3D Transport Loading Optimization for Axle Balance and Legal Constraints
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Solution Overview
Problem
Current methods for optimizing vehicle transport systems fail to account for the complexity of disparate freight shapes, leading to inefficient loading plans, increased fuel consumption, premature wear, and safety risks, while also being time-consuming and requiring specialized knowledge.
Innovation Solution
A digital method for simulating and optimizing vehicle loading that inputs structural and functional parameters, load dimensions, and legal constraints to calculate an optimized loading plan, using meta-heuristic algorithms and real-time data to distribute loads for improved stability and safety, and automatically controlling actuators for precise positioning and locking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional optimization methods are used for vehicle transport loading, then loading plans can be prepared, but the methods fail to account for the complexity of disparate freight shapes, leading to inefficient loading and increased fuel consumption
Solution Approach 1:
The patent transforms the loading optimization problem by changing parameters from traditional 2D floor-plan based approaches to 3D spatial parameters including length, width, height, and volume utilization. The system considers three-dimensional vehicle dimensions, cargo volume, weight distribution, and spatial arrangement to optimize loading configurations, thereby improving loading efficiency while reducing fuel consumption through better load consolidation.
2Reliability
If paper-based loading plans prepared by experienced designers are used, then loading plans can be created, but the process is time-consuming and requires specialized knowledge
Solution Approach 1:
The patent replaces the manual mechanical process of paper-based loading plan creation with an automated digital computing system. The system uses algorithms to process vehicle and cargo data, generate multiple loading configurations, evaluate them against constraints, and produce optimized loading plans automatically. This substitution eliminates the need for specialized human expertise and dramatically reduces preparation time while maintaining or improving plan quality.
Solution Approach 2:
The loading optimization system performs self-service by automatically generating loading plans without requiring human intervention for the core optimization task. The system takes input data about vehicles and cargo, autonomously processes this information through its algorithms, and produces optimized loading configurations. This self-service capability removes the dependency on experienced designers and reduces time loss.
3Ease of operation
If traditional loading optimization is applied, then basic loading arrangements can be made, but constraints related to fuel consumption and premature wear are not sufficiently taken into account
Solution Approach 1:
The patent implements feedback mechanisms where the optimization system continuously evaluates loading configurations against multiple constraints including fuel consumption metrics and vehicle wear parameters. The system adjusts loading arrangements based on feedback from constraint evaluations, weight distribution analysis, and route information. This iterative feedback process ensures that simplicity of operation does not compromise vehicle reliability, fuel efficiency, or wear considerations.
Solution Approach 2:
The optimization system performs multiple functions simultaneously: it arranges cargo for ease of loading, evaluates fuel consumption impacts, assesses vehicle wear risks, checks constraint compliance, and generates optimized solutions. This multi-functionality ensures that loading simplicity is achieved without sacrificing vehicle reliability, as the system concurrently optimizes for multiple objectives including reduced wear and improved fuel efficiency.
4Productivity
If loading plans are optimized without considering load distribution per axle, then loading can be completed quickly, but the risk of premature wear increases
Solution Approach 1:
The patent changes the optimization parameters to include axle-by-axle weight distribution analysis. The system calculates and evaluates the load on each individual axle based on cargo placement, vehicle configuration, and route characteristics. By incorporating this detailed parameter into the optimization process, the system can quickly determine loading arrangements that protect vehicle service life while maintaining loading speed.
Data Source
AI summary
The invention concerns a method for simulating and optimizing loading of a system for transporting loads in order to determine an optimized loading plan. The method includes: a) inputting the previously defined structural and functional parameters of at least one transport system; b) inputting the number and the dimensional and weight parameters of the loads to be transported; c) inputting the spacing between the loads from previously defined values; d) inputting the route and/or the destination of the load to select the previously defined legal constraints during transport; e) carrying out an optimization calculation in real time, taking into account the parameters and constraints of steps a), b), c) and d) to simulate at least one optimized loading plan consistent with all of the constraints; and f) presenting an optimized loading calculated in e) or approximating an initial loading request.