Autonomous Vehicle Handover Decisions for Dynamic Worker Allocation
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Solution Overview
Problem
Warehouse systems face inefficiencies due to the difficulty in predicting Stock Keeping Units (SKUs) and labor balancing in zone-based systems, leading to suboptimal use of resources and increased capital allocation for autonomous vehicles.
Innovation Solution
A control system dynamically allocates autonomous vehicles and workers by evaluating cost functions that consider travel time, worker availability, and task completion, allowing for handovers between workers and vehicles without the need for fixed zones, thereby optimizing resource utilization and reducing idle time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If zones are used to assign workers and robotic carts to specific areas, then worker travel time is reduced, but zone definition becomes difficult and labor balancing becomes suboptimal
Solution Approach 1:
The system dynamically adjusts worker-cart assignments and operational zones in real-time based on current workload, location, and task requirements rather than using fixed predetermined zones. This allows the system to optimize worker travel time while avoiding the complexity of defining and managing static zone boundaries.
Solution Approach 2:
The control system automatically performs labor balancing and resource allocation by evaluating cost functions that consider worker locations, cart positions, task priorities, and predicted SKU demand. This self-service approach eliminates the need for manual zone definition and labor distribution management.
2Area of stationary object
If fixed zones are established based on physical size, then zone area is simplified, but zones fail to efficiently match actual pick operations that vary seasonally or based on product demand
Solution Approach 1:
The system implements dynamic zone assignment where operational areas are not fixed but adapt in real-time based on predicted SKU clustering, seasonal demand patterns, and current order profiles. This allows the same physical warehouse space to be efficiently reconfigured for different operational requirements without redrawing zone boundaries.
Solution Approach 2:
The control system changes operational parameters including worker assignments, cart assignments, and effective operational zones based on predicted demand patterns and actual pick operations. This allows the system to adapt to seasonal variations and product demand changes while maintaining simple physical warehouse layout.
3Productivity
If zones are used to distribute work evenly, then labor balancing is attempted, but workloads across zones become drastically different
Solution Approach 1:
The control system continuously monitors actual pick operations, worker locations, cart positions, and workload distribution across different areas. This feedback is used to dynamically adjust assignments and rebalance workloads in real-time, preventing drastic workload differences from developing while maintaining high productivity.
Solution Approach 2:
The system automatically performs labor balancing by evaluating cost functions that consider current workload distribution, worker efficiency, and predicted task requirements. This self-service approach continuously optimizes work distribution without manual intervention, adapting to changing conditions as they occur.
4Reliability
If a large number of autonomous vehicles are allocated to handle peak demand, then service level agreements are met, but capital allocation becomes inefficient and equipment maintenance requirements increase
Solution Approach 1:
The system dynamically allocates autonomous vehicles to tasks based on real-time demand predictions, current cart utilization, and task priority. This allows the fleet to efficiently handle peak demand periods while minimizing the number of vehicles required during lower-demand periods, optimizing both service level agreement fulfillment and capital allocation.
Solution Approach 2:
The control system enables autonomous vehicles to be flexibly assigned to different task types and locations based on current needs rather than dedicating specific vehicles to fixed zones or functions. This multi-functional approach allows a smaller fleet to handle varied workloads efficiently, reducing the total number of vehicles required while maintaining reliability.
Data Source
AI summary
Methods and apparatus for making autonomous vehicle handover decisions are described. A handover decision involves deciding if an autonomous vehicle should be handed off from one worker to another worker. The methods allow for decisions to be made in real or near real time shortly before an autonomous vehicle changes location. Worker time, if a handover is not implemented, is considered including the amount of worker time involved with the worker moving with the autonomous vehicle to the new location as compared to a new worker meeting the autonomous vehicle at the new location or on the way to the new location. Handover decisions can consider worker distribution and/or order priority. Such factors can be used to weight one or more time based cost values with a cost value representation of the cost if a handover is not implemented vs implementing a handover being compared to make the handover decision.