Autonomous Item Movement Planning for Facility Resource Allocation
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Solution Overview
Problem
Existing systems face challenges in efficiently managing the complexities of coordinating item movements within facilities, including varying variables, parameters, constraints, and policies, which requires significant manual intervention and is prone to errors.
Innovation Solution
An intelligent computing system that automates the coordination of item movements by obtaining requests, geographical data, and state data, generating a movement plan that identifies optimal resources, and communicating assignments to execute the movements efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual coordination of item movements is used, then flexibility in handling complex variables and constraints is maintained, but productivity is reduced and errors increase
Solution Approach 1:
The system enables self-service automation by having the coordination system automatically manage resource allocation, movement planning, and constraint satisfaction without human intervention. The system processes movement requests, evaluates constraints, and executes coordination decisions autonomously, eliminating manual oversight while maintaining operational flexibility.
Solution Approach 2:
The patent replaces manual mechanical coordination processes with an automated computing system that processes movement data, evaluates constraints, and generates coordination plans. This substitution transforms human-based decision-making into algorithm-driven automation, significantly improving productivity while reducing errors.
2Productivity
If automated systems are implemented, then productivity and coordination efficiency are improved, but device complexity increases
Solution Approach 1:
The coordination system is segmented into distinct functional modules: request processing module, constraint evaluation module, resource allocation module, and movement planning module. Each module handles specific aspects of coordination independently, reducing overall system complexity while maintaining high productivity through specialized processing.
Solution Approach 2:
The system employs universal data structures and processing algorithms that can handle multiple types of movement requests, resource types, and constraint categories through a single integrated platform. This multi-functionality reduces the need for separate specialized systems, thereby managing complexity while improving coordination efficiency.
3Reliability
If comprehensive constraint evaluation is performed, then movement accuracy and reliability are improved, but processing time increases
Solution Approach 1:
The system performs preliminary evaluation of constraints and resource availability before final movement execution. By pre-assessing feasibility, identifying potential conflicts, and preparing resource allocations in advance, the system ensures reliable movement execution while minimizing actual processing time during critical operations.
Solution Approach 2:
The constraint evaluation process is dynamic and adaptive, adjusting the depth of evaluation based on request priority, resource availability, and system state. High-priority requests receive comprehensive evaluation for reliability, while lower-priority requests use streamlined evaluation to reduce processing time, optimizing the balance between accuracy and speed.
Data Source
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.