Supply Chain Resource Flow Modeling with Linear Optimization
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Solution Overview
Problem
Current supply chain modeling systems lack the ability to accurately calculate and visualize the flow of resources across multiple stages with constraints such as non-negativity, transportation costs, and demand satisfaction, leading to inefficiencies in resource allocation and transportation optimization.
Innovation Solution
A method and system utilizing a linear optimization model with modules for transportation network, mass balance, movement control, and non-negativity constraints to calculate minimum cost and distance, enforce resource movement, and report flow data through user interfaces, ensuring efficient resource flow and emission tracking across supply chain stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current supply chain modeling systems are used, then basic resource flow tracking is possible, but accurate calculation and visualization of resource flow across multiple stages with constraints cannot be achieved
Solution Approach 1:
The supply chain is divided into multiple discrete stages (supply nodes, first stages, second stages, third stages) with resource databases generated at each stage. This segmentation enables precise tracking and calculation of resource flow at each individual stage while maintaining overall system visibility, resolving the contradiction between measurement precision and system complexity.
Solution Approach 2:
A computing device acts as an intermediary between resource databases and user interfaces, performing the complex calculations and optimizations required for accurate resource flow measurement. This intermediary handles the computational complexity internally while presenting simplified accurate results to users, resolving the contradiction between precision and complexity.
2Reliability
If multiple constraint modules are implemented, then accurate resource flow optimization is achieved, but system complexity increases
Solution Approach 1:
Multiple constraint modules (transportation network module, mass balance module, movement control module, non-negativity constraint module) are merged into an integrated optimization system that processes all constraints simultaneously. This unified approach ensures reliable constraint enforcement while reducing the operational complexity that would arise from managing separate independent modules, resolving the contradiction between reliability and device complexity.
3Productivity
If minimum cost and distance calculation is performed, then transportation optimization is improved, but computational requirements increase
Solution Approach 1:
The transportation network module pre-calculates and stores minimum distance and cost information between supply nodes and stages in resource databases before optimization is needed. This preliminary action enables rapid query and decision-making during actual operations without requiring intensive real-time computation, resolving the contradiction between productivity improvement and energy consumption.
Data Source
AI summary
Implementations of a method of modeling a flow of resources may include generating a stage resource database when the resources reach one or more stages from one or more supply nodes. The method may also include using a mass balance module to maintain an outflow less than or equal to a supply at the one or more supply nodes and an inflow greater than or equal to demand at the one or more stages. The method may include enforcing movement of resources to or from one or more supply nodes of the supply chain to the one or more stages. The method may include using a non-negativity constraint module to maintain a non-negative amount to be transported. The method may also include using a flow generation module and one or more constraint thresholds to report to a user the flow of the resources using one or more computer interfaces.