A Generalized National Planning Approach for Admission Capacity in Higher Education: A Nonlinear Integer Goal...

Area of Science:

• Operations Research
• Higher Education Management
• Computational Intelligence

Background:

• National higher education enrollment planning faces complex challenges in balancing diverse objectives.
• Existing models may not adequately address the multifaceted nature of admission capacity planning.
• Accurate planning is crucial for meeting national educational and economic goals.

Purpose of the Study:

• To propose a nonlinear integer goal programming model (NIGPM) for comprehensive national admission capacity planning.
• To develop a robust solution methodology for implementing the NIGPM within a defined time horizon.
• To address key enrollment objectives crucial for a nation's higher education system.

Main Methods:

• Development of a nonlinear integer goal programming model (NIGPM).
• Utilizing a novel evolutionary algorithm based on a modified differential evolution (DE) algorithm.
• Application of the model and algorithm to a case study using up-to-date data for Saudi Arabia.

Main Results:

• Demonstrated effectiveness of the NIGPM in solving complex admission capacity planning problems.
• The modified DE algorithm successfully handled the complexity of the NIGPM across various goal priorities.
• Achieved high-quality and robust solutions for higher education admission capacity.

Conclusions:

• The proposed NIGPM provides an effective framework for national higher education admission capacity planning.
• The integrated evolutionary algorithm offers a powerful tool for solving complex optimization problems in this domain.
• The methodology is validated through a practical case study, showing significant potential for real-world application.