Harvesting Schedule Recommender Using Local Search Heuristic
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Solution Overview
Problem
Scheduling the harvesting of multiple fields with limited equipment to maximize crop yield is challenging due to optimal harvesting times, resource constraints, and varying field sizes, making it difficult to generate a feasible schedule that balances yield and resource allocation.
Innovation Solution
A computer-implemented harvesting schedule recommender system using a local search heuristic to iterate over candidate schedules, evaluating them based on a solution scoring scheme that considers yield curves and resource constraints, ensuring the recommended schedule maximizes total crop yield while adhering to harvesting constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If harvesting equipment is limited, then resource allocation constraints are reduced, but total crop yield decreases
Solution Approach 1:
The system segments the harvesting problem into multiple candidate schedules, each representing different harvesting sequences and resource allocations. By evaluating multiple segmented schedules rather than one monolithic plan, the system finds optimal ways to distribute limited equipment across fields to maximize total yield.
Solution Approach 2:
The system dynamically adjusts harvesting schedules by iterating through candidate solutions and evaluating them against yield curves and resource constraints. The local search heuristic dynamically modifies schedules to improve yield while adapting to equipment limitations, rather than using static allocation.
2Quantity of substance
If fields are harvested at optimal time, then crop yield is maximized, but resource constraints cannot be satisfied
Solution Approach 1:
The system evaluates candidate schedules that may partially satisfy optimal harvesting times for some fields while accepting suboptimal timing for others. By considering partial satisfaction across multiple fields rather than attempting full optimization, the system finds feasible schedules that respect resource constraints while maximizing overall yield.
Solution Approach 2:
The system changes the parameter of harvesting time for different fields based on yield curves and resource availability. By adjusting harvesting timestamps in candidate schedules and evaluating their impact on total yield, the system finds optimal trade-offs between timing precision and constraint satisfaction.
3Reliability
If multiple constraints are considered, then schedule feasibility is improved, but computational complexity increases
Solution Approach 1:
The system uses a self-contained local search heuristic that automatically evaluates candidate schedules against all constraints (resource availability, field capacities, timing requirements). The heuristic serves itself by generating, evaluating, and refining schedules without external intervention, efficiently handling multiple constraints through iterative improvement.
Solution Approach 2:
The system replaces complex manual scheduling mechanics with a computational local search heuristic. Instead of manually balancing multiple constraints, the computational system automatically evaluates candidate schedules against yield curves and resource constraints, substituting algorithmic processing for manual optimization efforts.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for generating a recommended harvesting schedule. In embodiments, input data is obtained that includes a respective representation of a crop yield curve for each crop zone in a plurality of crop zones and a set of harvesting constraints including at least one harvesting resource constraint. Based on the input data, a local search heuristic iterates over a plurality of candidate harvesting schedules to identify a current best candidate harvesting schedule and outputs the current best harvesting schedule as the recommended harvesting schedule. The iteration may include determining a solution score for each candidate harvesting schedule based at least upon a measure of a degree to which the candidate harvesting schedule satisfies the set of harvesting constraints and a total crop yield associated with the candidate harvesting schedule, and evaluating each candidate harvesting schedule based on the solution score determined therefor.