Facility Vehicle Scheduling Under Dynamic Task Changes
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Solution Overview
Problem
Existing in-facility transportation systems for materials in manufacturing facilities face inefficiencies due to inefficient vehicle routes and scheduling issues, particularly when accommodating dynamic changes and human operator schedules.
Innovation Solution
A facility operations management system that controls a fleet of vehicles using a non-transitory computer-readable medium to optimize point-to-point transportation tasks based on a hierarchy of optimization factors, including time and vehicle capabilities, to minimize infeasibility scores and ensure task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed schedules and static conditions are used for vehicle transportation, then operational simplicity is maintained, but transportation efficiency and adaptability to dynamic changes deteriorate
Solution Approach 1:
The patent implements dynamic scheduling that allows vehicle routes and schedules to be adjusted in real-time based on changing conditions such as material availability, workstation requirements, and vehicle status. This transforms the static transportation system into a dynamic one that can adapt to variations in production needs, thereby improving transportation efficiency while maintaining operational manageability through automated control.
Solution Approach 2:
The system incorporates continuous monitoring and feedback mechanisms that track vehicle locations, material flow requirements, and workstation status. This feedback loop enables the centralized control system to optimize vehicle deployment dynamically, resolving the contradiction by using real-time information to improve productivity without significantly increasing operational complexity for human operators.
2Device complexity
If inefficient vehicle routes are used, then route planning complexity is reduced, but transportation time and resource consumption increase
Solution Approach 1:
The patent replaces manual route planning with an automated computational system that uses algorithms to optimize vehicle routes. This substitution of mechanical/manual planning with an automated intelligent system reduces the actual transportation time and resource consumption while the complexity is managed through software rather than human cognitive processes.
Solution Approach 2:
The system dynamically changes route parameters based on real-time conditions, optimizing transportation paths to minimize travel time and resource consumption. The automated system handles the complexity of calculating and adjusting multiple route parameters simultaneously, achieving efficient transportation without proportionally increasing operational complexity.
3Stability of the object's composition
If transportation vehicles do not accommodate dynamic changes, then schedule stability is maintained, but task completion reliability deteriorates
Solution Approach 1:
The patent implements dynamic scheduling capabilities that allow the transportation system to adapt to changing production requirements, material availability, and vehicle status in real-time. This dynamic adjustment mechanism maintains task completion reliability by responding to dynamic changes while the centralized control system preserves overall schedule stability through coordinated management.
Solution Approach 2:
The system uses continuous feedback from monitoring vehicle status, material flow, and workstation requirements to adjust schedules dynamically. This feedback loop ensures that task completion reliability is maintained by detecting and responding to changes that would otherwise cause schedule failures, while the automated nature of the feedback process preserves schedule stability.
4Device complexity
If minimal variable expressions are used in optimization, then computational complexity is reduced, but optimization precision may deteriorate
Solution Approach 1:
The patent transforms the optimization problem by changing parameters and using variable expressions that capture the essential relationships in the transportation system. By carefully selecting and transforming variables, the system achieves effective optimization with reduced computational complexity while maintaining sufficient precision for practical transportation scheduling decisions.
Data Source
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AI summary
A system and computer-implemented method for optimizing operations of a facility having a number of associated vehicles providing point-to-point transportation of deliverables. The vehicles may comprise autonomous operations directed by a processor. The deliverables may comprise materials or components for facility operations. The facility may be able to dynamically adapt to changes in environment or operational demands.