Evaluation of Heuristic Approaches for Resource Allocation Scheduling with Conflicting Constraints in University Environments

This scientific paper presents an innovative software-based approach for optimizing the process of enrolling undergraduate students in any educational institution. Generally, each course comes with a set of requirements necessary for its proper operation (teacher availability, student time availability, classrooms with or without computers per student, teacher computer, video projector, student capacity, smart screens, TV, specialized laboratories, specialized equipment, etc.). These resources can be managed by different administrative units within the institution (physical space units, laboratory units, academic units, etc.). This is classified as an NP problem. The allocation process is usually carried out manually and by mutual agreement between the different administrative units. Furthermore, this can lead to a number of constraints that can cause conflicts. This paper examines two problem-solving methods that can automatically and efficiently allocate the resources required by each course in each academic unit. The aim is to integrate the most efficient method into the software for the allocation of resources for the courses. The methods evaluated were Tabu search (TS) and an application of Genetic Algorithm (GA). The research evaluates the performance and memory usage of the two methods. The measurements were conducted based on the number of constraints, where the TS algorithm demonstrated better performance in terms of time and computational resource usage. The findings of this research contribute to the field of educational scheduling and can serve as a basis for further improvements in academic resource management.