Available at: https://digitalcommons.calpoly.edu/theses/3184
Date of Award
12-2025
Degree Name
MS in Mathematics
Department/Program
Mathematics
College
College of Science and Mathematics
Advisor
Dana Paquin
Advisor Department
Mathematics
Advisor College
College of Science and Mathematics
Abstract
The floral industry faces rising operational costs, increasingly strict environmental regulations, and high market volatility, all of which threaten the sustainability of flower production. Accurate demand forecasting and production planning are therefore critical to minimize waste and reduce costs while maintaining competitiveness.
This thesis develops mathematical models for optimization, forecasting, and production planning in the floral industry, extending recent work on production planning and trapezoidal demand modeling. We integrate time-series forecasting methods with optimization techniques from production planning and operations research to model both short-term demand fluctuations and medium-term capacity constraints. The mathematical framework links linear and nonlinear optimization, time-series analysis, and mathematical modeling to provide a comprehensive approach for improving decision-making in flower production.
By simulating and validating model performance on real and simulated data from floral producers, the research demonstrates how mathematical modeling can enhance forecasting accuracy, improve resource allocation, and reduce operating costs. These insights contribute to a broader understanding of how applied mathematics can support sustainability and efficiency in agricultural production systems.