Forming coordinated teams that balance task coverage and expert workload

2025-03-07

Data mining and knowledge discovery

AI Summary

This paper addresses the multi-skill expert team formation problem: given a set of tasks requiring collaborative, multi-skill execution, the objective is to maximize skill coverage across tasks while minimizing the maximum expert workload, with an extension to incorporate collaboration efficiency. We formulate— for the first time—the joint optimization of skill coverage quality and workload fairness as a multi-objective integer programming problem. We propose a theoretically grounded greedy heuristic algorithm with provable approximation guarantees. Our approach integrates integer linear programming modeling, multi-objective optimization design, computational complexity analysis, and empirical evaluation on real-world datasets. Experimental results demonstrate that the algorithm improves skill coverage by 12.7%, reduces workload variance by 38.4%, and achieves two orders-of-magnitude speedup over baseline methods—effectively balancing task completion completeness and team workload equity.

Technology Category

Humans and AI: Teamwork, Team formationConstraint Satisfaction and Optimization: Distributed CSP/OptimizationSearch and Optimization: Mixed Discrete/Continuous Search

Application Category

Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAIResponsible Web: Human-perceived consequences of algorithmic deployment on the webGraph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Problem

• Form teams to cover diverse skills while minimizing expert workload
• Optimize team coordination using graph-based expert relationships
• Develop scalable algorithms for balanced task and workload assignment

Innovation

• Combines coverage and workload constraints into one objective
• Uses coordination graph for team compatibility
• Provides polynomial-time approximation algorithm with guarantees