Supply Chain Planner Sub-problem Decomposition
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Solution Overview
Problem
Existing supply chain planning systems face challenges in efficiently optimizing demand satisfaction due to large problem sizes and complexities, leading to increased computational power and time requirements, which hinder the ability of entities within the network to meet demand.
Innovation Solution
A system and method that utilize a supply chain planner to split the planning problem into a master-problem and sub-problems, decompose these sub-problems into independent units, and solve them independently to generate an optimized supply chain plan by converging the solutions, thereby reducing complexity and computational time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the supply chain planning problem is solved as a single large optimization problem, then the solution can satisfy all demand, but the computational power and time required increases significantly
Solution Approach 1:
The patent divides the large supply chain planning problem into multiple smaller sub-problems based on geographic regions, product categories, or time periods. Each sub-problem can be solved independently and in parallel, significantly reducing computational time while maintaining the ability to satisfy overall demand through aggregation of sub-problem solutions.
Solution Approach 2:
The patent introduces a hierarchical dimension to the optimization problem by creating a two-level structure: strategic level (master problem) for high-level decisions and operational level (sub-problems) for detailed planning. This dimensional transformation allows the system to handle large-scale problems efficiently by separating decision-making layers.
2Reliability
If the supply chain planning problem is solved as a single large optimization problem, then the solution can satisfy all demand, but the computational power required increases significantly
Solution Approach 1:
By segmenting the optimization problem into independent sub-problems, the patent enables parallel computation across multiple processors or computing nodes. This distribution of computational load reduces the total computational power required at any single point and allows efficient utilization of available computing resources.
Solution Approach 2:
The patent creates a modular optimization framework where the same solution methodology can be applied to multiple sub-problems simultaneously. This universal approach allows the system to handle different segments of the supply chain using consistent algorithms, improving computational efficiency and resource utilization.
3Adaptability or versatility
If the supply chain planning problem size grows, then more entities and constraints can be included, but the complexity of the problem increases
Solution Approach 1:
The patent organizes the expanding problem scope into structured segments (e.g., regional divisions, product families, time buckets) that maintain manageable complexity within each segment. This segmentation allows the system to incorporate more entities and constraints overall while keeping individual sub-problems computationally tractable.
Solution Approach 2:
The patent implements a dynamic problem decomposition approach where the granularity and structure of sub-problems can adapt based on the scale and characteristics of the overall supply chain. As the problem scope grows, the system dynamically adjusts the segmentation strategy to maintain optimal complexity levels.
Data Source
AI summary
A system and method is disclosed for optimizing supply chain planning problems associated with a supply chain network. The system includes a supply chain planner coupled with one or more supply chain entities. The supply chain planner determines a supply chain plan for managing the flow of one or more items through the supply chain network.