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Extreme weather events such as droughts have posed a significant risk to the agricultural economy of the semi-arid region of the Southwestern U.S. In this paper, a multistage stochastic optimization model is developed for harvest planning and machinery scheduling of guayule, a perennial woody shrub native to this region, and addresses the uncertainty posed by drought scenarios for harvesting in this region. We consider various aspects such as the yield of the crop as a stochastic parameter, time windows for crop quality, multi-machinery scheduling, the penalty for delayed harvesting, shortage, and inventory. The goal of the model is to minimize the expected total costs over the planning horizon. The results present long-term optimal harvest planning and machinery scheduling for crops, and optimal routes for harvesting based on the Geographic Information System (GIS). In addition, we perform analysis on the expected cost breakdown, the shortage cost, and planting plans for guayule, wheat, and cotton to offer a comprehensive comparison of the economic viability under various drought scenarios in this region. This study underscores incorporating drought scenarios into harvest planning and highlights the guayule's resilience for farmers in mitigating economic losses under drought scenarios.
Mahdavimanshadi et al. (Wed,) studied this question.