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Optimal production scheduling for the dairy industry

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  • Philip Doganis
  • Haralambos Sarimveis

Abstract

The increasing variety of products offered by the food industry has helped the industry to respond to market trends, but at the same time has resulted in a more complex production process, which requires flexibility and an efficient coordination of existing resources. Especially in industrial yogurt production, there is a wide variety of products that differ in features like fat content, the whey used to produce the mixture, the flavor, the size of the container or the language on the label. The great diversification and the special features that characterize yogurt production lines (satisfaction of multiple due dates, variable processing times, sequence-dependent setup times and costs and monitoring of inventory levels), render generic scheduling methodologies impractical for real-world applications. In this work we present a customized Mixed Integer Linear Programming (MILP) model for optimizing yogurt packaging lines that consist of multiple parallel machines. The model is characterized by parsimony in the utilization of binary variables and necessitates the use of only a small pre-determined number of time periods. The efficiency of the proposed model is illustrated through its application to the yogurt production plant of a leading dairy product manufacturing company in Greece. Copyright Springer Science+Business Media, LLC 2008

Suggested Citation

  • Philip Doganis & Haralambos Sarimveis, 2008. "Optimal production scheduling for the dairy industry," Annals of Operations Research, Springer, vol. 159(1), pages 315-331, March.
    • Handle: RePEc:spr:annopr:v:159:y:2008:i:1:p:315-331:10.1007/s10479-007-0285-y
    DOI: 10.1007/s10479-007-0285-y
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    References listed on IDEAS

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    1. Jose Pinto & Ignacio Grossmann, 1998. "Assignment and sequencing models for thescheduling of process systems," Annals of Operations Research, Springer, vol. 81(0), pages 433-466, June.
    2. Allahverdi, Ali & Gupta, Jatinder N. D. & Aldowaisan, Tariq, 1999. "A review of scheduling research involving setup considerations," Omega, Elsevier, vol. 27(2), pages 219-239, April.
    3. Sungmin Kang & Kavindra Malik & L. Joseph Thomas, 1999. "Lotsizing and Scheduling on Parallel Machines with Sequence-Dependent Setup Costs," Management Science, INFORMS, vol. 45(2), pages 273-289, February.
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    Citations

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    Cited by:

    1. Bilge Bilgen & Yelda Çelebi, 2013. "Integrated production scheduling and distribution planning in dairy supply chain by hybrid modelling," Annals of Operations Research, Springer, vol. 211(1), pages 55-82, December.
    2. Mili Mehrotra & Milind Dawande & Srinagesh Gavirneni & Mehmet Demirci & Sridhar Tayur, 2011. "OR PRACTICE---Production Planning with Patterns: A Problem from Processed Food Manufacturing," Operations Research, INFORMS, vol. 59(2), pages 267-282, April.
    3. Stefansdottir, Bryndis & Grunow, Martin & Akkerman, Renzo, 2017. "Classifying and modeling setups and cleanings in lot sizing and scheduling," European Journal of Operational Research, Elsevier, vol. 261(3), pages 849-865.
    4. Stüve, David & van der Meer, Robert & Lütke Entrup, Matthias & Agha, Mouhamad Shaker Ali, 2020. "Supply chain planning in the food industry," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Lo, volume 29, pages 317-353, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    5. Yiyi Xu & M’hammed Sahnoun & Fouad Ben Abdelaziz & David Baudry, 2022. "A simulated multi-objective model for flexible job shop transportation scheduling," Annals of Operations Research, Springer, vol. 311(2), pages 899-920, April.

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