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The Optimal Harvest Decisions for Natural and Artificial Maturation Mangoes under Uncertain Demand, Yields and Prices

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  • Sheng-I Chen

    (Department of Industrial Engineering and Management, College of Management, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan)

  • Wei-Fu Chen

    (Department of Industrial Engineering and Management, College of Management, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan)

Abstract

This study focuses on the decisions of picking, inventory, ripening, delivering, and selling mangoes in a harvesting season. Demand, supply, and prices are uncertain, and their probability density functions are fitted based on actual trading data collected from the largest spot market in Taiwan. A stochastic programming model is formulated to minimize the expected cost under the considerations of labor, storage space, shelf life, and transportation restrictions. We implement the sample-average approximation to obtain a high-quality solution of the stochastic program. The analysis compares deterministic and stochastic solutions to assess the uncertain effect on the harvest decisions. Finally, the optimal harvest schedule of each mango variety is suggested based on the stochastic program solution.

Suggested Citation

  • Sheng-I Chen & Wei-Fu Chen, 2021. "The Optimal Harvest Decisions for Natural and Artificial Maturation Mangoes under Uncertain Demand, Yields and Prices," Sustainability, MDPI, vol. 13(17), pages 1-17, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9660-:d:623563
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    References listed on IDEAS

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    1. Bohle, Carlos & Maturana, Sergio & Vera, Jorge, 2010. "A robust optimization approach to wine grape harvesting scheduling," European Journal of Operational Research, Elsevier, vol. 200(1), pages 245-252, January.
    2. Ferrer, Juan-Carlos & Mac Cawley, Alejandro & Maturana, Sergio & Toloza, Sergio & Vera, Jorge, 2008. "An optimization approach for scheduling wine grape harvest operations," International Journal of Production Economics, Elsevier, vol. 112(2), pages 985-999, April.
    3. Ahumada, Omar & Villalobos, J. Rene, 2011. "Operational model for planning the harvest and distribution of perishable agricultural products," International Journal of Production Economics, Elsevier, vol. 133(2), pages 677-687, October.
    4. Wenbo Zhang & Wilbert Wilhelm, 2011. "OR/MS decision support models for the specialty crops industry: a literature review," Annals of Operations Research, Springer, vol. 190(1), pages 131-148, October.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Béla Vizvári & Zoltán Lakner & Zsolt Csizmadia & Gergely Kovács, 2011. "A stochastic programming and simulation based analysis of the structure of production on the arable land," Annals of Operations Research, Springer, vol. 190(1), pages 325-337, October.
    7. Tim Noparumpa & Burak Kazaz & Scott Webster, 2015. "Wine Futures and Advance Selling Under Quality Uncertainty," Manufacturing & Service Operations Management, INFORMS, vol. 17(3), pages 411-426, July.
    8. Ahumada, Omar & Rene Villalobos, J. & Nicholas Mason, A., 2012. "Tactical planning of the production and distribution of fresh agricultural products under uncertainty," Agricultural Systems, Elsevier, vol. 112(C), pages 17-26.
    9. John R. Birge, 1997. "State-of-the-Art-Survey---Stochastic Programming: Computation and Applications," INFORMS Journal on Computing, INFORMS, vol. 9(2), pages 111-133, May.
    10. Ridier, Aude & Chaib, Karim & Roussy, Caroline, 2016. "A Dynamic Stochastic Programming model of crop rotation choice to test the adoption of long rotation under price and production risks," European Journal of Operational Research, Elsevier, vol. 252(1), pages 270-279.
    11. J V Caixeta-Filho, 2006. "Orange harvesting scheduling management: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(6), pages 637-642, June.
    12. Haim Mendelson & Tunay I. Tunca, 2007. "Strategic Spot Trading in Supply Chains," Management Science, INFORMS, vol. 53(5), pages 742-759, May.
    13. Lodree Jr., Emmett J. & Uzochukwu, Benedict M., 2008. "Production planning for a deteriorating item with stochastic demand and consumer choice," International Journal of Production Economics, Elsevier, vol. 116(2), pages 219-232, December.
    14. Miller, W. A. & Leung, L. C. & Azhar, T. M. & Sargent, S., 1997. "Fuzzy production planning model for fresh tomato packing," International Journal of Production Economics, Elsevier, vol. 53(3), pages 227-238, December.
    15. Andrew J. Higgins, 2002. "Australian Sugar Mills Optimize Harvester Rosters to Improve Production," Interfaces, INFORMS, vol. 32(3), pages 15-25, June.
    16. Guan, Z. & Philpott, A.B., 2011. "A multistage stochastic programming model for the New Zealand dairy industry," International Journal of Production Economics, Elsevier, vol. 134(2), pages 289-299, December.
    17. Béla Vizvári & Zoltán Lakner, 2014. "A stochastic programming based analysis of the field use in a farm," Annals of Operations Research, Springer, vol. 219(1), pages 231-242, August.
    18. José Vicente Caixeta-Filho & Jan Maarten van Swaay-Neto & Antonio de Pádua Wagemaker, 2002. "Optimization of the Production Planning and Trade of Lily Flowers at Jan de Wit Company," Interfaces, INFORMS, vol. 32(1), pages 35-46, February.
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