400-420. · doi:10.1007/s00170-008-1715-y
[14] Perez, C., de Castro, R., & Font i Furnols, M. (2009). The pork industry: a supply chain perspective. British Food Journal, 111(3), 257-274. · doi:10.1108/00070700910941462
[15] Rong, A., Akkerman, R., & Grunow, M. (2011). An optimization approach for managing fresh food quality throughout the supply chain. International Journal of Production Economics, 131(1), 421-429. · doi:10.1016/j.ijpe.2009.11.026
[16] Rossi, R., Tarim, S. A., Hnich, B., & Prestwich, S. (2012). Constraint programming for stochastic inventory systems under shortage cost. Annals of Operations Research, 195(1), 49-71. · Zbl 1251.90036 · doi:10.1007/s10479-011-0936-x
[17] Sahinidis, N. V. (2004). Optimization under uncertainty: state-of-the-art and opportunities. Computers & Chemical Engineering, 28(6-7), 971-983. · doi:10.1016/j.compchemeng.2003.09.017
[18] Tarim, S. A., Hnich, B., Prestwich, S., & Rossi, R. (2009). Finding reliable solutions: event-driven probabilistic constraint programming. Annals of Operations Research, 171(1), 77-99. · Zbl 1181.90208 · doi:10.1007/s10479-008-0382-6
[19] van der Vorst, J. G. A. J., van Kooten, O., & Luning, P. A. (2011). Towards a diagnostic instrument to identify improvement opportunities for quality controlled logistics in agrifood supply chain networks. International Journal on Food System Dynamics, 2(1), 94-105.
[20] van der Vorst, J. G. A. J., Tromp, S. O., & van der Zee, D. J. (2009). Simulation modelling for food supply chain redesign: integrated decision making on product quality, sustainability and logistics. International Journal of Production Research, 47(23), 6611-6631. · Zbl 1198.90071 · doi:10.1080/00207540802356747
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.