Production Network Simulation Optimization for Dynamic Events

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Solution Overview

Problem

Current production planning systems lack long-term optimization capabilities due to reliance on static, rule-based heuristic systems that do not provide future visibility feedback and fail to adjust to dynamic events, limiting their ability to generate optimal production schedules beyond a day-by-day basis.

Innovation Solution

A system utilizing a non-transitory memory and processor to implement an optimization engine that maximizes adherence percentage through an objective function, optimizing the sequence of operations for sub-nodes of a target production node, including parameters like resource type, quantity, timeframe, and sub-node selection, and outputs an optimized production data structure.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If static, rule-based heuristic systems are used for production planning, then the system is simple to implement and operate, but it cannot provide long-term optimization or adapt to dynamic events

Engineering Contradiction:

Improveadaptability to dynamic eventsVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent transitions from static, rule-based heuristic systems to a dynamic simulation-optimization system that can adapt to changing conditions. The simulation engine continuously models production scenarios and the optimization engine dynamically adjusts schedules based on simulated outcomes, enabling the system to respond to dynamic events while maintaining manageable complexity through automated decision-making.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements feedback loops where simulation results inform optimization decisions, and optimization outcomes are validated through continued simulation. This closed-loop approach allows the system to learn from simulated scenarios and improve long-term production planning while adapting to dynamic events, resolving the contradiction between adaptability and complexity.

Inventive Principle:

Principle #23Feedback

2Productivity

If manual data integration is used in current planning systems, then data accuracy can be maintained, but the process is time-consuming and limits optimization to day-by-day basis

Engineering Contradiction:

Improveoptimization speedVSAvoidtime for data integration

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual data integration processes with automated simulation and optimization engines that continuously process production data. This substitution eliminates time-consuming manual operations while maintaining data accuracy through systematic data collection and processing, enabling long-term optimization beyond day-by-day constraints.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The simulation-optimization system operates continuously, constantly integrating data and generating optimized schedules rather than relying on periodic manual updates. This continuous operation eliminates idle time in the planning process and enables real-time adaptation to changing conditions, dramatically improving optimization speed while reducing time loss.

Inventive Principle:

Principle #20Continuity of useful action

3Loss of information

If linear models with static constraints are used, then computational simplicity is maintained, but future visibility feedback and predictive capacity are lost

Engineering Contradiction:

Improvefuture visibility feedbackVSAvoidmodel complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The simulation engine performs preliminary modeling of future production scenarios before actual execution, providing visibility into potential outcomes and bottlenecks. By simulating various scenarios in advance, the system gains predictive capacity and future visibility without requiring excessively complex real-time models, balancing information quality with computational feasibility.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250245682A1Systems and methods for simulation optimization of production networks

Publication Date: 2025.07.31 WALMART APOLLO LLC

AI summary

Systems and methods of production network simulation optimization are disclosed. An optimization request identifying a target production node is received and an optimization engine is implemented to optimize a sequence of operation for one or more sub-nodes of the target production node. The optimization engine utilizes an objective function configured to maximize an adherence percentage. An optimized production data structure including the optimized sequence of operation of the one or more sub-nodes of the target production node is output.