Combine Routing Optimization for Crop Moisture Control
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Solution Overview
Problem
Large agricultural operations face complexity in determining the optimal movement of combines across multiple fields with varying crop maturity times and weather conditions, often leading to inefficient harvesting and missed deadlines due to the lack of consideration for specific crop characteristics and real-time updates.
Innovation Solution
A multi-stage prescriptive routing model engine is implemented, utilizing harvest input data and crop characteristics to generate a combine routing program that optimizes the movement of combines based on demand and scheduling, incorporating real-time updates and weather data to ensure crops are harvested within predetermined moisture content ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to determine combine movement, then simplicity of operation is maintained, but harvesting efficiency and ability to meet deadlines deteriorates due to inability to handle complexity
Solution Approach 1:
The patent replaces manual routing determination with an automated computer-based optimization system that uses algorithms to calculate optimal combine movements. The system substitutes human decision-making with computational processing, enabling handling of complex multi-field, multi-combine scenarios that manual methods cannot manage effectively.
Solution Approach 2:
The patent introduces a computer-based routing optimization system as an intermediary between harvest requirements and combine operations. This intermediary processes harvest data, crop characteristics, and combine availability to generate optimized routing plans, acting as a mediator that translates complex requirements into actionable schedules.
2Productivity
If combines are frequently moved between regions to harvest all fields, then harvesting coverage is improved, but time loss due to travel and coordination increases
Solution Approach 1:
The patent performs preliminary routing optimization before harvest operations begin. The system calculates optimal combine assignments and movement schedules in advance, considering field locations, crop maturity times, and combine availability. This preliminary planning minimizes travel time and coordination delays during actual harvest operations.
Solution Approach 2:
The patent implements dynamic routing optimization that adapts to changing harvest conditions. The system continuously monitors crop maturity, weather conditions, and combine availability, dynamically adjusting routing plans to minimize travel time while maximizing harvesting coverage. The optimization is not static but evolves with harvest progress.
3Manufacturing precision
If crop harvest time is optimized for specific moisture content, then crop quality is improved, but flexibility in scheduling deteriorates due to strict timing requirements
Solution Approach 1:
The patent incorporates crop moisture content as a critical parameter in the routing optimization. The system uses moisture content requirements to determine optimal harvest windows for each field, integrating these temporal constraints into the routing algorithm. This ensures combines are scheduled to harvest crops at the precise moisture content needed for quality.
Solution Approach 2:
The patent applies different harvest timing requirements to different fields based on their specific crop characteristics and moisture content needs. Each field receives customized harvest scheduling rather than a uniform approach, allowing the system to maintain scheduling flexibility overall while ensuring each field is harvested at its optimal time for quality.
4Measurement precision
If real-time updates and crop characteristics are incorporated into routing, then harvesting precision is improved, but system complexity and data processing requirements worsen
Solution Approach 1:
The patent implements feedback mechanisms where real-time data on crop moisture content, harvest progress, and combine status are continuously fed back into the routing optimization system. This feedback enables the system to adjust routing plans in real-time, maintaining high harvest timing precision while using automated data processing to manage the complexity of real-time information.
Data Source
AI summary
A routing processor implements a multi-stage prescriptive routing model engine based on harvest input data relating to the harvesting of crops at a plurality of locations by a plurality of combines and based on a harvest characteristic representing an attribute of the crops to be harvested by the combines. The multi-stage prescriptive routing model engine generates a combine routing program prescribing the movement of each combine between the locations and includes a demand stage configured to identify combine harvesting demand as a function of the harvest input data and the harvest characteristic and a scheduling stage configured to generate the combine routing program as a function of the harvesting demand.