Pricing Optimization System Decomposing Sub-Problems
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Solution Overview
Problem
Retailers face challenges in setting prices for multiple products that balance profitability, competitiveness, and adherence to constraints such as integer price requirements and price grid constraints, making it difficult to optimize pricing effectively.
Innovation Solution
A system and method that decompose the pricing problem into sub-problems using sub-problem item groups, employing non-linear optimization to generate target prices, and then using a mixed integer linear program to ensure final prices meet business constraints and grid requirements, ensuring prices are optimized while adhering to pricing objectives and constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive optimization model is used to consider all pricing constraints and cross-effects simultaneously, then pricing accuracy and profitability are improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent divides the comprehensive pricing optimization problem into multiple sub-problems based on product categories, market segments, or constraint types. Each sub-problem is solved independently using appropriate optimization models, and the results are integrated to form the final pricing strategy. This segmentation reduces computational complexity while maintaining pricing accuracy by focusing optimization efforts on specific product groups with similar characteristics and constraint profiles.
Solution Approach 2:
The patent extracts and handles integer pricing constraints and price grid constraints separately from the continuous optimization problem. The continuous optimization model first determines optimal prices without integer constraints, and then a separate rounding or adjustment process snaps these prices to the nearest valid grid points while minimizing deviation from the optimal continuous solution. This extraction allows the use of more efficient continuous optimization algorithms while still satisfying discrete pricing requirements.
2Reliability
If integer pricing requirements and price grid constraints are enforced during optimization, then pricing feasibility and operational compliance are improved, but pricing flexibility and optimization precision deteriorate
Solution Approach 1:
The patent introduces an intermediary rounding or adjustment mechanism that bridges the gap between continuous optimization results and discrete pricing requirements. The continuous optimization model generates flexible, precise price recommendations, and the intermediary process rounds these to the nearest valid price grid points while minimizing the deviation from the optimal continuous solution. This intermediary layer maintains both pricing flexibility (through the continuous model) and operational compliance (through the rounding to grid points).
Solution Approach 2:
The patent changes the state of pricing parameters from continuous to discrete by implementing a two-stage approach: first solving the optimization problem with continuous price parameters to maintain flexibility and precision, then transforming these continuous parameters into discrete values that satisfy integer requirements and price grid constraints. This parameter transformation allows the system to enjoy the benefits of both continuous optimization and discrete compliance.
3Productivity
If the pricing problem is decomposed into sub-problems, then computational efficiency and processing speed are improved, but model completeness and constraint satisfaction may deteriorate
Solution Approach 1:
The patent merges the solutions from multiple sub-problems by integrating the optimized prices across different product groups or segments. After solving each sub-problem independently, the system combines the results and applies global adjustments to ensure that cross-product constraints and overall business objectives are satisfied. This merging process maintains computational efficiency by allowing parallel processing of sub-problems while ensuring model completeness through subsequent integration and validation steps.
Data Source
AI summary
Computer-implemented systems and methods for regular pricing optimization. A system can include decomposing a pricing situation into sub-problems. A non-linear optimization problem is solved to determine continuous optimal prices. A mixed integer linear programming problem is solved to snap prices to grid points.