In-Memory Resource Assignment with Pruning for Dynamic Dock Allocation
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Solution Overview
Problem
Existing resource allocation systems in warehouses are inefficient, error-prone, and unable to scale with the complexity and real-time demands of modern logistics operations, particularly in dynamic environments where truck arrivals and dock availability fluctuate.
Innovation Solution
A hybrid approach combining permutation pruning, caching, and in-memory processing to reduce the number of permutations evaluated in real-time, using intelligent assignment techniques that leverage permutation pruning to eliminate illegal or suboptimal branches and cache intermediate results for rapid lookup and reuse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If brute-force evaluation of all resource assignment permutations is performed, then complete optimization is achieved, but computational complexity and processing time become prohibitively expensive
Solution Approach 1:
The patent extracts and removes illegal or suboptimal permutations from the evaluation set through pruning techniques. By identifying and eliminating assignments that violate constraints or are guaranteed to be suboptimal, the system reduces the permutation space from factorial complexity to a manageable subset, achieving significant computational savings while maintaining optimization quality.
Solution Approach 2:
The patent performs preliminary evaluation and scoring of permutations before complete execution. By calculating scores based on operational efficiency metrics and pruning low-scoring branches early in the evaluation process, the system avoids exhaustive computation of clearly inferior assignments, thereby reducing computational complexity while preserving the ability to find optimal solutions.
2Speed
If real-time evaluation of resource assignments is performed without caching, then responsiveness to dynamic changes is achieved, but redundant computations increase processing time
Solution Approach 1:
The patent performs preliminary computation and caching of permutation evaluation results. By pre-calculating and storing scores for resource assignments in memory, the system enables rapid retrieval and comparison when dynamic changes occur, avoiding redundant computation while maintaining real-time responsiveness to changing warehouse conditions.
Solution Approach 2:
The patent creates and stores copies of intermediate computation results in cache memory. By saving permutation scores and evaluation outcomes for reuse, the system eliminates the need to recompute identical or overlapping assignments, significantly reducing redundant computation time while enabling fast response to dynamic resource allocation needs.
3Reliability
If complete permutation evaluation is performed without pruning, then all possible assignments are considered, but processing power is wasted on illegal or suboptimal branches
Solution Approach 1:
The patent extracts and removes illegal permutations from consideration through constraint-based pruning. By identifying assignments that violate operational constraints or business rules and eliminating them from the evaluation set, the system prevents waste of processing power on invalid solutions while ensuring that only legally valid assignments are considered, thereby maintaining reliability.
Solution Approach 2:
The patent performs preliminary filtering and scoring of permutations to identify and prune suboptimal branches before complete evaluation. By calculating initial scores based on key operational metrics and eliminating low-scoring assignments, the system avoids wasting processing power on assignments guaranteed to be suboptimal, while maintaining thorough evaluation of promising candidates.
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.