Multi-Vehicle Work Planning Using Market-Based Assignment Scores
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Solution Overview
Problem
Existing data collection systems face challenges in consistently planning multi-vehicle data collection across different companies due to biased vehicle assignment processes, inconsistent administration, and varying store characteristics, which impact efficiency and accuracy.
Innovation Solution
A data collection system that includes a work order planning system, execution system, and client feedback mechanisms to optimize vehicle allocation based on market characteristics, client preferences, and vehicle metrics, ensuring consistent and efficient data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If vehicle assignment is based on human discretion or simple rules, then the process is easy to operate, but the assignment becomes biased and inconsistent
Solution Approach 1:
The patent replaces manual or rule-based vehicle assignment mechanisms with an automated scoring system that objectively evaluates vehicle suitability. The system automatically calculates scores based on multiple criteria (vehicle type, store characteristics, task requirements) eliminating human discretion and bias while maintaining ease of operation through automated processing.
Solution Approach 2:
The system transforms the assignment process from qualitative human judgment to quantitative parameter-based scoring. By converting assignment criteria into measurable parameters (vehicle capacity, store size, task complexity) and calculating objective scores, the system achieves consistent and reliable assignments while remaining easy to operate through automated score calculation.
2Reliability
If vehicle assignment considers multiple factors (market characteristics, vehicle metrics, client preferences), then assignment reliability improves, but system complexity increases
Solution Approach 1:
The patent segments the complex assignment problem into independent scoring components. Each factor (vehicle type suitability, store characteristic match, task requirement alignment) is evaluated as a separate scoring element, allowing the system to handle multiple factors systematically without overwhelming complexity. The segmentation enables modular processing of different criteria.
Solution Approach 2:
The scoring system serves multiple functions simultaneously: it evaluates vehicle suitability, matches store characteristics, aligns task requirements, and generates assignments. This multi-functionality consolidates what would otherwise be separate complex processes into a unified system, improving reliability while managing complexity through functional integration.
3Measurement precision
If the system adapts to varying store characteristics and market conditions, then data collection accuracy improves, but planning complexity increases
Solution Approach 1:
The system applies different evaluation criteria and scoring weights specific to each store's local characteristics (size, layout, product categories) rather than using uniform standards. This local quality approach enables accurate data collection planning tailored to each unique store while managing complexity through standardized scoring templates that can be adapted locally.
Solution Approach 2:
The system performs preliminary analysis of store characteristics and market conditions before finalizing vehicle assignments. By pre-evaluating factors like store layout, product types, and historical data collection patterns, the system prepares optimized assignment plans in advance, improving accuracy while reducing real-time planning complexity through pre-computed assessments.
Data Source
AI summary
Methods, apparatus, systems disclosed herein include a work plan generator to generate market vehicle scores corresponding to available market vehicles, the market vehicle scores based on at least one client weight and at least one vehicle metric, and perform a comparison of the market vehicle scores associated with vehicle types to determine a first vehicle assignment and a second vehicle assignment, the first vehicle assignment associated with a first vehicle type, the second vehicle assignment associated with a second vehicle type; and generate first work plans, the first work plans corresponding to (a) a sequence of tasks and (b) a set of corresponding vehicles to execute the sequence of tasks, and a vehicle metrics generator to update vehicle metrics instructions associated with the first vehicle assignment and the second vehicle assignment to facilitate subsequent execution of second work plans, a first subset of the second work plans corresponding to first ones of the sequence of tasks executed by the first vehicle assignment and a second subset of the second work plans corresponding to second ones of the sequence of tasks executed by the second vehicle assignment.