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Improving harvesting and transport planning within a sugar value chain

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  • A J Higgins

    (CSIRO Sustainable Ecosystems, Queensland Bioscience Precinct)

  • L A Laredo

    (CSIRO Sustainable Ecosystems, Queensland Bioscience Precinct)

Abstract

Recent economic pressures have forced the Australian sugar industry to achieve better integration and optimization of the cane harvesting and transport sectors of the value chain. The logistical and economic complexity of the harvesting and transport system was captured through the development of a modelling framework that effectively links several component models that describe the parts of the system. Through engaging in participatory research with representatives of a sugar region located at the north-east coast of Australia, we use this modelling framework to address some key industry issues in rationalizing rail track infrastructure and re-organizing harvesting. These issues were addressed by building component models for the modelling framework in the field of location science, namely the capacitated P-Median problem and spatial clustering. By applying the modelling framework and its component models to the case-study region, we explored a range of scenarios with a net cost reduction of up to AU$2 260 000 per year.

Suggested Citation

  • A J Higgins & L A Laredo, 2006. "Improving harvesting and transport planning within a sugar value chain," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(4), pages 367-376, April.
    • Handle: RePEc:pal:jorsoc:v:57:y:2006:i:4:d:10.1057_palgrave.jors.2602024
    DOI: 10.1057/palgrave.jors.2602024
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    1. Ruben, Ruerd & Moll, Henk & Kuyvenhoven, Arie, 1998. "Integrating agricultural research and policy analysis: analytical framework and policy applications for bio-economic modelling," Agricultural Systems, Elsevier, vol. 58(3), pages 331-349, November.
    2. Thomas A. Feo & Mauricio G. C. Resende & Stuart H. Smith, 1994. "A Greedy Randomized Adaptive Search Procedure for Maximum Independent Set," Operations Research, INFORMS, vol. 42(5), pages 860-878, October.
    3. Gigler, J. K. & Hendrix, E. M. T. & Heesen, R. A. & van den Hazelkamp, V. G. W. & Meerdink, G., 2002. "On optimisation of agri chains by dynamic programming," European Journal of Operational Research, Elsevier, vol. 139(3), pages 613-625, June.
    4. Kropff, M. J. & Bouma, J. & Jones, J. W., 2001. "Systems approaches for the design of sustainable agro-ecosystems," Agricultural Systems, Elsevier, vol. 70(2-3), pages 369-393.
    5. Higgins, Andrew J. & Muchow, Russell C., 2003. "Assessing the potential benefits of alternative cane supply arrangements in the Australian sugar industry," Agricultural Systems, Elsevier, vol. 76(2), pages 623-638, May.
    6. Gladwin, Christina H. & Peterson, Jennifer S. & Mwale, Abiud C., 2002. "The Quality of Science in Participatory Research: A Case Study from Eastern Zambia," World Development, Elsevier, vol. 30(4), pages 523-543, April.
    7. Andrew J. Higgins, 2002. "Australian Sugar Mills Optimize Harvester Rosters to Improve Production," Interfaces, INFORMS, vol. 32(3), pages 15-25, June.
    8. Manuel Laguna & Thomas A. Feo & Hal C. Elrod, 1994. "A Greedy Randomized Adaptive Search Procedure for the Two-Partition Problem," Operations Research, INFORMS, vol. 42(4), pages 677-687, August.
    9. Bernet, T. & Ortiz, O. & Estrada, R. D. & Quiroz, R. & Swinton, S. M., 2001. "Tailoring agricultural extension to different production contexts: a user-friendly farm-household model to improve decision-making for participatory research," Agricultural Systems, Elsevier, vol. 69(3), pages 183-198, September.
    10. Douthwaite, B. & Keatinge, J. D. H. & Park, J. R., 2002. "Learning selection: an evolutionary model for understanding, implementing and evaluating participatory technology development," Agricultural Systems, Elsevier, vol. 72(2), pages 109-131, May.
    11. Alberto, I. & Azcarate, C. & Mallor, F. & Mateo, P. M., 2002. "Optimization with simulation and multiobjective analysis in industrial decision-making: A case study," European Journal of Operational Research, Elsevier, vol. 140(2), pages 373-383, July.
    12. Agrell, Per J. & Stam, Antonie & Fischer, Gunther W., 2004. "Interactive multiobjective agro-ecological land use planning: The Bungoma region in Kenya," European Journal of Operational Research, Elsevier, vol. 158(1), pages 194-217, October.
    13. Martin, Adrienne & Sherington, John, 1997. "Participatory research methods--Implementation, effectiveness and institutional context," Agricultural Systems, Elsevier, vol. 55(2), pages 195-216, October.
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    Cited by:

