3D Load Simulation Optimizing Container Space Utilization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for packing items into containers are inefficient in maximizing space utilization, especially when dealing with items of different sizes, shapes, and types, and do not effectively handle complex constraints such as weight, volume, and business-specific requirements.
Innovation Solution
A three-dimensional load simulation method that determines the optimal number and type of containers, initializes lists for placed and unplaced items, and uses an item iterating process to select and rotate items for efficient placement, considering constraints like weight, volume, and equipment reference units, while allowing for customizable sorting and space evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If traditional packing methods are used, then the packing process is simple, but space utilization is not maximized
Solution Approach 1:
The patent transitions from two-dimensional top-view packing representations to three-dimensional spatial modeling. The system creates a three-dimensional model of the container and items, enabling optimization across length, width, and height dimensions. This dimensional expansion allows the algorithm to evaluate vertical stacking, multi-level placement, and spatial relationships that cannot be captured in traditional two-dimensional layouts, thereby maximizing container space utilization.
Solution Approach 2:
The system performs preliminary actions by creating detailed three-dimensional models of both the container and items before actual packing occurs. It pre-calculates optimal placement configurations, evaluates multiple packing scenarios, and determines the best arrangement in advance. This preliminary modeling and simulation phase allows the system to identify the optimal packing strategy before physical packing begins, avoiding trial-and-error approaches.
2Adaptability or versatility
If simple packing algorithms are used, then the algorithm is easy to implement, but it cannot handle complex constraints like weight, volume, and business requirements
Solution Approach 1:
The patent implements a constraint satisfaction mechanism that dynamically adjusts and evaluates multiple parameters simultaneously. The system considers item dimensions (length, width, height), weight, container capacity, destination requirements, and business rules as adjustable parameters. The optimization algorithm modifies these parameters iteratively to find configurations that satisfy all constraints, enabling the system to handle complex, multi-dimensional constraint scenarios beyond simple geometric fitting.
Solution Approach 2:
The patent segments the packing problem into distinct components: container modeling, item modeling, constraint definition, and optimization evaluation. Each component is handled separately through dedicated data structures and algorithms. The system divides the container into three-dimensional spatial zones and evaluates item placement in each zone independently, then integrates results to achieve global optimization while satisfying all constraints.
3Productivity
If manual packing optimization is attempted, then flexibility for customization is possible, but time consumption increases
Solution Approach 1:
The patent replaces manual, mechanical packing optimization with an automated computer-based optimization system. The system uses software algorithms to perform three-dimensional modeling, constraint evaluation, and optimization calculations that would be extremely time-consuming if done manually. The automated system processes multiple packing scenarios simultaneously and identifies optimal solutions in minutes rather than hours or days of manual effort, dramatically improving productivity while reducing time loss.
Data Source
AI summary
One embodiment is a three dimensional load method for simulating loading of items into at least one container to be transported to at least one destination. The method includes receiving a list of items to be transported, determining at least one container as an optimal number and type of container to be used for transporting the items, and initializing an empty space list to include one space equal to a size of the at least one container. The method also includes initializing a placed item list and an unplaced item list, such that the placed item list includes a list of items already loaded on the at least one container and the unplaced item list includes a list of items to be loaded on the at least one container. The method further includes selecting a subset of items from the unplaced item list for one of the destinations and, while there are more items to be loaded on the at least one container, selecting an item, space in the at least one container, and rotation using an item iterating process, inserting the selected item into the space, the item oriented according to the selected rotation, and updating the empty space list. The method then includes updating the placed item list and the unplaced item list.