Optical Lab Order Routing Using Real-Time Load Balancing
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Solution Overview
Problem
Existing automated routing systems for optical manufacturing orders lack efficiency and optimality in load balancing among optical manufacturing laboratories due to insufficient information about order backlogs and changing conditions, leading to inefficient distribution and potential overloading of laboratories.
Innovation Solution
A system where optical manufacturing laboratories publish performance indicators (PIs) and logic blocks expressing priorities, enabling intelligent load balancing through machine learning and deep learning networks to optimize order routing based on real-time data and changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing automated routing systems use large and complicated sets of rules to route orders, then routing decisions can be made based on technical aspects of orders, but the systems become administratively onerous and hardwired, making them unresponsive to changes and resulting in inefficient load balancing
Solution Approach 1:
The patent implements dynamic load balancing by replacing static, hardwired routing rules with a dynamic system that continuously monitors laboratory performance indicators (capacity, backlog, lead time) and automatically adjusts order routing decisions in real-time. This allows the system to adapt to changing conditions without administrative intervention, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where laboratories publish performance indicators that are continuously monitored and used to adjust routing decisions. This closed-loop feedback system enables automatic adaptation to changing conditions, eliminating the need for complex, hardwired rules while maintaining responsiveness to changes in laboratory capacity and workload.
2Productivity
If routing systems lack sufficient information about order backlogs and changing conditions, then routing decisions can be made with simpler processes, but the distribution of orders becomes inefficient and laboratories may become overloaded
Solution Approach 1:
The patent creates a universal information exchange platform where all laboratories publish their performance indicators (capacity, backlog, lead time) to a common network. This multi-functional system simultaneously provides capacity information, enables load balancing, and supports routing decisions across the entire network, improving efficiency without requiring individual laboratories to maintain separate information systems.
Solution Approach 2:
The system introduces an intermediary routing mechanism that collects performance indicator information from all laboratories and uses this aggregated information to make optimized routing decisions. This intermediary layer ensures efficient order distribution by matching orders with appropriate laboratories based on real-time capacity information, preventing overload while maintaining simple individual laboratory operations.
Data Source
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AI summary
A method and apparatus are provided for optical manufacturing laboratories (OMLs) obtaining a plurality of performance indictors (PIs). The PIs are published or otherwise distributed among one or more participants (e.g., one or more ECPs and/or an administrative team of the OMLs). Logic blocks are generated for respective manufacturing orders to express priorities of the requirements/preferences of the respective orders. A combination of the logic blocks together with the plurality of PIs are applied as inputs to the selection rules (e.g., routing logic), which then output rankings of the OMLs in accordance with the priorities of the respective orders. The orders are then routed to an optimal combination of the OMLs based on the results from the selection rules.