Dynamic Work Routing System for Stress-Based Resource Assignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for routing and assigning work items to resources in complex business environments often fail to optimize resource utilization, particularly in scenarios with varying skill levels and evolving deadlines, leading to inefficient service delivery.
Innovation Solution
A skills-based routing system that dynamically adjusts the pool of resources based on stress levels, reassigns work items as deadlines change, and considers both required and desired skills, as well as resource utilization, to optimize assignments and manage stress effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a simple system considers one work item at a time and makes fast assignments, then decision speed is improved, but resource utilization deteriorates
Solution Approach 1:
The system dynamically adjusts the scope of optimization from single-item to multi-item based on system state and requirements. The router can switch between considering one work item at a time (fast mode) and multiple work items simultaneously (optimization mode), making the decision process adaptive rather than static.
Solution Approach 2:
The system applies partial optimization by considering multiple work items only when beneficial, rather than always performing full multi-item optimization. This selective approach maintains fast decision-making for simple cases while improving resource utilization when the computational overhead is justified.
2Productivity
If a sophisticated system considers multiple work items at the same time to make better decisions, then resource utilization is improved, but decision speed deteriorates
Solution Approach 1:
The system dynamically determines the scope of work items to consider based on current system state, resource availability, and deadlines. Rather than always considering all work items simultaneously, the router adapts the optimization scope to balance computational complexity with decision quality.
Solution Approach 2:
The system changes key parameters such as the number of work items to consider, the weight of different objectives (service level vs. resource utilization), and the depth of optimization based on system conditions. This allows the decision-making process to adapt to varying priorities and constraints in real-time.
3Loss of energy
If assignments are made taking into account costs and capacities to use cheapest resources, then resource cost is reduced, but service level deteriorates
Solution Approach 1:
The system dynamically adjusts the weight given to cost versus service level objectives based on system state. When resources are abundant and service levels are being met, the system prioritizes cost reduction. When service levels are at risk or resources are constrained, the system shifts priority to maintaining service quality.
Solution Approach 2:
The routing strategy dynamically changes based on real-time conditions. The system can switch between cost-optimized routing and service-level-optimized routing depending on current workload, resource availability, and deadline pressures, making the cost-service level tradeoff adaptive rather than fixed.
4Reliability
If the pool size of resources increases to handle high stress levels, then service level is improved, but resource cost deteriorates
Solution Approach 1:
The system dynamically adjusts the effective pool size by selectively expanding or contracting which resources are considered available based on stress levels. Rather than permanently maintaining a large pool of resources, the system activates additional resources only when stress indicators suggest they are needed to maintain service levels.
Solution Approach 2:
The system changes the parameter of resource pool size dynamically based on system stress metrics. When stress is low, a smaller pool is used to minimize costs. When stress increases (e.g., approaching deadlines, high workload), the pool expands to include more resources, allowing the system to respond to varying demands without permanently over-provisioning.
Data Source
AI summary
Methods and apparatus for service-level based and/or skills-based assignment of a work item to one (or more) of a plurality of resources based on fitness, for example, of skills required by the former to those provided by the latter. Assignment takes into account the level of stress on the work item and/or resources, such that the number of resources fit for assignment varies as the level of stress varies. Systems according to the invention can be used, by way of example, to route a call or other request made by a customer to a service center. The requirements for processing the call (determined, for example, by an incoming call operator) are matched against the skill sets of available customer service agents, taking call and/or resource stress levels into account. For example, some implementations may match an incoming call having a low stress factor (e.g., a newly received call from a standard customer) to a smaller pool of agents with both required and desired skills, while assigning a call with a higher stress factor to a larger pool of agents with at least required skills. Other embodiments may match an incoming call having a low stress factor to the larger pool of agents having at least the required skills, while assigning a call with a higher stress factor (e.g., a call from a priority customer) to an agent from the smaller pool of agents who have both required and desired skills.