Dynamic Priority Updating for Multi-Round Resource Assignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to optimize resource assignment in complex scheduling scenarios with multiple constraints and preferences, often resulting in unsatisfactory outcomes for users due to conflicts and disparities in resource allocation.
Innovation Solution
A software system employing dynamic priority updating over multiple rounds of resource assignment using a greedy algorithm, where assignees' priorities are adjusted based on previous round results, optimizing overall resource allocation and maximizing user satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional scheduling methods are used to assign resources to users, then the scheduling process is simple to implement, but the satisfaction level of users receives unwanted or unnecessary resources varies widely and overall optimization is poor
Solution Approach 1:
The patent implements dynamic priority updating where user priorities are adjusted based on previous round assignment results. Users who did not receive desired resources have their priorities increased for subsequent rounds, while satisfied users have their priorities decreased. This dynamic adjustment resolves the contradiction by enabling sophisticated optimization without requiring complex manual scheduling rules.
Solution Approach 2:
The system performs preliminary actions by pre-calculating user priorities and resource availability before each assignment round. The greedy algorithm uses pre-sorted user lists based on priorities and pre-identified available resources to make efficient assignments. This preliminary preparation enables high-quality optimization while maintaining computational simplicity.
2Manufacturing precision
If complex optimization algorithms are used to maximize user satisfaction, then resource assignment quality improves, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the resource assignment problem into multiple independent rounds, where each round handles a subset of users and resources. The multi-round greedy algorithm processes users in priority order within each round, making local optimal decisions that collectively achieve global optimization. This segmentation reduces computational complexity compared to solving the entire problem in one step.
Solution Approach 2:
The system uses periodic action by executing assignment rounds in discrete iterations with dynamic priority updates between rounds. After each round, user priorities are recalculated based on satisfaction levels, creating a periodic cycle of assignment-evaluation-reassignment. This periodic approach achieves high-quality optimization through repeated simple iterations rather than one complex calculation.
3Reliability
If resource constraints are strictly enforced to ensure feasibility, then assignment validity is maintained, but user satisfaction levels decrease due to limited resource availability
Solution Approach 1:
The patent dynamically adjusts user priorities based on assignment outcomes and resource availability. When resources are constrained, the system identifies unsatisfied users and increases their priorities for subsequent rounds. This dynamic adaptation maintains strict resource constraints while improving user satisfaction over multiple rounds by reallocating resources to those who need them most.
Solution Approach 2:
The system implements feedback loops where user satisfaction from each assignment round feeds into priority calculations for the next round. Users who did not receive desired resources provide feedback that increases their priority, while satisfied users have reduced priority. This feedback mechanism maintains resource constraints while systematically improving overall user satisfaction across iterations.
Data Source
AI summary
Software techniques are described for optimizing resource assignments among assignees of those resources, by using dynamic priority updating over a plurality of rounds of resource assignment. In particular, in example implementations, a greedy algorithm is used to optimize the resource assignments over multiple rounds based on ranked selections within each selection set of each assignee, including dynamically updating the priority of the assignees at each round, based on assignment results from one or more preceding assignment rounds.