TAIP: an anytime algorithm for allocating student teams to internship programs
This addresses the practical challenge of efficiently allocating student teams to internship programs in educational settings, representing an incremental improvement over existing optimization methods.
The paper tackles the problem of matching student teams to internship programs by formalizing the Team Allocation for Internship Programs Problem and proposing TAIP, a heuristic algorithm that generates and iteratively improves allocations. The evaluation shows TAIP reaches optimality and outperforms CPLEX in time, with specific performance gains in computational efficiency.
In scenarios that require teamwork, we usually have at hand a variety of specific tasks, for which we need to form a team in order to carry out each one. Here we target the problem of matching teams with tasks within the context of education, and specifically in the context of forming teams of students and allocating them to internship programs. First we provide a formalization of the Team Allocation for Internship Programs Problem, and show the computational hardness of solving it optimally. Thereafter, we propose TAIP, a heuristic algorithm that generates an initial team allocation which later on attempts to improve in an iterative process. Moreover, we conduct a systematic evaluation to show that TAIP reaches optimality, and outperforms CPLEX in terms of time.