Mathematical optimization model for efficient resource management in lawn, ornamental, and turf operations

Overview

This research proposes a mixed-integer linear programming (MILP) model designed to optimize the use of water, energy, and labor resources in lawn, ornamental plant, and turf operations. Current industry practice typically analyzes each resource in isolation, missing opportunities for efficiency that emerge when resource interactions are considered together. By translating agricultural and business requirements into mathematical constraints, this model aims to determine when, how much, and how often resources should be deployed for maximum efficiency at equivalent performance levels.

The solution targets decision-makers in the green industry who currently lack comprehensive tools to balance multiple resource inputs simultaneously. The approach has been demonstrated in a prior co-op engagement at Monsanto, where a similar mathematical optimization model reduced overproduction, operational costs, seed return delays, and weather-related risk in nursery planting allocation.

Technical specifications

Core methodology:

• Mixed-integer linear programming model capturing all parameters that directly affect resource allocation decisions
• Integrated analysis of water, energy, and labor as interconnected variables rather than independent inputs
• Mathematical decision variables, objective function, and constraints derived from real operational requirements
• Computational implementation with solution methods for practical deployment

Key features:

• Global optimization across multiple resource types simultaneously
• Ability to model trade-offs and interactions between water, energy, and labor usage
• Performance benchmarking against current single-resource optimization approaches
• Adaptable framework applicable to diverse planting operations and scales

Research approach is structured in four phases:

• Phase 1: Detailed examination of agriculture and business requirements
• Phase 2: Translation of requirements into mathematical model components
• Phase 3: Computational implementation and solution method development
• Phase 4: Performance validation using real operational data sets

Technology readiness level

This research is at an early stage, currently in the requirements-gathering and model design phase. No preliminary data has been generated because building the mathematical model requires deep technical process knowledge that will be obtained through industry partnership. The PI brings relevant prior experience developing optimization models for agricultural operations during a 2018 co-op engagement. The research team is actively seeking a collaboration partner to provide domain expertise during Phases 1 and 2 and to share real data for experimental validation in Phase 4. The expected outcome is a validated computational model ready for further development toward deployment in commercial planting operations.