IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i17p9660-d623563.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/17/9660/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/17/9660/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Andrew J. Higgins, 2002. "Australian Sugar Mills Optimize Harvester Rosters to Improve Production," Interfaces, INFORMS, vol. 32(3), pages 15-25, June.
    6. 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.
    7. Haim Mendelson & Tunay I. Tunca, 2007. "Strategic Spot Trading in Supply Chains," Management Science, INFORMS, vol. 53(5), pages 742-759, May.
    8. 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.
    9. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    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. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Behzadi, Golnar & O’Sullivan, Michael Justin & Olsen, Tava Lennon & Zhang, Abraham, 2018. "Agribusiness supply chain risk management: A review of quantitative decision models," Omega, Elsevier, vol. 79(C), pages 21-42.
    3. 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.
    4. Tuğçe Taşkıner & Bilge Bilgen, 2021. "Optimization Models for Harvest and Production Planning in Agri-Food Supply Chain: A Systematic Review," Logistics, MDPI, vol. 5(3), pages 1-27, August.
    5. Soto-Silva, Wladimir E. & Nadal-Roig, Esteve & González-Araya, Marcela C. & Pla-Aragones, Lluis M., 2016. "Operational research models applied to the fresh fruit supply chain," European Journal of Operational Research, Elsevier, vol. 251(2), pages 345-355.
    6. 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.
    7. Shunyu Yao & Neng Fan & Clark Seavert & Trent Teegerstrom, 2023. "Demand-Driven Harvest Planning and Machinery Scheduling for Guayule," SN Operations Research Forum, Springer, vol. 4(1), pages 1-25, March.
    8. Wishon, C. & Villalobos, J.R. & Mason, N. & Flores, H. & Lujan, G., 2015. "Use of MIP for planning temporary immigrant farm labor force," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 25-33.
    9. Yiping Jiang & Liangqi Chen & Yan Fang, 2018. "Integrated Harvest and Distribution Scheduling with Time Windows of Perishable Agri-Products in One-Belt and One-Road Context," Sustainability, MDPI, vol. 10(5), pages 1-13, May.
    10. Kate, Joeri ten & Teunter, Ruud & Kusumastuti, Ratih Dyah & van Donk, Dirk Pieter, 2017. "Bio-diesel production using mobile processing units: A case in Indonesia," Agricultural Systems, Elsevier, vol. 152(C), pages 121-130.
    11. Víctor M. Albornoz & Lia C. Araneda & Rodrigo Ortega, 2022. "Planning and scheduling of selective harvest with management zones delineation," Annals of Operations Research, Springer, vol. 316(2), pages 873-890, September.
    12. Gómez-Lagos, Javier E. & González-Araya, Marcela C. & Soto-Silva, Wladimir E. & Rivera-Moraga, Masly M., 2021. "Optimizing tactical harvest planning for multiple fruit orchards using a metaheuristic modeling approach," European Journal of Operational Research, Elsevier, vol. 290(1), pages 297-312.
    13. Mauricio Varas & Franco Basso & Paul Bosch & Juan Pablo Contreras & Raúl Pezoa, 2022. "A horizontal collaborative approach for planning the wine grape harvesting," Operational Research, Springer, vol. 22(5), pages 4965-4998, November.
    14. 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.
    15. Ramos, Francisco López & Batres, Rafael & De-la-Cruz-Márquez, Cynthia Griselle & Anzures, Melina López, 2023. "Optimization models for nopal crop planning with land usage expansion and government subsidy," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    16. Omar Ahumada & J. Villalobos, 2011. "A tactical model for planning the production and distribution of fresh produce," Annals of Operations Research, Springer, vol. 190(1), pages 339-358, October.
    17. Behzadi, Golnar & O'Sullivan, Michael Justin & Olsen, Tava Lennon & Scrimgeour, Frank & Zhang, Abraham, 2017. "Robust and resilient strategies for managing supply disruptions in an agribusiness supply chain," International Journal of Production Economics, Elsevier, vol. 191(C), pages 207-220.
    18. Cleber D. Rocco & Reinaldo Morabito, 2016. "Robust optimisation approach applied to the analysis of production / logistics and crop planning in the tomato processing industry," International Journal of Production Research, Taylor & Francis Journals, vol. 54(19), pages 5842-5861, October.
    19. Hu, Ming-Che & Chen, Yu-Hui & Huang, Li-Chun, 2014. "A sustainable vegetable supply chain using plant factories in Taiwanese markets: A Nash–Cournot model," International Journal of Production Economics, Elsevier, vol. 152(C), pages 49-56.
    20. Franco Basso & Juan Pablo Contreras & Raúl Pezoa & Alejandro Troncozo & Mauricio Varas, 2023. "Optimizing the wine transportation process from bottling plants to ports," Operational Research, Springer, vol. 23(2), pages 1-28, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9660-:d:623563. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.