Constraint-Based Carton Packing for Heterogeneous Bin Constraints
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Solution Overview
Problem
Existing bin-packing technologies struggle with complex packing considerations involving time and resource constraints, leading to inefficient outcomes, re-packing mistakes, and high user involvement, which impacts on-time shipments and customer satisfaction.
Innovation Solution
A constraint programming model is employed to manage packing of items into heterogeneous bins, considering multi-dimensional constraints such as weight, volume, compatibility, and ordering rules, using a warehouse management system to minimize container usage and ensure efficient packing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If more factory workers are hired to improve packing efficiency, then productivity increases, but device complexity and operational costs increase
Solution Approach 1:
The packing system performs self-service through automated constraint programming algorithms that independently determine optimal packing configurations without human intervention. The system evaluates item characteristics, container capacities, and constraints to automatically generate packing plans, eliminating the need for additional factory workers while maintaining or improving productivity.
Solution Approach 2:
The patent replaces the mechanical human decision-making process with a computational constraint programming system. Instead of workers manually assessing packing options, the system uses algorithms to evaluate multiple constraints and dimensions simultaneously, substituting human cognitive processes with automated computational logic.
2Adaptability or versatility
If factory workers manually determine packing arrangements, then adaptability to complex constraints is improved, but manufacturing precision and packing accuracy deteriorate
Solution Approach 1:
The system transitions from two-dimensional manual assessment to multi-dimensional computational analysis by evaluating numerous constraints simultaneously (weight limits, volume capacities, item compatibility, ordering rules, expiration dates, geographic restrictions). This dimensional expansion enables the system to handle complex constraints with greater precision than human workers can achieve manually.
Solution Approach 2:
The constraint programming system incorporates feedback mechanisms that continuously evaluate packing configurations against defined constraints. The system adjusts packing arrangements based on constraint satisfaction feedback, ensuring high precision in meeting all specified requirements while maintaining adaptability to complex scenarios.
3Device complexity
If classical bin-packing algorithms are used to simplify the packing process, then device complexity is reduced, but productivity and packing efficiency deteriorate
Solution Approach 1:
The system enhances classical bin-packing algorithms by incorporating additional parameters and dimensions beyond simple volume fitting. The constraint programming model evaluates multiple parameters simultaneously including weight constraints, item compatibility, ordering rules, expiration dates, and geographic restrictions, transforming the algorithm from a simple volume-based approach to a multi-parameter optimization system that improves productivity while managing complexity.
4Use of energy by moving object
If traditional packing methods are used to reduce computational requirements, then use of energy is reduced, but loss of time due to re-packing mistakes increases
Solution Approach 1:
The constraint programming system performs preliminary action by determining the complete optimal packing configuration before physical packing begins. The system evaluates all constraints and item characteristics in advance to generate a finalized packing plan, preventing re-packing mistakes before they occur. This preliminary computational analysis saves time despite requiring initial computational energy.
Data Source
AI summary
A warehouse management system uses a constraint programming model and associated search algorithm for determining packing of items into minimal or otherwise moderated sets of heterogeneous bins (e.g., cartons or other types of containers). In some embodiments, a constraint solver of the warehouse management system minimizes or otherwise moderates the number of shipping containers required to package and ship a group of items. Packaging configurations may be based on multi-dimensional considerations of item properties, bin capacities, such as weight and volume capacities, ordering and grouping rules, and packaging restrictions. Given a set of items, each with a given weight and volume, and a set of containers, each with a specified weight and volume capacity, the goal is to compute carton placement for each item. Container selection may be performed such that the overall number of cartons required for packaging is minimized or otherwise moderated. Optionally, containers chosen for each group of packaged items may be chosen to minimize or otherwise moderate wasted space, while respecting item ordering and grouping.