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Optimization of the grapes reception process

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Abstract

Grapes reception is a key process in wine production. The harvest days are extremely challenging days in managing the reception of the grapes, as the winery needs to deal with the non-uniform arrival of the grapes, while guaranteeing suppliers’ satisfaction and wine quality. The best management of the resources of the suppliers (i.e., grapes and trucks) and winery (i.e., grain-tanks and pressing machines) must be ensured. In this paper, the underlying optimization problem for grape reception is solved by developing a genetic algorithm (GA) tailored for this specific challenge. The results of this algorithm are compared with a FIFO policy for a typical scenario that occurs on the harvest days of a real winery. Additionally, different scenarios are simulated to assess the validity and quality of the solutions found. The results show that, using modest computational resources, it is possible to achieve better solutions with the proposed GA. This allows for the algorithm to be used in real time, even whenever plant conditions change significantly (e.g., when a new truck arrives, when a machine fails). Furthermore, the trucks and grapes waiting time for the results using the developed GA are significantly smaller than the ones observed using a FIFO approach.

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References

  1. Allahverdi A (2015) The third comprehensive survey on scheduling problems with setup times/costs. Eur J Oper Res 246(2):345–378

    Article  MathSciNet  Google Scholar 

  2. Anderson K, Pinilla V (2018) Wine globalization: a new comparative history. Cambridge University Press, Cambridge

    Book  Google Scholar 

  3. Basso F, Varas M (2017) A MIP formulation and a heuristic solution approach for the bottling scheduling problem in the wine industry. Comput Ind Eng 105:136–145

    Article  Google Scholar 

  4. Bohle C, Maturana S, Vera J (2010) A robust optimization approach to wine grape harvesting scheduling. Eur J Oper Res 200(1):245–252

    Article  Google Scholar 

  5. Cholette S (2007) A novel problem for a vintage technique: using mixed-integer programming to match wineries and distributors. Interfaces 37(3):231–239

    Article  Google Scholar 

  6. de Ocampo ALP, Dadios EP (2017) Energy cost optimization in irrigation system of smart farm by using genetic algorithm. In: 2017IEEE 9th International conference on humanoid, nanotechnology, information technology, communication and control, environment and management (HNICEM). IEEE, pp 1–7

  7. Gumaste SS, Kadam AJ (2016) Future weather prediction using genetic algorithm and fft for smart farming. In: 2016 International conference on computing communication control and automation (ICCUBEA). IEEE, pp 1–6

  8. Hakli H, Harun U (2017) A novel approach for automated land partitioning using genetic algorithm. Expert Syst Appl 82:10–18

    Article  Google Scholar 

  9. Kisel’ák J. Lu Y, Švihra J, Szépe P, Stehlík M (2020) “spocu”: scaled polynomial constant unit activation function. Neural Computing and Applications pp. 1–17

  10. Li YZ, Shan-shan Y (2010) Application of svm optimized by genetic algorithm in forecasting and management of water consumption used in agriculture. In: 2010 The 2nd international conference on computer and automation engineering (ICCAE), vol 1. IEEE, pp 625–628

  11. Moccia L (2013) Operational research in the wine supply chain. INFOR Inf Syst Oper Res 51(2):53–63

    Google Scholar 

  12. Morande H, Maturana S (2010) Design and validation of a decision support system for oenologists. Rev ICHIO 1(1):46–58

    Google Scholar 

  13. Noguchi N, Terao H (1997) Path planning of an agricultural mobile robot by neural network and genetic algorithm. Comput Electron Agric 18(2–3):187–204

    Article  Google Scholar 

  14. Palmowski Z, Sidorowicz A (2018) Note on dynamic programming optimization for assigning pressing tanks at wineries. arXiv:1811.00469

  15. Tong DL, Mintram R (2010) Genetic algorithm-neural network (gann): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection. Int J Mach Learn Cybern 1(1–4):75–87

    Article  Google Scholar 

  16. van der Merwe A, Van Dyk F, Van Vuuren J (2011) Decision support for grape harvesting at a south african winery. ORiON 27(2):83–100

    Article  Google Scholar 

  17. Varas M, Basso F, Lüer-Villagra A, Mac Cawley A, Maturana S (2019) Managing premium wines using an (s-1, s) inventory policy: a heuristic solution approach. Ann Oper Res 280(1):351–376. https://doi.org/10.1007/s10479-019-03266-3

    Article  MathSciNet  MATH  Google Scholar 

  18. Varsei M, Polyakovskiy S (2017) Sustainable supply chain network design: a case of the wine industry in Australia. Omega 66:236–247

    Article  Google Scholar 

  19. Weintraub A, Romero C (2006) Operations research models and the management of agricultural and forestry resources: a review and comparison. Interfaces 36(5):446–457

    Article  Google Scholar 

Download references

Acknowledgements

This work has been supported by national funds through FCT—Fundação para a Ciência e Tecnologia through project UIDB/04728/2020.

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  1. CIICESI, ESTG, Politécnico do Porto, Felgueiras, Portugal

    Davide Carneiro, Jorge Pereira & Eliana Costa e Silva

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  1. Davide Carneiro
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  2. Jorge Pereira
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  3. Eliana Costa e Silva
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Correspondence to Davide Carneiro.

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Carneiro, D., Pereira, J. & e Silva, E.C. Optimization of the grapes reception process. Neural Comput & Applic 33, 8687–8707 (2021). https://doi.org/10.1007/s00521-020-05620-0

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  • DOI: https://doi.org/10.1007/s00521-020-05620-0

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