Forming coordinated teams that balance task coverage and expert workload
🤖 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.
Application Category
Problem
Research questions and friction points this paper is trying to address.
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
Methods, ideas, or system contributions that make the work stand out.
Combines coverage and workload constraints into one objective
Uses coordination graph for team compatibility
Provides polynomial-time approximation algorithm with guarantees
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