Neng Fan, "Agricultural Decision-Making under Drought: Models, Algorithms, and Applications"

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Abstract: Agricultural operations management (AOM) under drought presents a rich class of multiscale and temporally coupled decision problems, spanning activities from individual fields to early-stage logistics. These problems include long-term planting and crop-rotation planning, land-use optimization, irrigation and seasonal water allocation, harvesting planning and scheduling, and the coordination of equipment, machinery, and labor. Beyond upstream production, AOM also interfaces with biomass logistics and supply-chain design. In the United States, much of the West, particularly the Southwest, consists of arid and semi-arid regions with limited and highly variable annual precipitation. Despite these climatic constraints, the Southwestern U.S. is an important producer of high-value specialty crops, making effective agricultural operations and resource management economically and strategically important.

This talk presents optimization models, computational algorithms, and decision insights for AOM in the Southwestern U.S. across strategic, tactical, and operational timescales. It draws on three streams of recent research: planting and crop-rotation planning, harvesting and machinery scheduling, and biomass supply-chain design for low-water-use, drought-tolerant, and heat-resistant crops. The models incorporate uncertainties associated with crop adoption, drought scenarios, water availability, and weather variability. Stochastic optimization models and algorithms address the resulting large-scale and temporally coupled problems. Computational case studies illustrate how these approaches support decisions concerning crop selection and rotation, irrigation allocation, machinery schedules, and processing-facility design. The results demonstrate how optimization can inform economically viable, environmentally sustainable, and socially responsible agricultural operations under water scarcity and climate stress.

Bio: Dr. Neng Fan is a Professor of Systems and Industrial Engineering at the University of Arizona and a faculty member of the Applied Mathematics Graduate Interdisciplinary Program (GIDP) and Statistics & Data Science GIDP. He received his PhD in Industrial and Systems Engineering from University of Florida. His research focuses on optimization under uncertainty and large-scale optimization, with applications in renewable energy integration, sustainable agriculture, and healthcare systems. His research has been supported by the NSF, DOE, USDA, and industry partners. He received the 2025 William Golomski Award from the Reliability and Maintainability Symposium and multiple Best Paper Awards at the IISE Annual Conferences.