Adapting Behavioral Pairing Models to Runtime Conditions
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Solution Overview
Problem
Task assignment systems face challenges in adapting to changing goals and runtime conditions, such as agent availability, task queue sizes, and system failures, which can lead to suboptimal task allocation and performance.
Innovation Solution
A method and system that dynamically adapt behavioral pairing models based on monitored parameters, such as agent surplus, task queue size, and system conditions, allowing for real-time switching between different task assignment strategies to optimize goals like revenue, handle time, and customer satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single behavioral pairing model is used for task assignment, then the system structure is simple and easy to implement, but the system cannot adapt to changing runtime conditions and goals
Solution Approach 1:
The system implements dynamic adaptability by switching between multiple behavioral pairing models based on runtime conditions. The task assignment system monitors parameters such as agent availability, task queue sizes, and system failures, and dynamically selects the appropriate pairing model (e.g., diagonal pairing, payout matrix pairing) to optimize performance under varying conditions, thereby resolving the contradiction between adaptability and system complexity.
2Adaptability or versatility
If multiple pairing models are maintained for different conditions, then the system can adapt to changing goals, but the complexity of selecting and switching between models increases
Solution Approach 1:
The system employs feedback mechanisms by continuously monitoring runtime parameters (agent surplus, task queue size, system failures) and using this information to automatically select the appropriate pairing model. This feedback-driven approach enables the system to adapt to changing goals without requiring manual intervention, thus maintaining ease of operation while achieving high adaptability.
3Productivity
If task assignment follows a static strategy like FIFO, then the assignment process is simple and predictable, but the system cannot optimize for changing performance goals such as revenue or handle time
Solution Approach 1:
The system optimizes task assignment efficiency by changing the behavioral pairing parameters based on current system conditions and goals. Instead of using a fixed FIFO strategy, the system adjusts the pairing model parameters (e.g., switching between diagonal pairing for revenue optimization and payout matrix pairing for handle time reduction) to achieve the desired performance metrics, thereby resolving the contradiction between productivity and adaptability.
Data Source
AI summary
Techniques for adapting behavioral pairing to runtime conditions in a task assignment system are disclosed. In one particular embodiment, the techniques may be realized as a method for adapting behavioral pairing to runtime conditions in a task assignment system comprising: determining, by at least one computer processor communicatively coupled to and configured to operate in the task assignment system, at least two pairing models for assigning tasks in the task assignment system; monitoring, by the at least one computer processor, at least one parameter of the task assignment system; and selecting, by the at least one computer processor, one of the at least two pairing models based on a value of the at least one parameter.