    1. Higgins, Andrew & Thorburn, Peter & Archer, Ainsley & Jakku, Emma, 2007. "Opportunities for value chain research in sugar industries," Agricultural Systems, Elsevier, vol. 94(3), pages 611-621, June.
    2. Kamal Lamsal & Philip C. Jones & Barrett W. Thomas, 2016. "Continuous time scheduling for sugarcane harvest logistics in Louisiana," International Journal of Production Research, Taylor & Francis Journals, vol. 54(2), pages 616-627, January.
    3. Vera Sadovska & Lena Ekelund Axelson & Cecilia Mark-Herbert, 2020. "Reviewing Value Creation in Agriculture—A Conceptual Analysis and a New Framework," Sustainability, MDPI, vol. 12(12), pages 1-22, June.
    4. Leonardo Rivera-Cadavid & Pablo Cesar Manyoma-Velásquez & Diego F. Manotas-Duque, 2019. "Supply Chain Optimization for Energy Cogeneration Using Sugarcane Crop Residues (SCR)," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    5. Sadovska, V., 2018. "Sustainable value creation in the agricultural sector. A literature review," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 276984, International Association of Agricultural Economists.
    6. Esteban López-Milán & Lluis Plà-Aragonés, 2014. "A decision support system to manage the supply chain of sugar cane," Annals of Operations Research, Springer, vol. 219(1), pages 285-297, August.
    7. 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.
    8. Kamal Lamsal & Philip C. Jones & Barrett W. Thomas, 2017. "Sugarcane Harvest Logistics in Brazil," Transportation Science, INFORMS, vol. 51(2), pages 771-789, May.
    9. Sara Rodríguez & Lluis Plà & Javier Faulin, 2014. "New opportunities in operations research to improve pork supply chain efficiency," Annals of Operations Research, Springer, vol. 219(1), pages 5-23, August.
    10. 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.
    11. Piewthongngam, Kullapapruk & Pathumnakul, Supachai & Setthanan, Kanchana, 2009. "Application of crop growth simulation and mathematical modeling to supply chain management in the Thai sugar industry," Agricultural Systems, Elsevier, vol. 102(1-3), pages 58-66, October.
    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. 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.
    14. 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.
    15. Helenice de O. Florentino & Dylan F. Jones & Chandra Ade Irawan & Djamila Ouelhadj & Banafesh Khosravi & Daniela R. Cantane, 2022. "An optimization model for combined selecting, planting and harvesting sugarcane varieties," Annals of Operations Research, Springer, vol. 314(2), pages 451-469, July.
    16. Junqueira, Rogerio de Ávila Ribeiro & Morabito, Reinaldo, 2019. "Modeling and solving a sugarcane harvest front scheduling problem," International Journal of Production Economics, Elsevier, vol. 213(C), pages 150-160.
    17. Camila de Lima & Antonio Roberto Balbo & Thiago Pedro Donadon Homem & Helenice de Oliveira Florentino Silva, 2017. "A hybrid approach combining interior-point and branch-and-bound methods applied to the problem of sugar cane waste," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(2), pages 147-164, February.
    18. 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.

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