Resource Teaming Slices for Faster Task Assignment Optimization
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Solution Overview
Problem
Combinatorial problems involving resource assignment to tasks face exponential complexity increases as the number of tasks and resources grow, requiring significant computing resources and time, especially when resources are assigned in groups or teams across multiple subtasks.
Innovation Solution
A method and system that divide resources into candidate slices, form combinations to meet resource requirements, and identify the highest teaming metric across tasks, prioritizing whole-task teaming while penalizing resource mismatches and using a threshold to limit exhaustive searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search is used to find optimal resource assignment, then solution optimality is improved, but computational time and resources increase exponentially
Solution Approach 1:
The patent segments the resource assignment problem into discrete tasks and resources, representing assignments as binary variables (0 or 1) indicating whether a resource is assigned to a task. This segmentation transforms the continuous optimization problem into a discrete combinatorial problem that can be systematically explored through structured search methods rather than exhaustive enumeration.
Solution Approach 2:
The patent applies preliminary action by implementing pruning mechanisms that eliminate infeasible or suboptimal assignment combinations before complete evaluation. The system pre-calculates constraints and rules (such as resource availability, task requirements, and assignment policies) to guide the search process, avoiding exploration of obviously poor solutions and reducing the effective search space.
2Productivity
If resources are assigned in coordinated teams across multiple subtasks, then task coordination and efficiency are improved, but assignment complexity increases
Solution Approach 1:
The patent introduces a teaming metric dimension that evaluates resource assignments not just on individual task completion but on coordinated team performance across multiple subtasks. This additional dimensional criterion transforms the optimization landscape, allowing the system to identify assignments that maintain resource teams together across related tasks, thereby improving coordination efficiency while the structured search methodology manages the increased complexity.
Data Source
AI summary
Resource teaming optimization for resource planning is provided by identifying a set of tasks, wherein each task of the set of tasks includes a plurality of subtasks that includes an initial subtask starting at a given state and a final subtask returning to the given state; identifying resource requirements for each subtasks of the plurality of subtasks; dividing a set of resources into a plurality of candidate slices to meet resource requirements for the plurality of subtasks; forming a plurality of combinations from the plurality of candidate slices to satisfy the resource requirements; identifying a given combination from the plurality of combinations having a highest teaming metric across the set of tasks; and assigning the given combination for the set of tasks.