Logic Formulation and Evaluation of Academic Constraints

Abstract

University curriculum   scheduling is a prominent research issue of resources optimization.  The problem consists of constraints, composite event variables and their placement domains. This research work introduces a novel set of evaluation heuristics those sharply scan out the dataset with respect to hard or soft constraints and consequently assign penalties to conflicting events.  The research approach is examined over a number of diverse benchmark dataset where complexity increases with subject to their number of events, constraints and curriculums.  Furthermore, each dataset is classified over six complexity scales where each scale is differentiated set of constraints. The prime advantages are revealed from the research work to acquire accurate status of constraints violations with respect to various datasets and complexity scales which leads to obtain optimal solution in short span of time and using less computational resources.