Resource Allocation Rating System for Task Aggregation
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Solution Overview
Problem
Large service organizations face complex challenges in generating capacity and deployment plans due to the combinatorial complexity of numerous task and resource attributes, leading to inefficiencies and increased costs, especially in aligning demand with resource supply effectively.
Innovation Solution
A method involving the formation of resource and task sets with aggregate records, calculating rating values based on clearance scores and productivity, and iteratively optimizing mappings to allocate resources efficiently, considering attributes like location, skills, and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional capacity planning methods are used to allocate resources to tasks, then the organization can identify under-utilized resources and plan capacity, but the complexity of pairing thousands of tasks and resources makes the planning process extremely complex and computationally demanding
Solution Approach 1:
The patent segments the large-scale resource allocation problem into multiple hierarchical levels: capacity planning level (overall resource supply vs. demand), deployment level (which resources can be allocated to which tasks), and schedule level (specific resource to specific task assignments). This segmentation divides the complex combinatorial problem into manageable sub-problems that can be solved more efficiently at each level.
Solution Approach 2:
The patent transforms the resource allocation problem by changing parameters from individual task-resource pairings to aggregate metrics. It uses workstack aggregation (grouping tasks by type, location, skill requirements), resource aggregation (grouping resources by category, availability, skills), and introduces rating values that combine multiple attributes into single comparable metrics, thereby simplifying the decision space.
2Productivity
If detailed deployment plans are generated in advance, then resource allocation can be optimized, but the plans do not account for factors such as sickness or downtime of resources that occur hour-by-hour when tasks are performed
Solution Approach 1:
The patent generates deployment plans in advance that identify which resources can be allocated to which tasks based on aggregated capabilities and requirements. This preliminary planning optimizes resource utilization by matching resource pools to task stacks before knowing specific individual resource availability, thereby achieving optimization while maintaining flexibility for real-time adjustments.
Solution Approach 2:
The patent creates a dynamic planning system where the deployment plan is generated based on current resource availability data but is designed to be adaptable to hour-by-hour changes. The aggregation approach allows the system to maintain optimized allocations while accommodating individual resource absences, as the plan works with resource pools rather than fixed individual assignments.
3Reliability
If the organization tries to satisfy all customer demand with correctly equipped resources, then quality of service improves, but costs increase due to needing additional technicians and resources
Solution Approach 1:
The patent merges multiple tasks with similar requirements into workstack aggregates and multiple resources with similar capabilities into resource aggregates. This consolidation allows the organization to plan at aggregate levels, identifying under-utilized resources that can be reallocated to meet additional demand, thereby improving service quality without proportionally increasing resource numbers.
Solution Approach 2:
The patent changes the planning parameters from individual task-resource assignments to aggregate metrics including workstacks (grouped tasks), resource pools (grouped resources), and rating values (composite metrics). This parameter transformation enables the organization to optimize resource utilization across the portfolio, satisfying more demand with existing resources rather than requiring additional resources for each individual task.
Data Source
AI summary
A method for allocating resources of a group of resources to tasks of a group of tasks, the method comprising: forming a resource set comprising a resource record for each of the resources of the group of resources, each resource record having one or more fields representative of attributes of the respective resource; forming a task set comprising a task record for each of the tasks of the group of resources, each task record having one or more fields, each field holding data representative of an attribute of the respective task; condensing the task set by one or more iterations of the steps of: i. selecting two or more tasks of the task set; ii. adding to the task set an aggregate task record having one or more fields, each such field corresponding to a field of each of the task records of the selected tasks and holding data representative of the collective attributes of the task records of the selected tasks in those fields; and iii. removing the task records of the selected tasks from the task set; forming at least two mappings of some or all of the members of the resource set on to one or more members of the task set; calculating, for each of the mappings, a rating value dependent on the allocation of resources to the or each aggregate task in that mapping; comparing the rating values of the mappings; and selecting one of the mappings for further processing in dependence on the result of the said comparing step.