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Demand-Driven Harvest Planning and Machinery Scheduling for Guayule

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Abstract

Guayule (Parthenium argentatum) is a perennial woody shrub native to the semi-arid region of northern Mexico and the Southwestern US regions, and it has great potential for the agricultural economy of these areas. In this paper, to address a demand-driven guayule harvest planning problem, we propose a mathematical optimization model for guayule harvest and machinery scheduling that maximize the economic benefits. The resulting model yields a large-scale mixed-integer linear optimization problem, considering time-window qualification, multi-machinery scheduling, resource limitations and late penalties for not harvesting on time, etc. Further, the optimization model is validated by some numerical results performing on 37 fields located in Pinal County, Arizona. The optimal scheduling routes are determined based on the Geographic Information System (GIS), and the harvesting cost breakdown for different demands is analyzed as well.

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Data Availability

All data generated or analyzed during this study are included in this article.

Code Availability

The computer code will be available upon reasonable request to the corresponding author.

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Funding

This material is based upon funding provided by the USDA-NIFA, Grant No. 2017-68005-26867. Any opinions, findings, conclusions, or recommendations expressed in this publication/work are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture.

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Authors and Affiliations

  1. Department of Systems & Industrial Engineering, University of Arizona, Tucson, AZ, USA

    Shunyu Yao & Neng Fan

  2. Department of Applied Economics, Oregon State University, Corvallis, OR, USA

    Clark Seavert

  3. Department of Agricultural & Resource Economics, University of Arizona, Tucson, AZ, USA

    Trent Teegerstrom

Authors
  1. Shunyu Yao
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  2. Neng Fan
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  3. Clark Seavert
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  4. Trent Teegerstrom
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Contributions

S.Y. and N.F. wrote the main manuscript text, while C.S. and T.T. provided relevant data and information for modeling and numerical experiments. All authors reviewed the manuscript.

Corresponding author

Correspondence to Neng Fan.

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Yao, S., Fan, N., Seavert, C. et al. Demand-Driven Harvest Planning and Machinery Scheduling for Guayule. Oper. Res. Forum 4, 9 (2023). https://doi.org/10.1007/s43069-022-00192-2

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