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A two-stage stochastic programming with recourse model for determining robust planting plans in horticulture

Author

Listed:
  • K Darby-Dowman

    (Brunel University)

  • S Barker

    (Brunel University)

  • E Audsley

    (Silsoe Research Institute)

  • D Parsons

    (Silsoe Research Institute)

Abstract

A two-stage stochastic programming with recourse model for the problem of determining optimal planting plans for a vegetable crop is presented in this paper. Uncertainty caused by factors such as weather on yields is a major influence on many systems arising in horticulture. Traditional linear programming models are generally unsatisfactory in dealing with the uncertainty and produce solutions that are considered to involve an unacceptable level of risk. The first stage of the model relates to finding a planting plan which is common to all scenarios and the second stage is concerned with deriving a harvesting schedule for each scenario. Solutions are obtained for a range of risk aversion factors that not only result in greater expected profit compared to the corresponding deterministic model, but also are more robust.

Suggested Citation

  • K Darby-Dowman & S Barker & E Audsley & D Parsons, 2000. "A two-stage stochastic programming with recourse model for determining robust planting plans in horticulture," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(1), pages 83-89, January.
    • Handle: RePEc:pal:jorsoc:v:51:y:2000:i:1:d:10.1057_palgrave.jors.2600858
    DOI: 10.1057/palgrave.jors.2600858
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    Cited by:

    1. Maqsood, Imran & Huang, Guo H. & Scott Yeomans, Julian, 2005. "An interval-parameter fuzzy two-stage stochastic program for water resources management under uncertainty," European Journal of Operational Research, Elsevier, vol. 167(1), pages 208-225, November.
    2. A J Higgins & C J Miller & A A Archer & T Ton & C S Fletcher & R R J McAllister, 2010. "Challenges of operations research practice in agricultural value chains," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(6), pages 964-973, June.
    3. S Karabuk, 2008. "Production planning under uncertainty in textile manufacturing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 510-520, April.
    4. Jahantab, Mahboubeh & Abbasi, Babak & Le Bodic, Pierre, 2023. "Farmland allocation in the conversion from conventional to organic farming," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1103-1119.
    5. Ana Esteso & M. M. E. Alemany & Angel Ortiz & Shaofeng Liu, 2022. "Optimization model to support sustainable crop planning for reducing unfairness among farmers," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(3), pages 1101-1127, September.
    6. Lin, Q.G. & Huang, G.H., 2010. "An inexact two-stage stochastic energy systems planning model for managing greenhouse gas emission at a municipal level," Energy, Elsevier, vol. 35(5), pages 2270-2280.
    7. Liu, Qingyu & Shen, Bin & Wen, Xin, 2023. "Role of climate-smart agriculture in fighting against climate change in competitive supply chains," International Journal of Production Economics, Elsevier, vol. 264(C).
    8. M. Ortuño & B. Vitoriano, 2011. "A goal programming approach for farm planning with resources dimensionality," Annals of Operations Research, Springer, vol. 190(1), pages 181-199, October.
    9. Cai, Y.P. & Huang, G.H. & Tan, Q. & Chen, B., 2011. "Identification of optimal strategies for improving eco-resilience to floods in ecologically vulnerable regions of a wetland," Ecological Modelling, Elsevier, vol. 222(2), pages 360-369.
    10. Azaiez, M. N., 2002. "A model for conjunctive use of ground and surface water with opportunity costs," European Journal of Operational Research, Elsevier, vol. 143(3), pages 611-624, December.
    11. Jensen, Hector A. & Maturana, Sergio, 2002. "A possibilistic decision support system for imprecise mathematical programming problems," International Journal of Production Economics, Elsevier, vol. 77(2), pages 145-158, May.
    12. Zhenfang Liu & Yang Zhou & Gordon Huang & Bin Luo, 2019. "Risk Aversion Based Inexact Stochastic Dynamic Programming Approach for Water Resources Management Planning under Uncertainty," Sustainability, MDPI, vol. 11(24), pages 1-22, December.
    13. Tingey-Holyoak, Joanne & Pisaniello, John & Buss, Peter & Mayer, Wolfgang, 2021. "The importance of accounting-integrated information systems for realising productivity and sustainability in the agricultural sector," International Journal of Accounting Information Systems, Elsevier, vol. 41(C).
    14. Lin, Q.G. & Huang, G.H. & Bass, B. & Qin, X.S., 2009. "IFTEM: An interval-fuzzy two-stage stochastic optimization model for regional energy systems planning under uncertainty," Energy Policy, Elsevier, vol. 37(3), pages 868-878, March.
    15. 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.
    16. Marius Drechsler & Andreas Holzapfel, 2022. "Decision Support in Horticultural Supply Chains: A Planning Problem Framework for Small and Medium-Sized Enterprises," Agriculture, MDPI, vol. 12(11), pages 1-25, November.
    17. Kusumastuti, Ratih Dyah & Donk, Dirk Pieter van & Teunter, Ruud, 2016. "Crop-related harvesting and processing planning: a review," International Journal of Production Economics, Elsevier, vol. 174(C), pages 76-92.
    18. Tri-Dung Nguyen & Uday Venkatadri & Tri Nguyen-Quang & Claver Diallo & Duc-Huy Pham & Huu-Thanh Phan & Le-Khai Pham & Phu-Cuong Nguyen & Michelle Adams, 2024. "Stochastic Modelling Frameworks for Dragon Fruit Supply Chains in Vietnam under Uncertain Factors," Sustainability, MDPI, vol. 16(6), pages 1-29, March.
    19. Cao, M.F. & Huang, G.H. & Lin, Q.G., 2010. "Integer programming with random-boundary intervals for planning municipal power systems," Applied Energy, Elsevier, vol. 87(8), pages 2506-2516, August.
    20. ZhenFang Liu & GuoHe Huang, 2009. "Dual-Interval Two-Stage Optimization for Flood Management and Risk Analyses," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(11), pages 2141-2162, September.
    21. Li, Xiaojuan & Kang, Shaozhong & Niu, Jun & Du, Taisheng & Tong, Ling & Li, Sien & Ding, Risheng, 2017. "Applying uncertain programming model to improve regional farming economic benefits and water productivity," Agricultural Water Management, Elsevier, vol. 179(C), pages 352-365.
    22. Mike G. Tsionas & Dionisis Philippas & Constantin Zopounidis, 2023. "Exploring Uncertainty, Sensitivity and Robust Solutions in Mathematical Programming Through Bayesian Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 205-227, June.
    23. Vitoriano, B. & Ortuno, M. T. & Recio, B. & Rubio, F. & Alonso-Ayuso, A., 2003. "Two alternative models for farm management: Discrete versus continuous time horizon," European Journal of Operational Research, Elsevier, vol. 144(3), pages 613-628, February.

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