Resource Allocation Caching and Pruning for Real-Time Assignment
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Solution Overview
Problem
Existing resource allocation systems in warehouses, particularly for assigning trucks to dock doors and managing inbound and outbound storage areas, face factorial complexity in permutation evaluation, leading to inefficiencies, redundant computations, and an inability to scale with dynamic operational demands.
Innovation Solution
A hybrid approach combining permutation pruning, caching, and in-memory processing to reduce the number of permutations evaluated in real-time, using pruning to eliminate illegal or suboptimal branches and caching intermediate results for rapid lookup and reuse, while considering operational metrics and future resource availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute-force permutation evaluation is used to assign resources, then all possible assignments can be evaluated, but computational complexity becomes factorial and processing time becomes prohibitively long
Solution Approach 1:
The patent applies preliminary action by performing legality checks and scoring evaluations in a predetermined sequence before complete permutation evaluation. The system checks legal constraints first, then scores permutations based on operational metrics, allowing early elimination of invalid assignments and reducing the need to evaluate all factorial permutations.
Solution Approach 2:
The patent extracts and evaluates key operational metrics (such as dock door availability, storage area capacity, and truck scheduling constraints) separately from complete permutation evaluation. By identifying and removing permutations that fail these extracted criteria, the system avoids evaluating all factorial combinations, significantly reducing computational time while maintaining solution quality.
2Reliability
If complete permutation evaluation is performed to ensure optimal resource allocation, then all assignment possibilities are considered, but redundant computations increase processing overhead
Solution Approach 1:
The patent performs preliminary scoring of permutations based on operational metrics before complete evaluation. By pre-calculating scores for dock door assignments, storage area allocations, and truck scheduling, the system identifies high-quality permutations early, reducing the need to exhaustively evaluate all permutations and thereby conserving processing power while maintaining allocation optimality.
Solution Approach 2:
The patent discards permutations that fail legality checks or score below thresholds during intermediate evaluation stages, avoiding redundant computation of their complete assignments. The system recovers computational resources by eliminating these low-value permutations early in the evaluation process, reducing overall processing overhead while preserving optimal solutions.
3Productivity
If real-time resource allocation is implemented to respond to dynamic operational demands, then responsiveness to changing conditions is improved, but computational complexity increases due to continuous re-evaluation
Solution Approach 1:
The patent segments the resource allocation problem into distinct evaluation stages: legality checking, operational metric scoring, and complete permutation evaluation. This segmentation allows the system to process resource allocation requests in discrete, manageable steps, improving real-time responsiveness by returning partial results from earlier stages while reducing overall computational complexity through staged processing.
Solution Approach 2:
The patent performs preliminary evaluations of resource allocation permutations based on operational metrics before complete real-time re-evaluation is triggered. By pre-assessing dock door availability, storage capacity, and truck scheduling constraints, the system prepares candidate solutions in advance, enabling faster real-time responses to dynamic operational changes without requiring complete re-computation of all permutations.
Data Source
AI summary
Disclosed are techniques for in-memory resource assignment. A method can include: receiving resource data for first resources and operational environment data having current and scheduled occupancy information for second resources, generating a set of permutations of first resource-to-second resource assignments, while generating the set of permutations: determining whether each assignment of each permutation satisfies assignment conditions, based on determining the assignment does not satisfy the conditions, pruning a subtree of permutations stemming from the permutation to remove them from the set, locally caching information about the assignment that does not satisfy the conditions, for each permutation remaining in the set, determining a permutation score, locally caching a permutation remaining in the set having a highest score to reduce storage and lookup time, selecting the locally-cached permutation, and generating and executing electronic instructions to automatically allocate a first resource with a second resource of the selected permutation.