A data-driven goal programming framework for fair and efficient student-advisor assignment in higher education

Purpose This study aims to develop an optimization-based framework that improves fairness and efficiency in assigning academic advisors to students. The purpose is to overcome limitations of manual or ad-hoc allocation by balancing advisor workloads, aligning advisor preferences with student majors and risk levels, and ensuring equitable advising capacity. By introducing a goal-programming formulation validated with real-world university data, the research seeks to enhance academic advising quality, reduce inequities in advisor assignments, and contribute to sustainable and data-driven management practices in higher education. Design/methodology/approach A Mixed-Integer Linear Programming goal-programming model was formulated to minimize deviations in advisor workloads and class distributions while accounting for advisor preferences and student risk categories. The model was implemented in the Advanced Interactive Multidimensional Modeling System (AIMMS) using the CPLEX solver and applied to a real dataset of 2,476 students from a large Middle Eastern engineering college. Results from the optimized assignments were benchmarked against the institution’s baseline manual process by comparing fairness indices, preference alignment, and workload balance to demonstrate the model’s effectiveness and computational efficiency under realistic operational constraints. Findings The proposed model achieved substantial improvements over existing assignment methods. Workload fairness increased by 96.97%, class-load fairness by 98.09%, and preference-alignment penalties were reduced by 45.71%. Major-advisor pairing efficiency improved by 41.94%, resulting in an overall performance enhancement of 91.75%. These results confirm that integrating goal-programming with empirical advising data can yield fairer and more balanced advisor assignments. The findings demonstrate that data-driven optimization supports better resource utilization, improved advisor satisfaction, and a stronger institutional capacity to deliver equitable academic guidance. Originality/value This research is among the first to operationalize goal-programming for optimizing academic advisor assignments using real-world university data. It integrates fairness, workload balancing, and preference alignment within a unified mathematical framework and validates its effectiveness empirically. The study advances the literature by bridging operations-research modeling with higher-education management, offering quantifiable evidence that optimization can improve institutional advising equity. Its methodological and managerial contributions provide a replicable approach for universities seeking data-driven, transparent, and equitable advising systems.