Batch Consensus Planning Without Agent Code Exposure
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Solution Overview
Problem
Software-based solutions for supply chain consensus planning in large-scale supply chains require agents to expose their computer code, leading to inefficiencies and security risks, as they aggregate and execute codes from multiple delivery stations.
Innovation Solution
A batch computing architecture with a central coordinator uses elastic and serverless computing instances to facilitate consensus planning without exposing agent codes, employing ADMM to solve distributed optimization problems by breaking them into smaller components and iteratively negotiating consensus through price vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If software-based solutions aggregate and execute codes from multiple delivery station agents, then consensus planning can be achieved, but system complexity and security risks increase due to code exposure requirements
Solution Approach 1:
The patent introduces a central coordinator as an intermediary that mediates between delivery station agents. Instead of agents directly exposing and aggregating their codes, the coordinator receives encrypted computations and coordinates consensus through a trusted intermediary layer, thereby reducing system complexity and eliminating the need for direct code aggregation.
Solution Approach 2:
The patent extracts the code execution logic from the agents and relocates it to a centralized trusted environment. The agents only need to expose encrypted computations rather than their actual codes, separating the computation logic from the agents and reducing the complexity of code aggregation while maintaining automation.
2Productivity
If agents expose their computer code for consensus planning, then centralized optimization can be performed, but security risks and information leakage increase
Solution Approach 1:
The patent segments the optimization process into two distinct parts: (1) agents perform local computations and submit encrypted results to the coordinator, and (2) the coordinator performs centralized optimization on the encrypted data. This segmentation allows centralized optimization to proceed without requiring agents to expose their actual codes, thereby maintaining security while preserving productivity.
Solution Approach 2:
The patent uses encrypted copies of the agents' computations instead of their actual codes. The coordinator works with these encrypted copies to perform optimization, ensuring that the original code remains hidden and secure while still enabling centralized optimization efficiency.
3Reliability
If multiple agents' codes are aggregated and executed together, then comprehensive consensus can be reached, but processing time and computational overhead increase
Solution Approach 1:
The patent applies preliminary action by having agents perform their local computations and prepare encrypted results before submitting to the coordinator. This pre-processing eliminates the need for real-time code aggregation and execution, significantly reducing processing time while maintaining the reliability of consensus through the coordinator's centralized optimization on pre-prepared data.
Data Source
AI summary
A method for supply chain consensus planning using batch computing architecture may include instantiating an instance in a network; based on the instantiation of the instance, writing computational codes of local coordinators from scalable object storage in the network to batch computing devices in the network; (i) executing the computational codes, independently from one another, using the batch computing devices; (ii) based on executing the computational codes, writing first public variables, by the local coordinators independently from one another, from the batch computing devices to the scalable object storage; (iii) identifying, by the instance, based on the first public variables, a first quantity for a time period; iteratively repeating steps (i)-(iii) with updated data from the local coordinators until identifying a convergence on a second quantity, and sending, by the instance, an indication of the convergence to the local coordinators.