Multi-Agent Price Waterfall Optimization Engine
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current approaches to price waterfall optimization focus on a single element, neglecting the ripple effects on other elements and failing to integrate high-level business objectives with local objectives and multiple business constraints, limiting their effectiveness.
Innovation Solution
A multi-agent optimization engine that interacts among software agents and their environment to simultaneously optimize multiple elements of the price waterfall, such as initial list price, on invoice discount, off invoice rebate, and end of period discount, to meet user-defined revenue and profit objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If current approaches optimize only one element of the price waterfall, then the optimization process is simple and focused, but the ripple effects on other elements and the inability to meet high-level business objectives are neglected
Solution Approach 1:
The patent segments the price waterfall optimization into multiple independent elements (list price, on-invoice discount, off-invoice rebate, end-of-period discount) that can be optimized simultaneously. Each element is represented as a separate variable in the optimization model, allowing independent adjustment while considering their interdependencies through the multi-agent system.
Solution Approach 2:
The multi-agent optimization engine provides universal functionality to handle multiple business objectives (revenue maximization, margin optimization, constraint satisfaction) within a single unified system. The agents can operate under different objective functions and constraints, making the system adaptable to various business scenarios without requiring separate optimization processes.
2Use of energy by moving object
If current approaches focus on single-element optimization, then computational requirements are low, but the ability to factor high-level business objectives versus local objectives is limited
Solution Approach 1:
The optimization system dynamically adjusts its complexity based on the problem scope. For single-element optimization, the system operates with minimal computational overhead. When multiple elements and objectives are involved, the system automatically scales up computational resources and complexity, utilizing the multi-agent architecture to distribute computational tasks across multiple agents.
Solution Approach 2:
The system incorporates feedback mechanisms where agents continuously monitor the impact of their actions on overall business objectives and adjust their optimization strategies accordingly. This feedback loop enables the system to integrate high-level business objectives with local optimization decisions, ensuring that computational resources are efficiently allocated based on actual business needs.
3Ease of manufacture
If current approaches do not consider ripple effects, then the optimization is straightforward, but the side effects on other products and customers are not accounted for
Solution Approach 1:
The patent merges multiple optimization concerns into a single unified optimization problem. By combining the optimization of different price waterfall elements and their interdependencies into one integrated model, the system can simultaneously consider ripple effects on other products and customers while maintaining implementation simplicity through automated multi-agent coordination.
Solution Approach 2:
The multi-agent system acts as an intermediary between local optimization decisions and overall business objectives. Agents mediate the trade-offs between optimizing individual elements and maintaining the broader business context, ensuring that side effects on other products and customers are properly accounted for through collaborative decision-making.
4Productivity
If multiple price waterfall elements are optimized simultaneously, then comprehensive optimization is achieved, but the complexity of the optimization system increases
Solution Approach 1:
The system segments the complex optimization problem into multiple sub-problems handled by different agents. Each agent focuses on a specific price waterfall element or business objective, reducing individual complexity while achieving comprehensive optimization through coordinated interaction. This segmentation allows the system to handle multiple elements simultaneously without overwhelming complexity in any single component.
Solution Approach 2:
The multi-agent architecture uses copying and replication of optimization logic across multiple agents. Instead of implementing a completely new complex system for multi-element optimization, the patent replicates and adapts existing optimization frameworks across multiple agents, reducing overall system complexity while achieving comprehensive optimization capabilities.
Data Source
AI summary
A system optimizes price waterfall elements simultaneously using a multi-agent optimization engine. The multi-agent optimization engine may be implemented as an adaptive multiple agent system that includes multiple software agents. The agents interact among each other and their environment to optimize the price waterfall elements simultaneously in order to meet user-defined objectives for revenue and/or profit. The multi-agent optimization engine enables each price waterfall element (e.g., initial list price, an initial on invoice discount, an initial off invoice rebate, and an initial end of period discount) of the price waterfall to be optimized continuously and simultaneously.