Fluid Network Control Using Adjustable Server Effort Proportions
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Solution Overview
Problem
Existing fluid processing network control systems face challenges in handling uncertain service rates, as they often require reserving server effort for worst-case scenarios, leading to sub-optimal policies and implementation difficulties in continuous time systems.
Innovation Solution
A robust counterpart of separated continuous linear programming (SCLP) is used to formulate an optimal control problem as proportions of server effort, allowing for adjustable policies that do not reserve server effort for uncertainty, enabling efficient adjustment of parameters and easier implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a robust fluid approach is used to treat uncertainty in a deterministic manner, then the policy performs closely against the optimal policy, but server effort is reserved to meet the worst case leading to sub-optimal performance
Solution Approach 1:
The patent transforms the static robust optimization policy into a dynamic adjustable policy by introducing a continuous parameter α that can be tuned based on observed service rates. This allows the system to adapt between conservative (α=0) and optimistic (α=1) modes, dynamically adjusting server effort allocation rather than permanently reserving capacity for worst-case scenarios.
Solution Approach 2:
The patent introduces a new parameter α (alpha) that controls the degree of robustness in the policy. By varying this parameter continuously between 0 and 1, the system can adjust its behavior from fully robust (conservative) to fully nominal (optimistic), enabling flexible trade-offs between reliability and productivity without committing to a fixed worst-case approach.
2Reliability
If a robust optimization framework is used to cover uncertain service rates, then the policy is more reliable, but the fluid model becomes difficult to practically implement
Solution Approach 1:
The patent separates the robust optimization problem into two distinct components: (1) an offline SCLP formulation that computes base policies, and (2) an online adjustment mechanism that modifies these policies using the parameter α. This segmentation makes the system more implementable by combining a tractable offline computation with a simple online adjustment, rather than requiring complex real-time robust optimization.
Solution Approach 2:
The parameter α serves as an intermediary between the nominal policy and the robust policy. Instead of directly implementing complex robust optimization, the system uses α as a mediator to adjust the nominal SCLP policies, simplifying the implementation while maintaining robustness properties.
3Ease of manufacture
If a single non-adjustable policy is used for uncertain service rates, then the policy is simpler to implement, but it cannot adapt to observed service rates leading to inefficiency
Solution Approach 1:
The patent makes the previously static policy dynamic by introducing adjustable parameter α that can be modified based on observed service rates. The policy transitions from a fixed robust or nominal approach to an adaptive mechanism that learns from observations and adjusts server effort allocation accordingly, improving efficiency while maintaining implementation simplicity.
Solution Approach 2:
The patent incorporates feedback mechanisms where observed service rates are used to adjust the parameter α. This feedback loop allows the system to learn from actual system performance and adapt its policy accordingly, moving from open-loop static policies to closed-loop adaptive policies that improve productivity through continuous learning.
Data Source
AI summary
An example system includes a processor to receive parameters of a fluid network control system. The processor can formulate an optimal control problem as proportions of server effort. The processor can also solve the optimal control problem using a robust counterpart of a separated continuous linear programming (SCLP). The processor can further adjust a parameter of the fluid network control system based on the optimal solution.