Autonomous Item Movement Planning for Real-Time Resource Assignment
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Solution Overview
Problem
Existing systems face challenges in efficiently coordinating the movement of items within facilities due to the complexity of managing various variables, parameters, constraints, policies, and resources, often requiring manual intervention.
Innovation Solution
The implementation of an intelligent computing system that uses geographical and state data to generate a movement plan, identifying the optimal resource for each request by processing clusters of requests with similar priorities and conducting iterations to assign resources based on estimated time or distance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used to coordinate item movements, then flexibility and adaptability are maintained, but productivity and efficiency decrease due to complexity management overhead
Solution Approach 1:
The patent introduces an intelligent computing system as an intermediary between the complex coordination requirements and the physical item movement operations. This system processes geographical data, state data, and request priorities to generate optimized movement plans, effectively mediating the complexity of coordinating multiple resources and constraints while improving coordination efficiency
2Productivity
If automated systems are implemented to reduce manual intervention, then productivity increases, but the complexity of managing variables and constraints worsens
Solution Approach 1:
The intelligent computing system operates autonomously to generate movement plans by self-processing the input data (geographical data, state data, request priorities) according to predefined algorithms. The system serves itself by automatically clustering requests, evaluating resources, and generating assignments without requiring external intervention, thereby increasing productivity while the system internally manages the complexity of variables and constraints
3Measurement precision
If comprehensive data processing is performed to optimize resource allocation, then resource allocation quality improves, but processing time and computational complexity increase
Solution Approach 1:
The patent segments the comprehensive data processing into distinct phases: data acquisition (geographical data, state data, request priorities), data processing (clustering requests by priority, evaluating resources within clusters), and plan generation (creating movement plans and assignments). This segmentation allows the system to process data systematically, improving resource allocation accuracy while managing processing time through structured computation
Data Source
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AI summary
In general, various embodiments of the present disclosure provide methods, systems, computer-readable medium, and/or the like for coordinating movement of items within a facility. In various embodiments, a method is provided that comprises: obtaining requests that involve moving items between locations found at a facility; obtaining geographical data that comprises amounts of time involved in moving the items between the locations; obtaining state data that comprises current locations of resources available to execute the requests; and generating a movement plan that identifies a specific resource for each request by: processing the requests via iterations, wherein each iteration involves: processing the requests yet to be assigned a specific resource to identify a highest priority request; generating, based on the geographical and state data, an estimated amount of time for each eligible resource; and identifying, based on the estimated amount of time, the specific resource to execute the highest priority request.