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A Two-Stage Stochastic Linear Programming Model for Tactical Planning in the Soybean Supply Chain

Author

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  • Silvia Araújo dos Reis

    (Department of Business and Agribusiness, University of Brasília-UnB, Brasília 70910-900, DF, Brazil)

  • José Eugenio Leal

    (Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro 22541-041, RJ, Brazil)

  • Antônio Márcio Tavares Thomé

    (Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro 22541-041, RJ, Brazil)

Abstract

Background: The soybean market is representative of the world. Brazil is the largest producer and exporter of this crop and has low production costs but high logistical costs, which are influenced mainly by transport costs. Added to these characteristics, the disputed grain supply, the possibility of crop failure, and the randomness of some parameters that influence the soybean supply chain make decisions even more challenging. Methods: To mathematically model this problem, we carried out an analysis of the scientific production related to grain supply chain and the models used to address the problem, as well as a document analysis and a case study. Results: This paper proposes a new two-stage stochastic linear programming model with fixed recourse for tactical planning in the soybean supply chain from the perspective of the shipper under take or pay contracts over a one-year time horizon. The first-stage variables are the grain purchasing decisions and the volumes of rail and road transportation hired in advance. The model addresses 243 scenarios derived from four uncertainty sources: the purchase and sale prices of raw agricultural products on the spot market, the probability of crop failure, and the external demand. Conclusions: The model is successfully applied to a soybean trade firm in Brazil with expected gain of US$4,299,720 when using the stochastic model instead of the deterministic model. The stochastic model protected the firm from take or pay fines and crop failures, contracting a smaller volume of rail transport than what the company does.

Suggested Citation

  • Silvia Araújo dos Reis & José Eugenio Leal & Antônio Márcio Tavares Thomé, 2023. "A Two-Stage Stochastic Linear Programming Model for Tactical Planning in the Soybean Supply Chain," Logistics, MDPI, vol. 7(3), pages 1-26, August.
    • Handle: RePEc:gam:jlogis:v:7:y:2023:i:3:p:49-:d:1210849
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    References listed on IDEAS

    as
    1. Koltai, Tamás & Tatay, Viola, 2011. "A practical approach to sensitivity analysis in linear programming under degeneracy for management decision making," International Journal of Production Economics, Elsevier, vol. 131(1), pages 392-398, May.
    2. Jamileh Yousefi & Sahand Ashtab & Amirali Yasaei & Allu George & Ali Mukarram & Satinderpal Singh Sandhu, 2023. "Multiple Linear Regression Analysis of Canada’s Freight Transportation Framework," Logistics, MDPI, vol. 7(2), pages 1-17, May.
    3. Kouwenberg, Roy, 2001. "Scenario generation and stochastic programming models for asset liability management," European Journal of Operational Research, Elsevier, vol. 134(2), pages 279-292, October.
    4. Borodin, Valeria & Bourtembourg, Jean & Hnaien, Faicel & Labadie, Nacima, 2015. "A multi-step rolled forward chance-constrained model and a proactive dynamic approach for the wheat crop quality control problem," European Journal of Operational Research, Elsevier, vol. 246(2), pages 631-640.
    5. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    6. Donald L. Keefer & Samuel E. Bodily, 1983. "Three-Point Approximations for Continuous Random Variables," Management Science, INFORMS, vol. 29(5), pages 595-609, May.
    7. Ahumada, Omar & Villalobos, J. Rene, 2009. "Application of planning models in the agri-food supply chain: A review," European Journal of Operational Research, Elsevier, vol. 196(1), pages 1-20, July.
    8. Byung Min Soon & Jarrett Whistance, 2019. "Seasonal Soybean Price Transmission between the U.S. and Brazil Using the Seasonal Regime-Dependent Vector Error Correction Model," Sustainability, MDPI, vol. 11(19), pages 1-9, September.
    9. Borodin, Valeria & Bourtembourg, Jean & Hnaien, Faicel & Labadie, Nacima, 2016. "Handling uncertainty in agricultural supply chain management: A state of the art," European Journal of Operational Research, Elsevier, vol. 254(2), pages 348-359.
    10. An, Kun & Ouyang, Yanfeng, 2016. "Robust grain supply chain design considering post-harvest loss and harvest timing equilibrium," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 88(C), pages 110-128.
    11. Irvin J. Lustig & Roy E. Marsten & David F. Shanno, 1994. "Feature Article—Interior Point Methods for Linear Programming: Computational State of the Art," INFORMS Journal on Computing, INFORMS, vol. 6(1), pages 1-14, February.
    12. Qian, Xiaoyan, 2021. "Production planning and equity investment decisions in agriculture with closed membership cooperatives," European Journal of Operational Research, Elsevier, vol. 294(2), pages 684-699.
    13. Kjetil Høyland & Stein W. Wallace, 2001. "Generating Scenario Trees for Multistage Decision Problems," Management Science, INFORMS, vol. 47(2), pages 295-307, February.
    14. Wiedenmann, Susanne & Geldermann, Jutta, 2015. "Supply planning for processors of agricultural raw materials," European Journal of Operational Research, Elsevier, vol. 242(2), pages 606-619.
    15. Donald L. Keefer, 1994. "Certainty Equivalents for Three-Point Discrete-Distribution Approximations," Management Science, INFORMS, vol. 40(6), pages 760-773, June.
    16. Reis, Silvia Araújo & Leal, José Eugenio, 2015. "A deterministic mathematical model to support temporal and spatial decisions of the soybean supply chain," Journal of Transport Geography, Elsevier, vol. 43(C), pages 48-58.
    17. Sheu, Jiuh-Biing, 2016. "Supplier hoarding, government intervention, and timing for post-disaster crop supply chain recovery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 90(C), pages 134-160.
    18. Yuki Kinoshita & Takaki Nagao & Hiromasa Ijuin & Keisuke Nagasawa & Tetsuo Yamada & Surendra M. Gupta, 2023. "Utilization of Free Trade Agreements to Minimize Costs and Carbon Emissions in the Global Supply Chain for Sustainable Logistics," Logistics, MDPI, vol. 7(2), pages 1-21, June.
    19. da Silva, Marcelino Aurélio Vieira & de Almeida D’Agosto, Marcio, 2013. "A model to estimate the origin–destination matrix for soybean exportation in Brazil," Journal of Transport Geography, Elsevier, vol. 26(C), pages 97-107.
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