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Computer Science

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Heuristics, Integer linear programming, Local Search, Team formation problem



Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


With many companies quickly expanding their sizes, building the best team of experts from the applicants has evolved into an interesting subject for computer-aided decision-making tasks. In this regard, the Team Formation Problem (TFP) has been well-studied in Artificial Intelligence and operations research literature in recent years. We consider a Team Formation Problem of assigning qualified experts to a given set of positions in a given set of projects where each position is to be filled with an expert with a required skill. In our setting, an expert can be quantitatively characterized by one level per skill, and each expert has a limited workload capacity at any moment of the time. Under the condition that all projects need to be completed before they are due, the ultimate goal is to maximize the gain from all the projects in terms of the overall skill levels of each position. We formulated the problem in Integer Linear Programming (ILP) model. We also designed and implemented two improvement-based heuristic approaches, both following the local search strategy. The first one explores the neighbourhood of the current solution considering both the feasible and the infeasible solutions, where the evaluation of the solutions is defined by a linear combination of the objective function and the number of violated constraints. The second one explores the neighbourhood for only feasible solutions. The solutions obtained from these heuristic approaches and the one obtained from ILP solver Gurobi are compared according to their execution times and objective values.