Spatio-temporal Calendar Generation for Weather-Adaptive Field Scheduling
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Solution Overview
Problem
Current calendar and scheduling applications for field professionals lack optimization of resource allocation and site visit planning, failing to maximize productivity and customer engagement, especially in scenarios where weather and environmental conditions significantly impact service delivery across large spatial regions.
Innovation Solution
A system and method for generating a spatio-temporal calendar that predicts regions of interest and specific locations for service visits based on long-term weather predictions, time-independent factors, and travel constraints, using a hybrid approach combining process-based and machine learning features to dynamically determine optimal visit schedules and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual scheduling based on personal preferences and experiences is used, then flexibility and ease of operation are improved, but productivity and resource allocation optimization deteriorate
Solution Approach 1:
The system enables self-service through automated calendar generation where the scheduling system independently analyzes weather forecasts, service requirements, and field professional constraints to create optimized schedules without manual intervention, thereby maintaining ease of operation while significantly improving productivity
Solution Approach 2:
The patent replaces the mechanical manual scheduling process with an automated computational system that uses weather forecasting data, historical service patterns, and constraint optimization algorithms to generate schedules, substituting human cognitive processes with automated decision-making mechanisms that improve productivity while maintaining operational simplicity
2Ease of operation
If routine schedules are used for field professionals, then ease of operation is improved, but adaptability to weather and environmental conditions deteriorates
Solution Approach 1:
The system implements dynamic scheduling by continuously incorporating weather forecasts and environmental conditions into the calendar generation process, allowing schedules to adapt automatically to changing conditions while maintaining ease of operation through automated adjustments rather than manual re-planning
Solution Approach 2:
The patent applies preliminary action by integrating weather forecasting and environmental condition analysis into the schedule creation process before field professionals are assigned visits, enabling proactive adaptation to potential weather impacts rather than reactive adjustments, thus maintaining both ease of operation and adaptability
3Adaptability or versatility
If known calendar applications with weather predictions are used, then adaptability to weather conditions is improved, but resource allocation optimization and identification of previously unconsidered locations deteriorates
Solution Approach 1:
The system achieves multi-functionality by combining weather adaptability, resource allocation optimization, and geographic analysis into a single integrated calendar generation system that simultaneously performs multiple functions rather than requiring separate tools, thereby improving both adaptability and productivity
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors weather forecasts, service outcomes, and field professional constraints, using this feedback to iteratively optimize calendar generation and identify previously unconsidered service locations, thus improving both adaptability and resource allocation efficiency
Data Source
AI summary
A system, computer program product, and method are presented for forecasting a spatio-temporal calendar including predicted regions of interest based on time dependent factors such as long-term weather predictions, time-independent factors, and travel constraints. The method includes collecting information and constraints with respect to service visits. At least a portion of the collected information and constraints are directed toward weather and climate. The method also includes predicting weather and climate impacts on at least one geographical region of interest. The method further includes predicting, subject to the predictions of weather and climate impacts, one or more locations of interest within the at least one geographical region of interest that would be impacted by one or more service visits. The method also includes generating one or more spatio-temporal calendars that include the one or more locations of interest scheduled for the one or more service visits.