Hybrid Optimization of Distribution Networks Under Real-Time Constraints

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Solution Overview

Problem

Existing heuristic systems for network optimization fail to incorporate critical constraints and channel limitations, leading to impractical distribution plans and sub-optimality, and are insensitive to real-time changes.

Innovation Solution

A hybrid optimization framework using a mixed integer linear programming framework and trained optimization models to generate optimized fulfillment data structures, incorporating demand and channel capacities, and dynamically updating to account for real-time changes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If existing heuristic systems generate distribution plans without incorporating critical constraints and channel limitations, then the distribution plans are generated quickly, but the distribution plans become impractical and sub-optimal

Engineering Contradiction:

Improvedistribution plan generation speedVSAvoiddistribution plan practicality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system dynamically adjusts the optimization approach by combining heuristic methods for rapid initial plan generation with mixed integer linear programming (MILP) for constraint satisfaction. The hybrid framework allows the system to switch between speed-oriented and accuracy-oriented modes depending on the specific constraints and requirements, resolving the contradiction between generation speed and plan practicality

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes key parameters by incorporating distribution channel capacities, node parameters, and critical constraints as explicit variables in the MILP formulation. This parameter transformation allows the system to evaluate and adjust distribution plans against real-world limitations, converting impractical heuristic outputs into practical optimized plans without completely sacrificing generation speed

Inventive Principle:

Principle #35Parameter changes

2Reliability

If rule-based sequential systems are used to correct impractical distribution plans, then constraints are addressed, but sub-optimality and maintainability challenges increase

Engineering Contradiction:

Improveconstraint satisfactionVSAvoidsystem maintainability

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system merges heuristic methods with mixed integer linear programming into a unified hybrid optimization framework. Instead of using separate rule-based sequential correction systems, the integration allows constraint satisfaction and optimization to occur simultaneously in a single coherent system, reducing complexity while maintaining both reliability and ease of maintenance

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The MILP formulation acts as an intermediary between the heuristic generation system and the final distribution plan. It serves as a mathematical mediator that systematically evaluates heuristic outputs against all constraints and channel capacities, providing a structured approach to correction that is more maintainable than ad-hoc rule-based systems

Inventive Principle:

Principle #24Intermediary (Mediator)

3Device complexity

If existing heuristic systems generate distribution plans periodically, then system complexity is reduced, but sensitivity to real-time changes is lost

Engineering Contradiction:

Improvesystem complexityVSAvoidreal-time change sensitivity

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The hybrid optimization system incorporates feedback mechanisms that monitor real-time changes in distribution network conditions, demand patterns, and channel capacities. This feedback loop triggers re-optimization when significant changes occur, allowing the system to maintain low complexity during stable periods while becoming highly adaptive when changes are detected

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system transitions from static periodic generation to dynamic event-driven optimization. The hybrid framework continuously monitors system state and automatically adjusts its operation based on real-time conditions, maintaining simplicity when no changes occur while providing immediate responsiveness to actual events, thus resolving the contradiction between complexity and adaptability

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20250217758A1Systems and methods for hybrid heuristic optimization of large scale distribution networks

Publication Date: 2025.07.03 WALMART APOLLO LLC

AI summary

Systems and methods of hybrid optimization of distribution networks are disclosed. A demand distribution optimization request for a target network is received. A heuristic demand for one or more demand nodes in the target network and a distribution channel capacity for a distribution channel connecting each of the one or more demand nodes and at least one distribution node is generated. An optimized fulfillment data structure representative of an optimized demand fulfillment is generated for the one or more demand nodes. The optimized fulfillment data structure is generated by applying a mixed integer linear programming framework. The optimized fulfillment data structure is stored in a data storage mechanism.