Allocating Dynamic and Finite Resources to a Set of Known Tasks

• João da Silva USP https://orcid.org/0009-0003-0101-8704
• Sarajane Peres USP https://orcid.org/0000-0003-3551-6480
• Daniel Cordeiro USP https://orcid.org/0000-0003-4971-7355
• Valdinei Freire USP https://orcid.org/0000-0003-0330-3931

Resumo

We consider a generalization of the task allocation problem. A finite number of human resources are dynamically available to try to accomplish tasks. For each assigned task, the resource can fail or complete it correctly. Each task must be completed a number of times, and each resource is available for an independent number of tasks. Resources, tasks, and the probability of a correct response are modeled using Item Response Theory. The task parameters are known, while the ability of the resources must be learned through the interaction between resources and tasks. We formalize such a problem and propose an algorithm combining shadow test replanning to plan under uncertain knowledge, aiming to allocate resources optimally to tasks while maximizing the number of completed tasks. In our simulations, we consider three scenarios that depend on knowledge of the ability of the resources to solve the tasks. Results are presented using real data from the Mathematics and its Technologies test of the Brazilian Baccalaureate Examination (ENEM).