Demand-Driven Harvest Planning and Machinery Scheduling for Guayule

Shunyu Yao, Neng Fan (), Clark Seavert and Trent Teegerstrom
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Shunyu Yao: University of Arizona
Neng Fan: University of Arizona
Clark Seavert: Oregon State University
Trent Teegerstrom: University of Arizona

SN Operations Research Forum, 2023, vol. 4, issue 1, 1-25

Abstract: 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.

Keywords: Guayule harvest planning; Machinery scheduling; Crop rotation for harvesting; Large-scale mixed-integer linear optimization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s43069-022-00192-2

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